Project: Nightingale Project
Machine learning, we are told, will transform medical diagnosis and patient care: by integrating ‘big data’ on patients’ history and physiology, algorithms can dramatically improve the quality of doctors’ decisions, with the potential both to reduce waste, avoid misdiagnosis, and produce breakthrough discoveries. For example, if massive datasets of ECG waveforms could be linked to national mortality registries, we could supercharge the current research, and find better, more consistent ways to allocate life-saving defibrillators. But most clinical data like this is siloed by different institutions and unavailable to researchers. Further complicating things, in order to protect patient privacy, public medical datasets are almost universally limited to a single, easily de-identified stream of information, like a set of X-rays.
The goal of the Nightingale Project in the Booth Center for Applied AI is to gather and share just the sort of rich, multidimensional data needed to feed AI-enabled discovery. Work with a team of engineers, data analysts, and medical experts to build a secure platform that can warehouse curated de-identified clinical datasets linked to ground truth outcomes. Using this initiative as a proof of concept to develop other privacy tools, own and work through the de-identification, sharing, and privacy components of large-scale datasets — work that will include writing models and creating security-related challenges — with the goal of making the data available to researchers securely.
Mentor: Sendhil Mullainathan, Faculty Director, Center for Applied AI, Roman Family University Professor of Computation and Behavioral Science, Chicago Booth
Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth. His current research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data. He currently teaches a course on Artificial Intelligence.
In past work he has combined insights from economics and behavioral science with causal inference tools—lab, field, and natural experiments—to study social problems such as discrimination and poverty. Papers include: the impact of poverty on mental bandwidth; how algorithms can improve on judicial decision-making; whether CEO pay is excessive; using fictitious resumes to measure discrimination; showing that higher cigarette taxes makes smokers happier; and modeling how competition affects media bias.
Mullainathan enjoys writing. He recently co-authored Scarcity: Why Having too Little Means so Much and writes regularly for the New York Times. Additionally, his research has appeared in a variety of publications including the Quarterly Journal of Economics, Science, American Economic Review, Psychological Science, the British Medical Journal, and Management Science.
Mullainathan helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), serves on the board of the MacArthur Foundation, has worked in government in various roles, is affiliated with the NBER and BREAD, and is a member of the American Academy of Arts and Sciences.
Prior to joining Booth, Mullainathan was the Robert C. Waggoner Professor of Economics in the Faculty of Arts and Sciences at Harvard University, where he taught courses about machine learning and big data. He began his academic career at the Massachusetts Institute of Technology.
Mullainathan is a recipient of the MacArthur “Genius Grant,” has been designated a “Young Global Leader” by the World Economic Forum, was labeled a “Top 100 Thinker” by Foreign Policy Magazine, and was named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK).