De-identification of patients’ protected health information (PHI) from medical free-text | AISC

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=e-wAGEDl39Y


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Duration: 52:09
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For slides and more information on the paper, visit https://ai.science/e/data-de-identification-medical-text-phi-de-identification-of-patients-protected-health-information-phi-from-medical-free-text--2020-11-26

Speaker: Hesam Dadafarin; Host: Parisa Naraei, PhD

Motivation:
Massive amount of invaluable medical information is buried inside hospitals’ and physicians’ electronic medical records in the format of free-text. Examples include physician’s notes when you see your family doctor or the lab results when you do your blood work. Neither healthcare organizations nor governmental agencies who store and maintain these unstructured libraries of information can share them with researchers due to regulations around patients’ privacy. In this talk, Hesam is going to present a recent collaboration to leverage natural language processing algorithms to minimize the risk of re-identification for patients’ information. This solution is going to create many opportunities for healthcare researchers to conduct further studies on unstructured health information of patients.

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