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deck slide 05: compact numeral band on top (+monthly letters rate), analysis components as clinician-friendly flow with icons in the center

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<title>AritmoLab — Role of AI in the Analysis of Unstructured Clinical Databases</title>
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<link rel="stylesheet" href="deck.css?v=29">
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<div class="reveal"><div class="slides">
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</section>
<section class="arit center-v">
<section class="arit center-v stats">
<header class="head">
<img src="logo.png" alt="">
<span class="head-org">AritmoLab · Policlinico San Donato</span>
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<div class="stat"><span class="num">73,389</span><span class="lbl">pathologies extracted</span><span class="sub">two-tier clinical ontology</span></div>
<div class="stat"><span class="num">10,908</span><span class="lbl">drug-challenge tests parsed</span><span class="sub">flecainide · ajmaline · adrenaline · isoprenaline</span></div>
<div class="stat"><span class="num">2,307</span><span class="lbl">Brugada patients identified</span><span class="sub">confirmed by clinical review on dashboards</span></div>
<div class="stat month"><span class="num">≈ 240</span><span class="lbl">letters processed monthly</span><span class="sub">nightly batch on twenty years of history</span></div>
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<div class="comps">
<div class="comp"><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="arrow">→</div>
<div class="comp"><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="arrow">→</div>
<div class="comp"><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="arrow">→</div>
<div class="comp"><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>
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<p class="stat-note">Every row carries its pattern-library semver (<code>pattern_version</code>) · quality gate: ≥95% accuracy on 100 manual reviews.</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">05 / 14</span></footer>
<aside class="notes">
[DRAFT — Sara] I'll take you inside these numbers. We read 58,438 discharge letters — every single one, every night. From them the text miner extracted 73,389 pathology records, classified into our clinical ontology. It parsed 10,908 drug-challenge tests, distinguishing the therapy from the actual test. And at the end of the chain: 2,307 patients with a confirmed Brugada pattern — a cohort nobody could have built by hand.
This is not a one-off migration: the pipeline runs every night, and today it processes on average two hundred and forty letters a month. Four simple pieces do the work: a letter reader for the Italian and English text, a clinical matcher that recognises diagnoses, procedures and test outcomes, a context guard that keeps negations and family history apart, and the ontology sorter that files every finding into its clinical category.
The key word here is deterministic: no black box. Every number can be traced back to the rule that produced it.
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