AritmoLab · Policlinico San Donato

Role of AI in the Analysis of Unstructured Clinical Databases

How we turned a mix of structured and free-text clinical records into a research data platform — the AritmoLab platform experience.

AritmoLab A clinical data platform hosting cardiology data and analysis tools

Dr. Marco Pancotti - MultiPhysixLab Dr. Sara Paratico - I.R.C.C.S. Policlinico San Donato

“Multidimensional Characterization of Cardiac Arrhythmias: Role of Electrocardiology in the Artificial Intelligence Era”

San Donato Milanese, Milan, Italy · 2–3 October 2026

AritmoLab · Policlinico San Donato

The plan

What we wanted to build

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.

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What we built

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AI reads the clinical text: pathologies, procedures, drug-challenge outcomes

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AI builds them on demand from plain-English questions (ThothII)

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AritmoLab Data Warehousequeried for research

AritmoLab Portalmanagement & exploration

AI coding agents — Anthropic · OpenAI — used wherever they helped, 360° in the code Airflow · orchestration Superset · dashboards Django · portal scaffolding Authentik · auth — GSD LDAP

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.

(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.

(2) To connect them, staging preserves a working copy of the sources.

(3) Integration then cleans, normalizes and links the records.

(4) The data warehouse organizes events and their context for analysis.

(5) From there, datamarts turn that shared information into research datasets and dashboards within AritmoLab.

(6) The brain symbols show where AI contributes. It drafts the mappings, which humans review.

(7) During integration, AI extracts structured information from clinical text.

(8) ThothII then builds datamarts from plain-English questions, with human approval.

Open-source tools support the platform, and AI coding agents helped throughout development. The popups connect each component to the gap it addresses.

Next, we turn to the hardest part: unstructured data. Sara will explain how we extract clinical meaning and check its reliability.

AritmoLab · Policlinico San Donato

From free text to a research platform

The clinical truth lives in unstructured columns

“Il paziente riferisce sincope ricorrente; ECG basale con sopraslivellamento ST in V1–V3; test provocativo con flecainide positivo per pattern Brugada.”

EN“The patient reports recurrent syncope; baseline ECG with ST-segment elevation in V1–V3; flecainide provocation test positive for a Brugada pattern.”

SourcesCardioref · Genetic data
Omics Portal · ECG

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Stagingraw replica

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Integration3NF · text mining

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Data Warehousestar schema

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Martsdbt · Superset

four challenges to face · the answers come next

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Volume without structuretwenty years of records locked in free-text columns

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Ambiguous languagenegation · family history · bilingual shorthand

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No shared ontologyevery system names conditions its own way

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Proving reliabilityextraction must be demonstrably correct

outputs of the datamarts

Dashboardsclinical review on Superset

Machine Learningcohort tables for model training

Predictive Statisticsoutcomes & risk analysis

ThothII — natural-language → SQL over the DWH, builds the final datamarts

AritmoLab · Policlinico San Donato

What the text mining produced

Deterministic AI, audited numbers

58,438letters read

73,389pathologies extracted

10,908tests parsed

2,307Brugada patients

≈ 240letters / month

Letter readerevery discharge letter, Italian & English

Clinical matcherrecognises diagnoses, procedures, test outcomes

Context guardnegations & family history kept apart

Ontology sorterevery finding into its clinical category

Traceable. Every finding carries the version of the rules that produced it (pattern_version): any number can be rebuilt years later.

Audited. A new rule set goes live only after it proves ≥95% accuracy on 100 records reviewed by hand.

AritmoLab · Policlinico San Donato

Clinical NLP

Reading clinical text is not keyword matching

Negation — “fibrillazione atriale esclusa”, “non FA” (“AF ruled out”, “no AF”): the rule scans a window around every match before deciding
Family ≠ patient — “padre con FA” (“father with AF”) is attributed to the family member, not to the patient
Two languages — every pattern matches IT and EN forms: fibrillazione atriale / atrial fibrillation
Abbreviations & noise — FA, f.a., “TA 140/90” (blood pressure, not a diagnosis)
One field, many statements — the text is split into clauses before matching

what the rules seeexamples from the corpus

“padre con fibrillazione atriale”
“father with atrial fibrillation”
  → patologia: FA · attribuzione: familiare
pathology: AF · attribution: family

“fibrillazione atriale esclusa”
“atrial fibrillation ruled out”
  → patologia: FA · negato
pathology: AF · negated

“test provocativo con flecainide
  positivo per pattern Brugada”
“flecainide provocation test, positive for Brugada pattern”
  → test: flecainide · esito: POSITIVO
test: flecainide · outcome: POSITIVE

AritmoLab · Policlinico San Donato

The clinical ontology

Two tiers, one clinical order

TIER 1 · arrhythmic11

AFFABrugadaFlutterATTAAVNRTTRNPSVTWPW / TPSVVTTVVFFVVentricular ectopyExtrasistolia V.SyncopeSincopeLong QTQT lungo

TIER 2 · structural5

AV blockBlocco AVBundle branch blockBlocco di brancaCardiomyopathyCardiomiopatiaHeart failureScompensoValvular diseaseValvulopatia

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Order encodes clinical precedence — “Brugada” is matched before “TV”, so “substrato per TV” (“substrate for VT”) can't mask a Brugada pattern

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Synonyms live inline — FA / f.a. / fib. atriale / atrial fibrillation → one canonical label

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Versioned like software — semver for the pattern library, pattern_version on every extracted row

The output: text becomes recorded data

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Quantitative data on the recordsevery procedure and implant the text analysis reads is written back as structured, quantitative values on the record that describes it

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Straight into the DWH flowthese records enter the data-warehouse generation like any other source

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As if typed at the visitthe data lands exactly as if clinicians had keyed it in themselves during the visits

AritmoLab · Policlinico San Donato

Validation

Trusting the text is a process, not a promise

Quality gateManual comparison checks procedure accuracy. Pathology coverage and clinical correctness are assessed separately.

(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.

(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.

(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.

(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.

(5) This lets us reprocess the archive quickly, inspect the rules behind each result, and reproduce the same extraction with the same rules.

AritmoLab · Policlinico San Donato

The portal, today

AritmoLab — a quick tour

Select a screen to enlarge · Follow the tour from 1 to 4

(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.

The home page summarises around 57,000 patients by age, sex and geographical origin. It is the starting point for exploring the archive.

(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.

(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.

(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.

AritmoLab · Policlinico San Donato

All good? Not yet.

The warehouse speaks SQL. Research needs more.

The gapTwenty years of data, one warehouse — and no fast road from a research question to an answer.

The warehouse speaks SQL, which means Structured Query Language. It tells a database what to select, connect and count.

(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.

(2) The same need for clarity applies to Health Intelligence: dashboards need agreed definitions, time periods and denominators to make comparisons meaningful.

(3) For predictive research, study tables separate characteristics known before prediction from outcomes observed afterwards.

(4) A datamart provides an analysis dataset with explicit selection rules and repeatable checks.

The gap is therefore between clinical meaning and database instructions: storing data does not automatically make a question answerable.

AritmoLab · Policlinico San Donato

Human in the Loop · Eight phases, six screens

From clinical question to datamart, with human review

AI proposes. People review, correct and approve. · Select a screen to enlarge

(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.

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.

(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.

(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.

(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.

(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.

(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.

AritmoLab · Policlinico San Donato

Questions?

Thank you

We are available to explore AI in data reorganization and clinical text analysis,
and datamart generation from natural-language requests with ThothII.
Meet us during the conference or arrange a remote session.

Dr. Marco Pancotti - MultiPhysixLab mpancotti@mpxlab.org Dr. Sara Paratico - I.R.C.C.S. Policlinico San Donato sara.paratico@grupposandonato.it