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We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), in service of the learning health system (see examples).

We answer clinical questions using aggregate patient data at the bedside. The Informatics Consult Service put this idea in action and led to the creation of Atropos Health.

We make predictions that allow taking mitigating actions, keeping the human in the loop. The Program for AI in Healthcare conducts the research which the Applied Data Science team puts into practice.

We develop methods to analyze multiple datatypes for generating insights. Such as:

Identifying biomarkers for poor outcomes in fibrotic diseases, learning reference intervals of laboratory tests and monitoring Point-of-Care glucose meters using routine laboratory testing data.
Detecting skin adverse reactions by analyzing content in a health social network, enabling medical device surveillance, discovering drug adverse events as well as drug-drug interactions from clinical notes using novel methods for processing textual documents.
Inferring physical function from wearables data, predicting healthcare utilization from Web search logs and understanding information seeking behavior of health professionals.

About us: Lab members, Open positions
Internal (log in required): On boarding, Compute Resources, Lab communication, Projects, Rotations, For Collaborators, Archived pages

Teaching

BIOMEDIN 215 Data Science for Medicine, taught for the BMI Graduate program is designed to prepare you to pose and answer meaningful clinical questions using routinely collected healthcare data.
BIOMEDIN 225 Data Science for Medicine, taught for the MCiM program explores how to use electronic health records (EHRs) and other patient data in conjunction with recent advances in artificial intelligence (AI) and evolving business models to improve healthcare.
AI in Healthcare Specialization on Coursera, which reviews the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically.
XBIOMEDIN215 Machine Learning Projects in Healthcare, where you work through interactive exercises and case studies, attend live webinars, receive ongoing feedback from the course team, and collaborate with your fellow learners to gain the real-world skills you need to run your own machine learning projects.
AI in Healthcare Bootcamp, provides students an opportunity to do cutting-edge research at the intersection of AI and healthcare.
Miscellaneous Talks, Seminars

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