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We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system. Read more ...
We answer clinical questions to enable better medical decisions using EHR and Claims data, via a bedside consult service that enables the use of aggregate patient data at the point of care. Check out our Informatics Consult Service that puts this idea in action.
We make predictions that allow taking mitigating actions. We characterize the fairness and examine the ethical implications of using machine learning in clinical care. We have built models for predicting future increases in cost, identifying slow healing wounds, missed diagnoses of depression and for improving palliative care. Check out our Program for AI in Healthcare
We develop methods to analyze multiple datatypes for generating insights. Such as:
- Combining molecular data with EHR data to identify biomarkers for poor outcomes in fibrotic diseases
- Learning effective treatment pathways in Type 2 Diabetes with OHDSI using claims data from multiple countries.
- Unsupervised learning of reference intervals of laboratory tests from a clinical data warehouse, and monitoring Point-of-Care glucose meters using coincident testing with central laboratory measurements.
- Detecting skin adverse reactions by analyzing content in a health social network. Finding drug adverse events, and drug-drug interactions by analyzing using clinical notes; and developing weakly supervised methods for medical device surveillance.
- Mining Web search logs to predict health utilization, and analyzing information seeking behavior of health professionals.
- Inferring physical function from wearables data, and personalizing ICU alarm thresholds.
- Developing new methods for processing clinical textual documents
About us: Lab members
Internal (log in required): On boarding, Compute Resources, Lab communication, Projects, Rotations, For Collaborators, Archived pages
Teaching:
- BIOMEDIN 215, 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, 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, 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.
- AI in Healthcare Bootcamp, provides students an opportunity to do cutting-edge research at the intersection of AI and healthcare
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