The pitfalls and potential of using ML to personalize patient treatments | AI FOR GOOD DISCOVERY
0In this talk, we discuss the idea of learning how to individualize patient treatments based on data such as electronic medical records, patient generated data, and clinical trial data. We present the notion of causality and explain why “ordinary” supervised machine learning is insufficient in many cases for properly learning individualized treatments, and why learning such treatment is strictly more challenging. A framework is presented for evaluating whether a given clinical problem and dataset are amenable for learning individualized treatments, as well as two case studies: one in chronic diabetes care and one in chronic heart failure care.
Speaker 🎙
Uri Shalit, Senior lecturer, @technion.israelIsrael Institute of Technology, Faculty of Industrial Engineering and Management
00:00 Intro
1:56 Panellist intro
4:12 personalized treatment with patient data
19:27 Identification 1: The target system
22:22 Identification 2: Is the data suitable?
33:00 Preliminary results – Chronic disease
37:00 Preliminary results – Acute disease
39:46 Preliminary results – Heart failure with kidney injury
44:30 Should we believe this?
45:28 Q&A and Closing remarks
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