#01 Natural Language Processing for Healthcare - Named Entity Recognition

In this episode we explore named entity recognition (NER) and its uses in clustering 1 million hospital inpatients, monitoring pandemics and outbreaks, automating clinical coding, enriching research cohorts, and more. Dev&Doc is a podcast where doctors and developers deep dive into the potential of AI in healthcare. 👨🏻‍⚕️Doc - Dr. Joshua Au Yeung 🤖Dev - Zeljko Kraljevic LinkedIn Newsletter YouTube Spotify Apple Substack For enquiries - 📧 [email protected] Timestamps: 00:00 Start 00:38 Intro 01:03 Setting the scene, clinical audit 03:13 What is Named Entity Recognition (NER) 14:59 Medical text as its own language 16:43 Medical abbreviations test 19:23 NER in different industries 21:55 NER with neural networks, deep learning, large language models 24:25 MedCAT medical concept annotation tool 25:50 When AI models go wrong, women get erectile dysfunction 28:20 Teaching a model to disambiguate 31:12 NER use case 1 - Clinical audit 33:04 How to fine tune a clinical model with clinician knowledge 36:07 NER use case 2 - Automating clinical audits 37:13 Why is NER not being used in the NHS? Windows XP 40:19 NHS is resistant to change 42:15 NER use case 3 - Enriching research databases 44:57 Which model should I use? 47:02 NER use case 4 - Extracting diseases from 1 million patients in King's College Hospital 52:05 Clustering 1 million patients with AI 55:14 Top 10 diagnoses in South London 58:45 Diseases by age in MIMIC dataset 1:01:27 Monitoring pandemic outbreaks 1:04:30 Predicting the future with Foresight References: Using machine learning for automated auditing of stroke comorbidities Hospital-wide natural language processing summarising the health data of 1 million patients 🎞️ Editor - Dragan Kraljević 🎨 Brand design and art direction - Ana Grigorovici

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