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<title>AritmoLab — Role of AI in the Analysis of Unstructured Clinical Databases</title>
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<section class="arit title-slide">
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<img src="logo.png" alt="Policlinico San Donato">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
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<span class="band-section">AritmoLab · Clinical Data Platform</span>
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<div class="sbody">
<h1>Role of AI in the Analysis of Unstructured Clinical Databases</h1>
<div class="hgap"></div>
<p class="lead">How we turned a mix of structured and free-text clinical records into a research data platform — the AritmoLab platform experience.</p>
<div class="title-meta">
<span class="pill">AritmoLab</span>
<span class="what">A clinical data platform hosting cardiology data and analysis tools</span>
</div>
<div class="speakers">
<span>Dr. Marco Pancotti - MultiPhysixLab</span>
<span>Dr. Sara Paratico - I.R.C.C.S. Policlinico San Donato</span>
</div>
<p class="event">“Multidimensional Characterization of Cardiac Arrhythmias: Role of Electrocardiology in the Artificial Intelligence Era”</p>
<p class="event-where">San Donato Milanese, Milan, Italy · 2–3 October 2026</p>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">01 / 11</span></footer>
<aside class="notes">
Good morning. My name is Marco Pancotti, and I led the part of the PAMP-FA project dedicated to building the AritmoLab portal at Policlinico San Donato, with support from Sara Paratico, who will co-present with me today.
The scope of the project included a data warehouse and tools for machine learning and predictive statistics to support research by the Arrhythmology Unit, directed by Professor Pappone and Professor Locati.
Today we'll focus on the use of AI to extract structured information from clinical text, followed by a brief tour of the portal and ThothII, a tool for building datamarts from natural-language requests.
</aside>
</section>
<section class="arit flow-slide">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
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<nav class="band">
<span class="band-section">The project</span>
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<div class="sbody">
<div class="kicker">The plan</div>
<h2>What we wanted to build</h2>
<p class="lead">One platform, two destinations: the 360° patient portal and a research-ready warehouse. Built on open-source pillars, with AI coding agents wherever they helped. Next, we’ll look at the hardest part: unstructured data.</p>
<div class="flow">
<button type="button" class="zone existed lens-target" id="plan-existed" data-lens="plan-existed" data-lens-title="What existed" data-lens-number="1" aria-label="Explore What existed" aria-expanded="false" aria-controls="slide-lens">
<span class="zlabel">What existed</span>
<span class="fcol">
<span class="fbox src"><b>Cardioref</b><span>cardiology records · procedures · letters</span></span>
<span class="fbox src"><b>Genetic data</b><span>labs · variants · nomenclature</span></span>
<span class="fbox src"><b>ECG</b><span>signals · device follow-up</span></span>
<span class="fbox src future"><b><span class="plus">+</span>Future sources</b><span>new subsystems can be connected as sources</span></span>
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<p class="detail-intro">Four disconnected systems held complementary views of the patient.</p>
<p><b>Clinical course · Cardioref</b>Twenty years of visits, procedures and reports supported daily clinical work. Much of the information needed for research remained in free text.</p>
<p><b>Genetic findings</b>Laboratory results and variants were entered manually into Excel, without integration with Cardioref.</p>
<p><b>Electrical activity · ECG</b>Separate device systems provided paper records or CSV exports on request, requiring manual collection.</p>
<p><b>The early portal</b>The AritmoLab prototype, originally the Omics Portal, lacked integration with the sources. Connecting clinical history, genetics and ECG was the prerequisite for a shared patient view.</p>
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<div class="fcol ai-hit">
<button class="brain brain-btn" data-ai="ingestion" aria-label="AI contribution: the mappings" aria-pressed="false"><span class="ai-label" aria-hidden="true">AI</span><img class="artificial-brain" src="artificial-brain.svg" alt=""><span class="ai-number">6</span></button>
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<div class="zone built">
<span class="zlabel">What we built</span>
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<div class="fcol" style="flex:0.8; justify-content:center"><button type="button" class="fbox lens-target" id="plan-staging" data-lens="plan-staging" data-lens-number="2" aria-label="Explore Staging" aria-expanded="false" aria-controls="slide-lens"><b>Staging</b><span>raw replica of the sources</span>
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<p class="detail-intro">The landing area: a faithful working copy of the data brought in from each source system.</p>
<p><b>What it contains</b>Original tables, identifiers, dates and clinical text, still in the source’s format.</p>
<p><b>Why it matters</b>Subsequent processing works on this copy. The original clinical system continues its daily work, and transformations can be checked against the imported data.</p>
<p class="detail-example"><b>Clinical example</b>A Cardioref letter arrives with its original wording. “No syncope” is still text; this layer does not yet turn it into a clinical variable.</p>
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<div class="farrow">→</div>
<div class="fcol ai" style="flex:1.25">
<button class="brain brain-btn" data-ai="integration" aria-label="AI contribution: clinical text reading" aria-pressed="false"><span class="ai-label" aria-hidden="true">AI</span><img class="artificial-brain" src="artificial-brain.svg" alt=""><span class="ai-number">7</span></button>
<button type="button" class="fbox lens-target" id="plan-integration" data-lens="plan-integration" data-lens-number="3" aria-label="Explore Integration" aria-expanded="false" aria-controls="slide-lens"><b>Integration</b><span>cleaned, normalized, deduplicated</span>
<template class="lens-details">
<p class="detail-intro">The reconciliation layer: source records become consistent, connected clinical information.</p>
<p><b>What happens here</b>Formats and terminology are standardized, duplicates reconciled, and records linked through patient and event identifiers.</p>
<p><b>Where AI contributes</b>It extracts conditions, procedures and drug-challenge outcomes from clinical text. Negation and context matter; extraction quality needs validation.</p>
<p class="detail-example"><b>Clinical example</b>“No syncope” is not a positive finding. A family history of Brugada must remain distinct from the patient’s own diagnosis.</p>
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<div class="fcap">AI reads the clinical text: pathologies, procedures, drug-challenge outcomes</div>
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<div class="fcol" style="flex:1.05; justify-content:center"><button type="button" class="fbox lens-target" id="plan-star-schema" data-lens="plan-star-schema" data-lens-number="4" aria-label="Explore Data warehouse" aria-expanded="false" aria-controls="slide-lens"><b>Data warehouse</b><span>star schema · organized for analysis</span>
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<p class="detail-intro">A shared analytical database replaces separate exports and fragile spreadsheets with one source of truth.</p>
<p><b>How a star schema works</b>“Facts” represent events or measurements, such as a procedure or test. “Dimensions” describe their context, such as the patient, date and procedure type.</p>
<p><b>Clinical Intelligence</b>Common definitions make the Unit’s activity and patients’ histories comparable over time. Dashboards can show volumes, incidence and outcomes for management, audit and research.</p>
<p class="detail-example"><b>Clinical question it can support</b>How many patients underwent a given procedure each year, and how does that distribution vary by age group?</p>
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<button class="brain brain-btn" data-ai="datamarts" aria-label="AI contribution: datamart generation" aria-pressed="false"><span class="ai-label" aria-hidden="true">AI</span><img class="artificial-brain" src="artificial-brain.svg" alt=""><span class="ai-number">8</span></button>
<button type="button" class="fbox lens-target" id="plan-datamarts" data-lens="plan-datamarts" data-lens-number="5" aria-label="Explore Datamarts" aria-expanded="false" aria-controls="slide-lens"><b>Datamarts</b><span>research-ready marts</span>
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<p class="detail-intro">Reproducible cohorts provide the structured data previously missing for statistics and model training.</p>
<p><b>What they define</b>The cohort, time window, variables and level of detail, such as one row per patient with features and outcomes.</p>
<p><b>How ThothII helps</b>A plain-English question becomes proposed SQL through a guided workflow with human review, reducing manual dataset preparation.</p>
<p><b>The professional portal</b>AritmoLab brings integrated patient information and research dashboards into one place, extending the early Omics Portal.</p>
<p class="detail-example"><b>Illustrative study dataset</b>Drug-challenge patients, test dates, results and clinical characteristics, with agreed cohort and variable definitions.</p>
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<div class="fcap">AI builds them on demand from plain-English questions (ThothII)</div>
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<div class="farrow">→</div>
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<div class="fbox end"><b>AritmoLab Data Warehouse</b><span>queried for research</span></div>
<div class="fbox end"><b>AritmoLab Portal</b><span>management &amp; exploration</span></div>
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<div class="pillars">
<span class="agents">AI coding agents — Anthropic · OpenAI — used wherever they helped, 360° in the code</span>
<span class="pillar"><b>Airflow</b> · orchestration</span>
<span class="pillar"><b>Superset</b> · dashboards</span>
<span class="pillar"><b>Django</b> · portal scaffolding</span>
<span class="pillar"><b>Authentik</b> · auth — GSD LDAP</span>
</div>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">02 / 11</span></footer>
<aside class="notes">
<p>We had four goals: clinical dashboards to understand the Unit’s activity and patients’ histories, a shared data warehouse, structured cohorts for machine learning and predictive statistics, and a unified professional portal.</p>
<p>(1) We started with disconnected systems: twenty years of Cardioref records, much of their research value in free text, genetic results entered manually into Excel, ECGs on paper or exported on request, and an early portal without source integration. Each source answered a different need, but combining them for a clinical study meant collecting exports and rebuilding patient histories by hand.</p>
<p>(2) To connect them, staging preserves a working copy of the sources.</p>
<p>(3) Integration then cleans, normalizes and links the records.</p>
<p>(4) The data warehouse organizes events and their context for analysis.</p>
<p>(5) From there, datamarts turn that shared information into research datasets and dashboards within AritmoLab.</p>
<p>(6) The brain symbols show where AI contributes. It drafts the mappings, which humans review.</p>
<p>(7) During integration, AI extracts structured information from clinical text.</p>
<p>(8) ThothII then builds datamarts from plain-English questions, with human approval.</p>
<p>Open-source tools support the platform, and AI coding agents helped throughout development. The popups connect each component to the gap it addresses.</p>
<p>Next, we turn to the hardest part: unstructured data. Sara will explain how we extract clinical meaning and check its reliability.</p>
</aside>
</section>
<section class="arit center-v problem">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
</header>
<nav class="band">
<span class="band-section">The problem</span>
</nav>
<div class="sbody">
<div class="kicker">From free text to a research platform</div>
<h2>The clinical truth lives in unstructured columns</h2>
<div class="quote">
<div class="tr-grid">
<p class="it">“Il paziente riferisce sincope ricorrente; ECG basale con sopraslivellamento ST in V1–V3; test provocativo con flecainide positivo per pattern Brugada.”</p>
<p class="en"><span class="en-tag">EN</span>“The patient reports recurrent syncope; baseline ECG with ST-segment elevation in V1–V3; flecainide provocation test positive for a Brugada pattern.”</p>
</div>
</div>
<div class="pipeline" id="pipeline">
<div class="stage"><b>Sources</b><span>Cardioref · Genetic data<br>Omics Portal · ECG</span><span class="brain-ai"><svg viewBox="0 0 32 32" fill="none" stroke="currentColor" stroke-width="2.4" stroke-linecap="round" stroke-linejoin="round"><path d="M16 4 C11 2 5.5 4 6 8.5 C2.5 10 2 14.5 4.5 17 C2.5 20 4 24.5 8 25 C9 28 14 29.5 16 27"/><path d="M16 4 C21 2 26.5 4 26 8.5 C29.5 10 30 14.5 27.5 17 C29.5 20 28 24.5 24 25 C23 28 18 29.5 16 27"/><path d="M16 4.5 V26.5"/><path d="M16 10 h4.5"/><circle cx="23" cy="10" r="1.7" fill="currentColor" stroke="none"/><path d="M16 15.5 h-4.5"/><circle cx="8.5" cy="15.5" r="1.7" fill="currentColor" stroke="none"/><path d="M16 21 h4.5"/><circle cx="23" cy="21" r="1.7" fill="currentColor" stroke="none"/></svg></span></div>
<div class="arrow">→</div>
<div class="stage"><b>Staging</b><span>raw replica</span><span class="brain-ai"><svg viewBox="0 0 32 32" fill="none" stroke="currentColor" stroke-width="2.4" stroke-linecap="round" stroke-linejoin="round"><path d="M16 4 C11 2 5.5 4 6 8.5 C2.5 10 2 14.5 4.5 17 C2.5 20 4 24.5 8 25 C9 28 14 29.5 16 27"/><path d="M16 4 C21 2 26.5 4 26 8.5 C29.5 10 30 14.5 27.5 17 C29.5 20 28 24.5 24 25 C23 28 18 29.5 16 27"/><path d="M16 4.5 V26.5"/><path d="M16 10 h4.5"/><circle cx="23" cy="10" r="1.7" fill="currentColor" stroke="none"/><path d="M16 15.5 h-4.5"/><circle cx="8.5" cy="15.5" r="1.7" fill="currentColor" stroke="none"/><path d="M16 21 h4.5"/><circle cx="23" cy="21" r="1.7" fill="currentColor" stroke="none"/></svg></span></div>
<div class="arrow">→</div>
<div class="stage ai"><b>Integration</b><span>3NF · text mining</span><span class="brain-ai"><svg viewBox="0 0 32 32" fill="none" stroke="currentColor" stroke-width="2.4" stroke-linecap="round" stroke-linejoin="round"><path d="M16 4 C11 2 5.5 4 6 8.5 C2.5 10 2 14.5 4.5 17 C2.5 20 4 24.5 8 25 C9 28 14 29.5 16 27"/><path d="M16 4 C21 2 26.5 4 26 8.5 C29.5 10 30 14.5 27.5 17 C29.5 20 28 24.5 24 25 C23 28 18 29.5 16 27"/><path d="M16 4.5 V26.5"/><path d="M16 10 h4.5"/><circle cx="23" cy="10" r="1.7" fill="currentColor" stroke="none"/><path d="M16 15.5 h-4.5"/><circle cx="8.5" cy="15.5" r="1.7" fill="currentColor" stroke="none"/><path d="M16 21 h4.5"/><circle cx="23" cy="21" r="1.7" fill="currentColor" stroke="none"/></svg></span></div>
<div class="arrow">→</div>
<div class="stage"><b>Data Warehouse</b><span>star schema</span></div>
<div class="arrow">→</div>
<div class="stage hot" id="marts"><b>Marts</b><span>dbt · Superset</span></div>
</div>
<div class="midrow">
<div class="concepts">
<div class="lbl">four challenges to face · the answers come next</div>
<div class="citem"><span class="n">01</span><div><b>Volume without structure</b><span class="d">twenty years of records locked in free-text columns</span></div></div>
<div class="citem"><span class="n">02</span><div><b>Ambiguous language</b><span class="d">negation · family history · bilingual shorthand</span></div></div>
<div class="citem"><span class="n">03</span><div><b>No shared ontology</b><span class="d">every system names conditions its own way</span></div></div>
<div class="citem"><span class="n">04</span><div><b>Proving reliability</b><span class="d">extraction must be demonstrably correct</span></div></div>
</div>
<div class="railwrap" id="railwrap">
<div class="conn-drop" id="conn-drop"></div>
<div class="conn-elbow" id="conn-elbow"></div>
<div class="rail" id="rail">
<div class="lbl">outputs of the datamarts</div>
<div class="row"><svg class="oicon" viewBox="0 0 34 34" fill="none" stroke="currentColor" stroke-width="2.2" stroke-linecap="round"><path d="M4 29 h26"/><rect x="7" y="17" width="5" height="12" rx="1"/><rect x="15" y="9" width="5" height="20" rx="1"/><rect x="23" y="13" width="5" height="16" rx="1"/></svg><b>Dashboards</b><span class="d">clinical review on Superset</span></div>
<div class="row"><svg class="oicon" viewBox="0 0 34 34" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round"><circle cx="7" cy="7" r="2.6"/><circle cx="7" cy="27" r="2.6"/><circle cx="17" cy="17" r="3.4"/><circle cx="27" cy="7" r="2.6"/><circle cx="27" cy="27" r="2.6"/><path d="M9.3 8.8 L14.6 14.6"/><path d="M9.3 25.2 L14.6 19.4"/><path d="M19.4 14.6 L24.7 8.8"/><path d="M19.4 19.4 L24.7 25.2"/></svg><b>Machine Learning</b><span class="d">cohort tables for model training</span></div>
<div class="row"><svg class="oicon" viewBox="0 0 34 34" fill="none" stroke="currentColor" stroke-width="2.2" stroke-linecap="round"><path d="M3 27 L11 19 L17 22 L23 13"/><path d="M23 13 L30 6" stroke-dasharray="3 3.4"/><circle cx="30" cy="6" r="2.2" fill="currentColor" stroke="none"/><path d="M3 31 h28" opacity=".35"/></svg><b>Predictive Statistics</b><span class="d">outcomes &amp; risk analysis</span></div>
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</div>
</div>
<div class="thoth-wrap" id="thothwrap">
<div class="t-conn" id="t-conn"></div>
<div class="t-head" id="t-head"></div>
<div class="thoth-col">
<div class="thoth-box"><span class="brain-ai"><svg viewBox="0 0 32 32" fill="none" stroke="currentColor" stroke-width="2.4" stroke-linecap="round" stroke-linejoin="round"><path d="M16 4 C11 2 5.5 4 6 8.5 C2.5 10 2 14.5 4.5 17 C2.5 20 4 24.5 8 25 C9 28 14 29.5 16 27"/><path d="M16 4 C21 2 26.5 4 26 8.5 C29.5 10 30 14.5 27.5 17 C29.5 20 28 24.5 24 25 C23 28 18 29.5 16 27"/><path d="M16 4.5 V26.5"/><path d="M16 10 h4.5"/><circle cx="23" cy="10" r="1.7" fill="currentColor" stroke="none"/><path d="M16 15.5 h-4.5"/><circle cx="8.5" cy="15.5" r="1.7" fill="currentColor" stroke="none"/><path d="M16 21 h4.5"/><circle cx="23" cy="21" r="1.7" fill="currentColor" stroke="none"/></svg></span>ThothII — natural-language → SQL over the DWH, builds the final datamarts</div>
</div>
</div>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">03 / 11</span></footer>
<aside class="notes">
<p>Over twenty years, we have collected clinical information in discharge letters and procedure reports. Much remains in free text.</p>
<p>Clinicians connect these details and form hypotheses. Here, one sentence combines syncope, ECG changes and a positive flecainide test. Research needs defined variables that preserve this meaning.</p>
<p>Our text miner extracts them in the integration layer, before the data enter the warehouse.</p>
<p>We face four challenges: capturing diverse clinical histories, interpreting context correctly, mapping different expressions to common terms, and validating accuracy. Reliable research and patient care depend on getting these details right.</p>
</aside>
</section>
<section class="arit center-v stats">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
</header>
<nav class="band">
<span class="band-section">Text analysis</span>
</nav>
<div class="sbody">
<div class="kicker">What the text mining produced</div>
<h2>Deterministic AI, audited numbers</h2>
<div class="strip">
<div class="sstat"><span class="n">58,438</span><span class="l">letters read</span></div>
<div class="sstat"><span class="n">73,389</span><span class="l">pathologies extracted</span></div>
<div class="sstat"><span class="n">10,908</span><span class="l">tests parsed</span></div>
<div class="sstat"><span class="n">2,307</span><span class="l">Brugada patients</span></div>
<div class="sstat"><span class="n">≈ 240</span><span class="l">letters / month</span></div>
</div>
<div class="cycle" id="cycle">
<svg class="cycle-svg" viewBox="0 0 1184 270">
<defs><marker id="arrw" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="6.5" markerHeight="6.5" orient="auto-start-reverse"><path d="M0 0L10 5L0 10z" fill="#cb333b"/></marker></defs>
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<path marker-end="url(#arrw)"/>
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<path marker-end="url(#arrw)"/>
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</svg>
<div class="vcomp" style="left:592px; top:50px"><svg class="cicon" viewBox="0 0 34 34" fill="none" stroke="currentColor" stroke-width="2.2" stroke-linecap="round" stroke-linejoin="round"><rect x="7" y="4" width="20" height="26" rx="2.5"/><path d="M12 11h10"/><path d="M12 16h10"/><path d="M12 21h6"/></svg><b>Letter reader</b><span class="d">every discharge letter, Italian &amp; English</span></div>
<div class="vcomp" style="left:992px; top:135px"><svg class="cicon" viewBox="0 0 34 34" fill="none" stroke="currentColor" stroke-width="2.2" stroke-linecap="round" stroke-linejoin="round"><circle cx="14.5" cy="14.5" r="8"/><path d="M20.5 20.5 L29 29"/><path d="M11 14.5 h7"/><path d="M14.5 11 v7"/></svg><b>Clinical matcher</b><span class="d">recognises diagnoses, procedures, test outcomes</span></div>
<div class="vcomp" style="left:592px; top:220px"><svg class="cicon" viewBox="0 0 34 34" fill="none" stroke="currentColor" stroke-width="2.2" stroke-linecap="round" stroke-linejoin="round"><path d="M17 3.5 L28 8 v8.5 c0 7-4.7 11.6-11 14 C10.7 28.1 6 23.5 6 16.5 V8 Z"/><path d="M12.5 16.5 l3.2 3.2 L23.5 12"/></svg><b>Context guard</b><span class="d">negations &amp; family history kept apart</span></div>
<div class="vcomp" style="left:192px; top:135px"><svg class="cicon" viewBox="0 0 34 34" fill="none" stroke="currentColor" stroke-width="2.2" stroke-linecap="round" stroke-linejoin="round"><circle cx="17" cy="7" r="3"/><circle cx="7" cy="26.5" r="3"/><circle cx="27" cy="26.5" r="3"/><path d="M17 10 v7"/><path d="M17 17 L8 23.5"/><path d="M17 17 L26 23.5"/></svg><b>Ontology sorter</b><span class="d">every finding into its clinical category</span></div>
</div>
<div class="note-row">
<div class="nitem"><b>Traceable.</b> Every finding carries the version of the rules that produced it (<code>pattern_version</code>): any number can be rebuilt years later.</div>
<div class="nitem"><b>Audited.</b> A new rule set goes live only after it proves ≥95% accuracy on 100 records reviewed by hand.</div>
</div>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">04 / 11</span></footer>
<aside class="notes">
<p>Our text miner uses explicit clinical rules to identify diagnoses and procedures, including catheter ablation and drug challenge tests, while checking their context.</p>
<p>These figures cover over 58,000 letters and around 73,000 records of clinical conditions. We identified almost 11,000 drug challenge test entries, including positive Brugada tests in 2,307 patients.</p>
<p>Each extracted record retains the rule version used. This lets us compare classifications with clinical review and investigate errors. For procedure classification, our target is at least 95 percent accuracy against 100 manually reviewed records.</p>
</aside>
</section>
<section class="arit nlp">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
</header>
<nav class="band">
<span class="band-section">Text analysis</span>
</nav>
<div class="sbody">
<div class="kicker">Clinical NLP</div>
<h2>Reading clinical text is not keyword matching</h2>
<div class="hgap"></div>
<div class="split">
<div class="left">
<ul class="points">
<li><b>Negation</b> — “fibrillazione atriale <i>esclusa</i>”, “<i>non</i> FA” <span class="g">(“AF ruled out”, “no AF”)</span>: the rule scans a window around every match before deciding</li>
<li><b>Family ≠ patient</b> — “padre con FA” <span class="g">(“father with AF”)</span> is attributed to the family member, not to the patient</li>
<li><b>Two languages</b> — every pattern matches IT and EN forms: fibrillazione atriale / atrial fibrillation</li>
<li><b>Abbreviations &amp; noise</b> — FA, f.a., “TA 140/90” (blood pressure, not a diagnosis)</li>
<li><b>One field, many statements</b> — the text is split into clauses before matching</li>
</ul>
</div>
<div class="right">
<div class="code">
<div class="code-head"><span>what the rules see</span><span>examples from the corpus</span></div>
<pre>“<i>padre con</i> fibrillazione atriale”
<span class="en">“father with atrial fibrillation”</span>
→ patologia: FA · attribuzione: <b>familiare</b>
<span class="en">pathology: AF · attribution: family</span>
“fibrillazione atriale <i>esclusa</i>”
<span class="en">“atrial fibrillation ruled out”</span>
→ patologia: FA · <b>negato</b>
<span class="en">pathology: AF · negated</span>
“test provocativo con flecainide
positivo per pattern Brugada”
<span class="en">“flecainide provocation test, positive for Brugada pattern”</span>
→ test: flecainide · esito: <b>POSITIVO</b>
<span class="en">test: flecainide · outcome: POSITIVE</span></pre>
</div>
</div>
</div>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">05 / 11</span></footer>
<aside class="notes">
<p>A report may describe atrial fibrillation in the patient, rule it out, or mention it in the father's history. The same term must lead to different classifications.</p>
<p>Our analyser checks for negation and family references, recording these attributes separately. It recognises Italian and English terms and common abbreviations. For example, "TA" may mean atrial tachycardia, but followed by a blood pressure value, it should not trigger that diagnosis.</p>
<p>When reports describe several events, the system separates the text into clauses. For drug challenge tests, this helps distinguish a positive result before ablation from a negative result afterwards, preserving each observation and its clinical context.</p>
</aside>
</section>
<section class="arit center-v">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
</header>
<nav class="band">
<span class="band-section">Text analysis</span>
</nav>
<div class="sbody">
<div class="kicker">The clinical ontology</div>
<h2>Two tiers, one clinical order</h2>
<div class="onto-split">
<div class="onto-left">
<div class="tier-h">TIER 1 · arrhythmic<span class="cnt">11</span></div>
<div class="cats">
<span class="cat fill"><b>AF</b><span class="it">FA</span></span><span class="cat fill"><b>Brugada</b></span><span class="cat fill"><b>Flutter</b></span><span class="cat fill"><b>AT</b><span class="it">TA</span></span><span class="cat fill"><b>AVNRT</b><span class="it">TRN</span></span><span class="cat fill"><b>PSVT</b><span class="it">WPW / TPSV</span></span><span class="cat fill"><b>VT</b><span class="it">TV</span></span><span class="cat fill"><b>VF</b><span class="it">FV</span></span><span class="cat fill"><b>Ventricular ectopy</b><span class="it">Extrasistolia V.</span></span><span class="cat fill"><b>Syncope</b><span class="it">Sincope</span></span><span class="cat fill"><b>Long QT</b><span class="it">QT lungo</span></span>
</div>
<div class="tier-h">TIER 2 · structural<span class="cnt">5</span></div>
<div class="cats">
<span class="cat"><b>AV block</b><span class="it">Blocco AV</span></span><span class="cat"><b>Bundle branch block</b><span class="it">Blocco di branca</span></span><span class="cat"><b>Cardiomyopathy</b><span class="it">Cardiomiopatia</span></span><span class="cat"><b>Heart failure</b><span class="it">Scompenso</span></span><span class="cat"><b>Valvular disease</b><span class="it">Valvulopatia</span></span>
</div>
<div class="onto-points">
<div class="oitem"><span class="n">01</span><div><b>Order encodes clinical precedence</b> <span class="d">— “Brugada” is matched before “TV”, so “substrato per TV” <span class="g">(“substrate for VT”)</span> can't mask a Brugada pattern</span></div></div>
<div class="oitem"><span class="n">02</span><div><b>Synonyms live inline</b> <span class="d">— FA / f.a. / fib. atriale / atrial fibrillation → one canonical label</span></div></div>
<div class="oitem"><span class="n">03</span><div><b>Versioned like software</b> <span class="d">— semver for the pattern library, <code>pattern_version</code> on every extracted row</span></div></div>
</div>
</div>
<div class="out-panel">
<div class="out-h">The output: text becomes recorded data</div>
<div class="out-item"><span class="n">01</span><div><b>Quantitative data on the records</b><span class="d">every procedure and implant the text analysis reads is written back as structured, quantitative values on the record that describes it</span></div></div>
<div class="out-item"><span class="n">02</span><div><b>Straight into the DWH flow</b><span class="d">these records enter the data-warehouse generation like any other source</span></div></div>
<div class="out-item"><span class="n">03</span><div><b>As if typed at the visit</b><span class="d">the data lands exactly as if clinicians had keyed it in themselves during the visits</span></div></div>
</div>
</div>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">06 / 11</span></footer>
<aside class="notes">
<p>Our clinical ontology, a shared set of terms, helps identify arrhythmia-related diagnoses in the narrative.</p>
<p>The first tier covers conditions such as atrial fibrillation and Brugada. If none are found within a text field, the system checks a second tier for cardiovascular comorbidities, such as cardiomyopathy or heart failure, that may influence the patient's arrhythmic presentation.</p>
<p>Different expressions map to one concept: for example, "fibrillazione atriale" and "atrial fibrillation" receive the same label.</p>
<p>The extracted records enter the warehouse alongside data clinicians entered in structured fields. We can then define patient groups and research datasets, retaining each finding's source and the version of the extraction rules.</p>
</aside>
</section>
<section class="arit center-v validation">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
</header>
<nav class="band">
<span class="band-section">Text analysis</span>
</nav>
<div class="sbody">
<!-- Verified against aritmolab/chirone-etl on Gitea, commit 2ab00106188f298c1a0c1e2c48b3a4dbc66c45b5.
Exact sources and interpretation limits: ../slides/08-validation-sources.md.
SC-003/004 are targets; ~893 refers to false-positive new-letter records, not distinct patients. -->
<div class="kicker">Validation</div>
<h2>Trusting the text is a process, not a promise</h2>
<div class="onto-split">
<div class="onto-left">
<div class="onto-points">
<button type="button" class="oitem lens-target" id="validation-quality" data-lens="validation-quality" data-lens-number="1" data-lens-placement="center" data-lens-caption="Validation · 01 / 04" aria-label="Explore Quality gates" aria-expanded="false" aria-controls="slide-lens"><span class="n">01</span><span><b>Quality gates</b> <span class="d">— targets: ≥95% procedure accuracy on 100 manual reviews; ≥85% pathology coverage</span></span>
<template class="lens-details">
<p class="detail-intro">We separate correct classification from how often the rules find a condition.</p>
<p><b>Procedure classification</b>The specification sets a target of at least 95% agreement with a manual review of 100 randomly selected records: did the system assign the right procedure type?</p>
<p><b>Pathology coverage</b>The 85% target asks whether at least one recognized arrhythmia is extracted from a record, assuming most procedures concern known arrhythmias. It does not measure whether every diagnosis is correct or every disease is found.</p>
<p class="detail-example"><b>How to interpret the numbers</b>These are acceptance targets. Clinical review is still needed to detect incorrect labels and missed findings; coverage alone cannot establish clinical accuracy.</p>
</template>
</button>
<button type="button" class="oitem lens-target" id="validation-review" data-lens="validation-review" data-lens-number="2" data-lens-placement="center" data-lens-caption="Validation · 02 / 04" aria-label="Explore Clinical criteria" aria-expanded="false" aria-controls="slide-lens"><span class="n">02</span><span><b>Clinical criteria</b> <span class="d">— explicit criteria determine which patients belong in the analysis; a disease name alone is not enough</span></span>
<template class="lens-details">
<p class="detail-intro">Finding a disease name is the first step. Inclusion in an analysis depends on what the text means and the criteria for that cohort.</p>
<p><b>Read the context</b>“Father with Brugada” concerns a relative; “test negative for Brugada” reports a negative result; “suspected Brugada” expresses uncertainty. None of these phrases alone establishes a diagnosis in the patient.</p>
<p><b>Make the selection explicit</b>Queries apply the cohort criteria to prepare the dataset. Superset displays the result. The diagnosis view first excludes negated findings and findings attributed to relatives.</p>
<p class="detail-example"><b>A rule used in this project</b>For Brugada and long QT syndrome, that view also requires a positive provocative test or an ablation for the condition, at patient level. Other pathologies use only the first filter. These are project inclusion rules, not a universal diagnostic standard.</p>
</template>
</button>
<button type="button" class="oitem lens-target" id="validation-feedback" data-lens="validation-feedback" data-lens-number="3" data-lens-placement="center" data-lens-caption="Validation · 03 / 04" aria-label="Explore Feedback loop" aria-expanded="false" aria-controls="slide-lens"><span class="n">03</span><span><b>Feedback loop</b> <span class="d">— v1.3.1 fixed missed negations behind ~893 false-positive Brugada records</span></span>
<template class="lens-details">
<p class="detail-intro">The 11 June 2026 audit identified about 893 false-positive Brugada records in the newer letter format.</p>
<p><b>The error and the fix</b>“Test alla flecainide negativo per sindrome di Brugada” was treated as an affirmed finding: the rules recognized “negato”, but missed “negativo”. Version 1.3.1 added “negativo”, “negativa” and “negativi” before and after the condition.</p>
<p><b>Make the correction repeatable</b>Regression tests require that this sentence still produces a Brugada finding, now marked as negated. A separate test checks that an affirmed Brugada diagnosis remains positive. Reprocessing applies the revised rules to historical letters.</p>
<p class="detail-example"><b>What changed</b>The finding is reclassified, not erased. The ~893 figure counts affected records, not necessarily distinct patients; the cohort filters can now exclude those negated findings.</p>
</template>
</button>
<button type="button" class="oitem lens-target" id="validation-context" data-lens="validation-context" data-lens-number="4" data-lens-placement="center" data-lens-caption="Validation · 04 / 04" aria-label="Explore Nothing is silently dropped" aria-expanded="false" aria-controls="slide-lens"><span class="n">04</span><span><b>Nothing is silently dropped</b> <span class="d">— negated and family-attributed findings are stored too, filtered only at the mart layer</span></span>
<template class="lens-details">
<p class="detail-intro">A recognized finding can be kept even when it is negated or refers to someone else.</p>
<p><b>Store context alongside the finding</b>The extractor looks up to 60 characters before and after the match, stopping at a full stop, semicolon or line break. It records whether the finding is negated and whether it concerns the patient or a relative.</p>
<p><b>Filter when building the research dataset</b>Those flags travel through integration and the warehouse to the mentions dataset. The confirmed-diagnosis view excludes negated and family findings, while the underlying dataset remains available for other analyses.</p>
<p class="detail-example"><b>Preservation has a defined scope</b>Repeated matches for the same pathology are consolidated, preferring an affirmed patient finding. “Suspected” or “to exclude” is not automatically treated as a negation. These limits remain explicit for clinical review.</p>
</template>
</button>
</div>
<div class="roi roi-quiet"><b class="k">Quality gate</b>Manual comparison checks procedure accuracy. Pathology coverage and clinical correctness are assessed separately.</div>
</div>
<button type="button" class="out-panel lens-target" id="validation-advantages" data-lens="validation-advantages" data-lens-number="5" data-lens-title="The advantages" data-lens-placement="center" data-lens-caption="Validation · Advantages" aria-label="Explore the advantages: speed, precision, determinism" aria-expanded="false" aria-controls="slide-lens">
<span class="out-h">The advantage: speed, precision, determinism</span>
<span class="out-item"><span class="n">01</span><span><b>Machine speed</b><span class="d">twenty years of letters are read by rules, not by hand; when a pattern improves, the whole archive can be reprocessed</span></span></span>
<span class="out-item"><span class="n">02</span><span><b>Explicit quality targets</b><span class="d">≥95% procedure accuracy on a manual sample; ≥85% pathology coverage, with clinical review of the resulting cohort</span></span></span>
<span class="out-item"><span class="n">03</span><span><b>Deterministic by design</b><span class="d">the same letter and rule version yield the same extraction: no LLM sampling, no randomness, results traceable to versioned rules</span></span></span>
<template class="lens-details">
<p class="detail-intro">Speed, precision and determinism each address a different part of making clinical text usable for research.</p>
<p><b>01 · Speed: apply a correction across the archive</b>Once defined, the extraction rules process letters automatically. An improved rule can be applied again to historical text, without manually relabelling every record.</p>
<p><b>02 · Precision: make errors visible and correctable</b>Explicit quality targets, clinical context and cohort filters make the results inspectable. The Brugada correction shows how an audit can identify a recurring error, and regression tests can protect the fix. Quality still needs measurement and clinical review.</p>
<p><b>03 · Determinism: reproduce the extraction</b>With the same input, rule version and configuration, extraction produces the same findings. There is no language-model sampling at this step. Versioning explains why a result changes when the rules change.</p>
</template>
</button>
</div>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">07 / 11</span></footer>
<aside class="notes">
<p>(1) To check the extraction, we set a 95% procedure-accuracy target against 100 manual reviews. The 85% pathology target measures coverage, not diagnostic accuracy.</p>
<p>(2) Finding a disease name in a letter is only the first step. We also apply explicit clinical criteria to decide which patients belong in the analysis.</p>
<p>(3) Review feeds back into the rules. Version 1.3.1 corrected missed negations in roughly 900 Brugada records, with regression tests protecting the fix.</p>
<p>(4) Throughout this process, negated and family findings retain their context. Research datasets filter them explicitly, so preserving a finding does not mean counting it as the patient’s diagnosis.</p>
<p>(5) This lets us reprocess the archive quickly, inspect the rules behind each result, and reproduce the same extraction with the same rules.</p>
</aside>
</section>
<!-- ============ 09 · ARITMOLAB TODAY ============ -->
<section class="arit">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
</header>
<nav class="band">
<span class="band-section">AritmoLab today</span>
</nav>
<div class="sbody">
<div class="kicker">The portal, today</div>
<h2>AritmoLab — a quick tour</h2>
<div class="screenshot-tour screenshot-tour-four" data-screenshot-auto-open aria-label="AritmoLab tour, screenshots 1 to 4">
<button type="button" data-screenshot="home" data-screenshot-number="1" data-screenshot-title="Home page" aria-haspopup="dialog"><img src="screenshots/1-HomePage.png" alt="" loading="lazy"><span><b>1</b> Home page</span></button>
<button type="button" data-screenshot="profile" data-screenshot-number="2" data-screenshot-title="Patient data" aria-haspopup="dialog"><img src="screenshots/3-PatientGeneralData.png" alt="" loading="lazy"><span><b>2</b> Patient data</span></button>
<button type="button" data-screenshot="dashboards" data-screenshot-number="3" data-screenshot-title="Dashboard catalogue" aria-haspopup="dialog"><img src="screenshots/6-DashboardsList.png" alt="" loading="lazy"><span><b>3</b> Dashboard catalogue</span></button>
<button type="button" data-screenshot="brugada" data-screenshot-number="4" data-screenshot-title="Brugada: one of many dashboards" aria-haspopup="dialog"><img src="screenshots/7-Brugada-dashboard.png" alt="" loading="lazy"><span><b>4</b> Brugada: one of many dashboards</span></button>
</div>
<p class="hint">Select a screen to enlarge · Follow the tour from 1 to 4</p>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">08 / 11</span></footer>
<aside class="notes">
<div data-popup-notes="home"><div data-popup-notes-body>
<p>(1) AritmoLab is an extensive portal with many interconnected features. Our limited time prevents us from presenting it in detail, so we will show just four screens. Sara Paratico and I are available for a more in-depth presentation on request, either during the conference or afterwards through a remote connection.</p>
<p>The home page summarises around 57,000 patients by age, sex and geographical origin. It is the starting point for exploring the archive.</p>
</div></div>
<div data-popup-notes="profile"><div data-popup-notes-body>
<p>(2) From this overview, we can open a patient profile, which brings demographic and clinical information together, with access to procedures, devices, diagnostic examinations and genetics. It provides a single starting point for exploring an individual patient's record.</p>
</div></div>
<div data-popup-notes="dashboards"><div data-popup-notes-body>
<p>(3) Moving from individual patient records to an overview of the department, the dashboard catalogue gives us access to clinical activity, procedures, devices and genetics.</p>
</div></div>
<div data-popup-notes="brugada"><div data-popup-notes-body>
<p>(4) Among the many dashboards we could present, we chose Brugada as an example. The funnel separates text mentions, filtered mentions, confirmed cases and ablation outcomes. Other charts show sex, age, annual diagnoses and the timing of pre- and post-assessments.</p>
</div></div>
</aside>
</section>
<!-- ============ 10 · CRISIS ============ -->
<section class="arit crisis">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
</header>
<nav class="band">
<span class="band-section">The crisis</span>
</nav>
<div class="sbody">
<div class="kicker">All good? Not yet.</div>
<h2>The warehouse speaks SQL. Research needs more.</h2>
<div class="hgap"></div>
<div class="crisis-points" aria-label="Four barriers between the warehouse and research">
<button type="button" class="oitem lens-target" id="crisis-language" data-lens="crisis-language" data-lens-number="1" data-lens-title="From clinical question to SQL" data-lens-placement="center" data-lens-caption="The research gap · 01 / 04" aria-expanded="false" aria-controls="slide-lens"><span class="n">01</span><span><b>Clinical questions need translation</b><span class="d">Clinicians define the question; a query must express it across linked tables.</span></span>
<template class="lens-details">
<p class="detail-intro">Knowing what to ask is different from knowing how the database stores the answer.</p>
<p><b>The clinical question</b>“How many patients with confirmed Brugada underwent an ablation?” requires agreed definitions of the cohort and procedure.</p>
<p><b>The engineering task</b>SQL must connect diagnoses, patients and procedures, apply the time window and count each patient once, even when several records describe the same person.</p>
<p class="detail-example"><b>The bridge</b>Clinical expertise defines the meaning. A reviewed query turns that meaning into an explicit, checkable selection.</p>
</template>
</button>
<button type="button" class="oitem lens-target" id="crisis-intelligence" data-lens="crisis-intelligence" data-lens-number="2" data-lens-title="Health Intelligence: describe what happened" data-lens-placement="center" data-lens-caption="The research gap · 02 / 04" aria-expanded="false" aria-controls="slide-lens"><span class="n">02</span><span><b>Health Intelligence needs shared definitions</b><span class="d">Volumes, diagnoses and outcomes need consistent groups, periods and denominators.</span></span>
<template class="lens-details">
<p class="detail-intro">A dashboard needs agreed indicators, not simply a chart drawn over raw records.</p>
<p><b>Define what is counted</b>Patients, admissions and procedures answer different questions. For an outcome percentage, specify which patients are eligible and the observation period.</p>
<p><b>Prepare comparable summaries</b>Aggregate by year, procedure or patient group using the same definitions. Keep missing information visible so that changes in documentation are not mistaken for changes in care.</p>
<p class="detail-example"><b>Example</b>Annual ablation volumes describe activity. An outcome percentage also needs a defined denominator and follow-up window.</p>
</template>
</button>
<button type="button" class="oitem lens-target" id="crisis-prediction" data-lens="crisis-prediction" data-lens-number="3" data-lens-title="Predictive research: build the study table" data-lens-placement="center" data-lens-caption="The research gap · 03 / 04" aria-expanded="false" aria-controls="slide-lens"><span class="n">03</span><span><b>Predictive research needs a study dataset</b><span class="d">A defined cohort, consistent variables and outcomes measured over an agreed period.</span></span>
<template class="lens-details">
<p class="detail-intro">Linked clinical records must become a table shaped around the study question.</p>
<p><b>Define one row</b>Choose the unit of analysis: for example, one patient or one procedure. Place the selected characteristics in columns, with consistent units and explicit handling of missing values.</p>
<p><b>Respect the timeline</b>Define when prediction would occur and when the outcome is assessed. Predictor variables must contain only information available at that prediction time.</p>
<p class="detail-example"><b>Example</b>To study outcomes after ablation, separate pre-procedure characteristics from later observations. This prevents future information from leaking into the prediction.</p>
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</button>
<button type="button" class="oitem lens-target" id="crisis-datamarts" data-lens="crisis-datamarts" data-lens-number="4" data-lens-title="Datamarts: the preparation bottleneck" data-lens-placement="center" data-lens-caption="The research gap · 04 / 04" aria-expanded="false" aria-controls="slide-lens"><span class="n">04</span><span><b>Hand-made datamarts are the bottleneck</b><span class="d">Each question requires selection, joins, checks and a reproducible dataset.</span></span>
<template class="lens-details">
<p class="detail-intro">A datamart is a focused dataset prepared for a particular analysis.</p>
<p><b>More than writing SQL</b>Someone must agree the cohort, connect the sources, resolve duplicate records and check missing values and patient counts. A query can run successfully and still answer the wrong question.</p>
<p><b>Make the work repeatable</b>Keep the selection rules, query and checks together, so the dataset can be rebuilt when the data or study definition changes.</p>
<p class="detail-example"><b>Where assistance helps</b>AI can draft the query. Clinicians and engineers still review its meaning and results before using the dataset.</p>
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</button>
</div>
<div class="roi"><b class="k">The gap</b>Twenty years of data, one warehouse — and no fast road from a research question to an answer.</div>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">09 / 11</span></footer>
<aside class="notes">
<p>The warehouse speaks SQL, which means Structured Query Language. It tells a database what to select, connect and count.</p>
<p>(1) To answer a clinical question such as “How many patients with confirmed Brugada underwent an ablation?”, we must define confirmation and count each patient once.</p>
<p>(2) The same need for clarity applies to Health Intelligence: dashboards need agreed definitions, time periods and denominators to make comparisons meaningful.</p>
<p>(3) For predictive research, study tables separate characteristics known before prediction from outcomes observed afterwards.</p>
<p>(4) A datamart provides an analysis dataset with explicit selection rules and repeatable checks.</p>
<p>The gap is therefore between clinical meaning and database instructions: storing data does not automatically make a question answerable.</p>
</aside>
</section>
<!-- ============ 11 · THOTHII TOUR ============ -->
<section class="arit">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
</header>
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<span class="band-section">A tour of ThothII</span>
</nav>
<div class="sbody">
<div class="kicker">Human in the Loop · Eight phases, six screens</div>
<h2>From clinical question to datamart, with human review</h2>
<div class="screenshot-tour screenshot-tour-compact" data-screenshot-auto-open aria-label="ThothII tour, screenshots 1 to 6">
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<img src="screenshots/thothii/01-StartingPoint.png?v=4" alt="" loading="lazy"><span><b>1</b> Why ThothII exists</span>
</button>
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<img src="screenshots/thothii/02-Disambiguation01.png?v=4" alt="" loading="lazy"><span><b>2</b> Agreeing on the question</span>
</button>
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<img src="screenshots/thothii/05-CloseSchemaLinking.png?v=4" alt="" loading="lazy"><span><b>3</b> Connecting to the data</span>
</button>
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<img src="screenshots/thothii/07-CTE01.png?v=4" alt="" loading="lazy"><span><b>4</b> Checking each step</span>
</button>
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<img src="screenshots/thothii/08-FinalSQL.png?v=5" alt="" loading="lazy"><span><b>5</b> Approving the final query</span>
</button>
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<img src="screenshots/thothii/09-DatamartProduction.png?v=5" alt="" loading="lazy"><span><b>6</b> Reusing the result</span>
</button>
</div>
<p class="hint"><strong>AI proposes. People review, correct and approve.</strong> · Select a screen to enlarge</p>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">10 / 11</span></footer>
<aside class="notes">
<div data-popup-notes="thothii-start"><div data-popup-notes-body>
<p>(1) ThothII combines a wide range of capabilities in a guided, eight-phase workflow. A full presentation deserves at least thirty minutes. Here, we focus on the main steps.</p>
<p>It helps researchers turn clinical questions into checked SQL and reusable analysis datasets, bridging clinical meaning and database structure. AI proposes, and people review, correct and approve. This is Human in the Loop throughout the workflow, combining clinical judgment and database expertise.</p>
</div></div>
<div data-popup-notes="thothii-disambiguation-1"><div data-popup-notes-body>
<p>(2) We begin by clarifying the question: here, what counts as an atrial fibrillation ablation? We then review relevant knowledge from earlier work before approving a precise reformulation: how many patients had their first recorded AF ablation between 2020 and 2023? We find the first procedure across the available history before filtering dates. The reviewer can challenge the interpretation.</p>
</div></div>
<div data-popup-notes="thothii-schema-final"><div data-popup-notes-body>
<p>(3) With the question agreed, we connect its concepts to tables, fields and relationships. We then review the mapping and selection rules together before building SQL. The reviewer checks which patients, procedures and dates will count.</p>
</div></div>
<div data-popup-notes="thothii-cte-1"><div data-popup-notes-body>
<p>(4) Once these choices are clear, we build and test smaller query steps, called CTEs. Each has a purpose, SQL and sample results for review. Here, we inspect patient and procedure records. Successful execution alone does not establish clinical correctness, so the reviewer can accept or request changes.</p>
</div></div>
<div data-popup-notes="thothii-sql"><div data-popup-notes-body>
<p>(5) We can now assemble and verify the final SQL against the agreed question. The reviewer approves it or requests changes, checking that the query answers the original clinical intent.</p>
</div></div>
<div data-popup-notes="thothii-datamart"><div data-popup-notes-body>
<p>(6) With the query approved, we decide whether to produce a datamart: an analysis dataset that can be refreshed through the ETL pipeline. We also choose which clarifications to retain for future questions. The saved artifacts and review decisions document how the result was reached.</p>
</div></div>
</aside>
</section>
<section class="arit title-slide">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
</header>
<nav class="band">
<span class="band-section">Thank you</span>
</nav>
<div class="sbody">
<div class="kicker">Questions?</div>
<h1>Thank you</h1>
<div class="hgap"></div>
<p class="lead">We are available to explore AI in data reorganization and clinical text analysis,<br>and datamart generation from natural-language requests with ThothII.<br>Meet us during the conference or arrange a remote session.</p>
<div class="speakers" style="margin-top:32px">
<span>Dr. Marco Pancotti - MultiPhysixLab</span>
<a href="mailto:mpancotti@mpxlab.org" style="font-size:32px;font-weight:700;color:var(--bordeaux)">mpancotti@mpxlab.org</a>
<span>Dr. Sara Paratico - I.R.C.C.S. Policlinico San Donato</span>
<a href="mailto:sara.paratico@grupposandonato.it" style="font-size:32px;font-weight:700;color:var(--bordeaux)">sara.paratico@grupposandonato.it</a>
</div>
</div>
<footer class="foot"><span class="g">Role of AI in the Analysis of Unstructured Clinical Databases</span><span class="g">Dr. Marco Pancotti - MultiPhysixLab</span><span class="g">Dr. Sara Paratico - Gruppo San Donato</span><span class="g">San Donato Milanese, Milan, Italy · 2–3 October 2026</span><span class="g num">11 / 11</span></footer>
<aside class="notes">
Thank you for your attention. Sara Paratico and I are available for a closer look at how we use AI to reorganize data, analyse clinical text, and generate datamarts from natural-language requests with ThothII. We can discuss these topics and demonstrate the tools during the conference or remotely in the coming days. Please contact us at the email addresses on this slide.
</aside>
</section>
</div></div>
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ingestion: {
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<ul class="ai-pop-list">
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<li><b>What is a mapping?</b> — a plain instruction sheet that tells the system, field by field, where each piece of data comes from and where it has to land</li>
<li><b>At scale</b> — <span class="n">19,764</span> lines of instructions, all machine-validated</li>
<li><b>Humans revise</b> — every line is checked, then recorded in <b>git</b>, the archive that keeps every version of the instructions and can undo any change</li>
</ul>
<div class="roi"><b class="k">Positive effects</b>Weeks of hand-writing became days of reviewing.</div>`,
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integration: {
title: 'Reading the clinical text',
body: `
<p class="lead">A deterministic, bilingual (IT/EN) pattern library reads the free text inside the nightly pipeline.</p>
<ul class="ai-pop-list">
<li><b>Pathologies</b> — <span class="n">73,389</span> extracted from <span class="n">58,438</span> discharge letters, into a two-tier clinical ontology</li>
<li><b>Procedures ↔ pathologies</b> — linked at clause level</li>
<li><b>Drug-challenge tests</b> — <span class="n">10,908</span> parsed (flecainide, ajmaline, adrenaline, isoprenaline)</li>
<li><b>No black box</b> — semver rules, <code>pattern_version</code> on every row, <span class="n">≥95%</span> accuracy gate on 100 manual reviews</li>
</ul>
<div class="roi"><b class="k">Positive effects</b>58,438 letters of free text became an analysable research asset — automatically, every night.</div>`,
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datamarts: {
title: 'Datamarts from plain English',
body: `
<p class="lead">Researchers ask in plain English; the AI writes the SQL over the star schema.</p>
<ul class="ai-pop-list">
<li><b>Ask</b> — “how many Brugada patients had an effective ablation?”</li>
<li><b>Generate</b> — AI builds the SQL and assembles a curated datamart</li>
<li><b>Serve</b> — results flow to Superset for statistics and machine learning</li>
<li><b>In the loop</b> — the researcher reviews every proposed query before it runs</li>
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<div class="roi"><b class="k">Positive effects</b>A new research datamart in minutes instead of weeks of hand-written SQL — with the researcher approving every query.</div>`,
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ci: { kicker: 'The missing piece · 20 years ago', title: 'Clinical Intelligence — the history, made visible',
body: `<p class="lead">Twenty years of clinical history, with no way to show it.</p>
<ul class="ai-pop-list"><li><b>What it is</b> — dashboards on the Unit's activity and its patients: volumes, incidence, outcomes</li>
<li><b>Who needed it</b> — management, audit, clinical research</li>
<li><b>Why it never existed</b> — the data was in prose; building dashboards on text was impractical</li></ul>
<div class="roi"><b class="k">The gap</b>No fast answer to “how are we doing?” — every question meant a manual query.</div>` },
dwh: { kicker: 'The missing piece · 20 years ago', title: 'Datawarehouse — one analysis-ready home',
body: `<p class="lead">One home for every record the Unit produces: text, structured fields, genetics, ECG.</p>
<ul class="ai-pop-list"><li><b>What it is</b> — a single schema joining all the sources, designed for analysis</li>
<li><b>Why it never existed</b> — each system kept its own silo; joining them meant hand-stitching exports</li></ul>
<div class="roi"><b class="k">The gap</b>Every research question started with manual exports — and ended in fragile spreadsheets.</div>` },
ml: { kicker: 'The missing piece · 20 years ago', title: 'ML-ready data — cohorts for research',
body: `<p class="lead">Tables shaped for statistics and model training: clean, wide, reproducible.</p>
<ul class="ai-pop-list"><li><b>What it is</b> — one row per patient, features beside outcomes, rebuildable at will</li>
<li><b>Why it never existed</b> — building it by hand from free text and separate silos took weeks</li></ul>
<div class="roi"><b class="k">The gap</b>Predictive research on the Unit's data stayed out of reach.</div>` },
portal: { kicker: 'The missing piece · 20 years ago', title: 'Professional Portal — the 360° view',
body: `<p class="lead">The 360° view of the patient: Cardioref and genetics finally in one place.</p>
<ul class="ai-pop-list"><li><b>What it is</b> — the grown-up Omics Portal: management, care and research on the same platform</li>
<li><b>Why it never existed</b> — the idea was there; the integrated data to feed it was not</li></ul>
<div class="roi"><b class="k">The gap</b>A single door for patients and clinicians — promised, never delivered.</div>` },
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cardioref: {
kicker: 'The starting point · 20 years ago',
title: 'Cardioref — the daily workhorse',
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<p class="lead">For twenty years, the database running the Arrhythmology Unit.</p>
<ul class="ai-pop-list">
<li><b>Used for</b> — patient records, the Unit's daily activity, planning and logging of everything that was done</li>
<li><b>Good at it</b> — as a management database, it did (and still does) its job</li>
<li><b>The catch</b> — most clinical information lives in free text, outside the structured fields</li>
</ul>
<div class="roi"><b class="k">Problem</b>Almost unusable for research and data extraction: the knowledge is there — locked in prose.</div>`,
},
genetic: {
kicker: 'The starting point · 20 years ago',
title: 'Genetic data — an island',
body: `
<p class="lead">A dedicated system collecting the genetic results of our patients.</p>
<ul class="ai-pop-list">
<li><b>Used for</b> — storing genetic tests, variants and lab reports</li>
<li><b>The catch</b> — its only output is Excel sheets, with no integration into Cardioref</li>
</ul>
<div class="roi"><b class="k">Problem</b>Genotypes on one side, clinical records on the other — no way to join them.</div>`,
},
omics: {
kicker: 'The starting point · 20 years ago',
title: 'Aritmolab Portal — born too early',
body: `
<p class="lead">A 360° patient-management system, still in its infancy.</p>
<ul class="ai-pop-list">
<li><b>Used for</b> — the first patient-management features beyond the hospital electronic health record</li>
<li><b>The catch</b> — embryonic functionality, and no integration with Cardioref</li>
</ul>
<div class="roi"><b class="k">Problem</b>A promising idea without a bridge: it could not see the clinical history stored elsewhere.</div>`,
},
ecg: {
kicker: 'The starting point · 20 years ago',
title: 'ECG subsystems — one per device',
body: `
<p class="lead">A constellation of ECG systems, each with its own software and its own silo.</p>
<ul class="ai-pop-list">
<li><b>Used for</b> — acquiring and storing ECG signals</li>
<li><b>The catch</b> — no machine-to-machine communication: the only export is a CSV file, on request</li>
</ul>
<div class="roi"><b class="k">Problem</b>Every ECG meant a manual download — automation was impossible by design.</div>`,
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if (lens) {
const wasOpen = SlideLens.active?.key === lens.dataset.lens;
closePop();
if (!wasOpen) SlideLens.open(lens);
return;
}
if (e.target.closest('.slide-lens-close')) { closePop(); return; }
const b = e.target.closest('.brain-btn[data-ai]');
if (b) { openPop(b.dataset.ai, b, AI_CONTRIBS); return; }
const s = e.target.closest('.isle[data-start]');
if (s) { openPop(s.dataset.start, s, START_CONTRIBS); return; }
const m = e.target.closest('.todo[data-missing]');
if (m) { openPop(m.dataset.missing, m, MISSING_CONTRIBS); return; }
if (e.target.closest('.ai-pop-x') || e.target === pop) closePop();
if (SlideLens.active && !e.target.closest('.slide-lens')) closePop();
});
// Escape closes the popup first (capture), without toggling Reveal's overview
document.addEventListener('keydown', (e) => {
if (e.key === 'Escape' && (pop.classList.contains('open') || SlideLens.active || ScreenshotTour.active)) {
closePop(); e.preventDefault(); e.stopPropagation();
}
const block = e.target.closest('button[data-lens], button[data-screenshot]');
if (block && (e.key === 'Enter' || e.key === ' ')) {
e.preventDefault(); e.stopPropagation();
if (!e.repeat) block.click();
return;
}
if (e.repeat || e.altKey || e.ctrlKey || e.metaKey || e.target.closest('input, select, textarea, [contenteditable="true"]')) return;
const lens = [...(Reveal.getCurrentSlide()?.querySelectorAll('[data-lens-number], [data-screenshot-number]') || [])]
.find(button => (button.dataset.lensNumber || button.dataset.screenshotNumber) === e.key);
if (lens) { lens.click(); e.preventDefault(); e.stopPropagation(); }
}, true);
// Small shared interface for the presenter console and its synchronized audience view.
const popupSelector = '[data-lens], [data-screenshot], .brain-btn[data-ai], .isle[data-start], .todo[data-missing]';
const popupIdentity = el => {
const kind = ['lens', 'screenshot', 'ai', 'start', 'missing'].find(k => el.dataset[k]);
return { kind, key: el.dataset[kind] };
};
const popupDictionary = kind => ({ ai: AI_CONTRIBS, start: START_CONTRIBS, missing: MISSING_CONTRIBS })[kind];
window.PresentationControls = {
snapshot: () => ({ index: Reveal.getIndices().h, popup: ScreenshotTour.active || SlideLens.active || activePopup }),
slides: () => Reveal.getSlides().map(slide => ({
title: slide.querySelector('h1, h2')?.textContent.trim() || '',
notes: slide.querySelector('aside.notes')?.innerHTML || '',
popupNotes: [...slide.querySelectorAll('aside.notes [data-popup-notes]')].map(note => ({
key: note.dataset.popupNotes,
html: note.querySelector('[data-popup-notes-body]').innerHTML,
})),
popups: [...slide.querySelectorAll(popupSelector)].map(el => {
const id = popupIdentity(el);
if (id.kind === 'screenshot') return { ...id, title: el.dataset.screenshotTitle };
const source = id.kind === 'lens' ? document.getElementById(id.key) : null;
return { ...id, title: source ? source.dataset.lensTitle || source.querySelector('b').textContent : popupDictionary(id.kind)[id.key].title };
}),
})),
openPopup: id => {
const el = [...Reveal.getCurrentSlide().querySelectorAll(popupSelector)]
.find(el => { const candidate = popupIdentity(el); return candidate.kind === id.kind && candidate.key === id.key; });
if (el && id.kind === 'screenshot') {
if (ScreenshotTour.active?.key !== id.key) { closePop(); ScreenshotTour.open(el); }
} else if (el && id.kind === 'lens') {
if (SlideLens.active?.key !== id.key) { closePop(); SlideLens.open(el); }
else SlideLens.layout();
} else if (el) openPop(id.key, el, popupDictionary(id.kind));
},
closePopup: closePop,
};
// Open the first tour stop only on slide entry, never on hover or focus.
// The thumbnail stays unobstructed; synchronized state can still close or replace the popup.
const openInitialScreenshot = () => {
if (new URLSearchParams(location.search).get('view') === 'thumbnail') return;
const first = Reveal.getCurrentSlide()?.querySelector('[data-screenshot-auto-open] [data-screenshot-number="1"]');
if (first) window.PresentationControls.openPopup(popupIdentity(first));
};
Reveal.on('slidechanged', openInitialScreenshot);
if (Reveal.isReady()) openInitialScreenshot();
else Reveal.on('ready', openInitialScreenshot);
addEventListener('resize', () => {
if (activePopup) window.PresentationControls.openPopup(activePopup);
});
</script>
<script src="presenter-bridge.js"></script>
</body>
</html>
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