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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, and also study 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, learning effective treatment pathways in Type 2 Diabetes with OHDSI using claims data from multiple countries. Learning reference intervals of laboratory tests from a clinical data warehouse. 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 using using clinical notes. Mining user 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. Assessing impact of informatics tools and databases, and profiling research on gun violence.
About us: Lab members
Internal (log in required): New Lab members, Lab information, Lab communication, Projects, Rotations, For Collaborators, Archived pages
Teaching: BIOMEDIN 215 Autumn quarter of each year, AI in Healthcare Bootcamp
Talks and videos:
2 min overview of our work in supporting clinical decision making at the bedside.
1 min video on the Informatics Consult Service.
Equitable and Fair Use of ML in Healthcare, at AIMiE 2018
5 min clip on how we need to improve the quality of the inputs to our COVID-19 models.
A talk about Stanford's data science efforts at COVID-19 and AI: A Virtual Conference by Stanford HAI.
