Скачать или смотреть Learning from big medical data: statistical analysis on electronic health data

On August 23-24, 2018 the CMSA hosted our fourth annual Conference on Big Data. The Conference featured many speakers from the Harvard community as well as scholars from across the globe, with talks focusing on computer science, statistics, math and physics, and economics.

Speaker: Sam Kou

Title: Learning from big medical data: statistical analysis on electronic health data

Abstract: Big data have attracted significant interest from business, government agencies, academic communities and the general public. They offer the potential to transform knowledge discovery and decision making. We consider in this talk big medical data, in particular electronic health insurance data, which have been widely adopted over the last two decades to give healthcare and insurance providers faster and easier access to record, retrieve and process patient information. The massive health insurance data also present opportunities for studying the causal relationship between diseases and treatments. We will use our analysis of the causal relationship between cancer immunotherapy and autoimmune diseases as an illustration. Immunotherapy is one of the most exciting cancer treatments developed in the last five years; it works by enhancing the body’s own immune system to fight cancer and has been shown to extend patients’ life expectancy. An unintended side effect observed anecdotally by several doctors is that immunotherapy seems to lead to more autoimmune diseases. We analyzed an electronic health insurance data system, which covers over 44 million members, to study the potential causal relationship between cancer immunotherapy and autoimmune diseases. Mining the massive data allows us to answer the causal question. We will also discuss the complications and lessons we learned from working on big medical data.