Reversing aging to treat disease

Reversing aging to treat disease

Advised by Jonathan Chen.Member of the Stanford Health Care Data Science team lead by Nigam Shah.Built DEPLOYR, a technical framework for integrating custom researcher developed clinical prediction models directly into Stanford Health Care's production electronic medical record instance.Developed prospective monitoring strategies for deployed clinical prediction models that take into consideration feedback effects, predictions that in production affect outcomes they intend to predict.Assessed the impact of label selection (censoring) on the evaluation of clinical prediction models and the ability of inverse probability weighted estimators of performance to recover unbiased estimates.Developed and retrospectively evaluated antibiotic susceptibility prediction models to guide stewardly antibiotic selection.Supported in the development of an emergency department triaging model used to assign patients to appropriate levels of care.Supported in the development of a clinical foundation model capable of embedding complex and heterogeneous electronic medical record data into feature representations that improve sample efficiency over common count based representations.Supported in the development of machine learning models trained on crowd sourced video annotations for rapid and accurate diagnosis of autism spectrum disorder.

BIOMEDIN 215: Data Science for Medicine (with Nigam Shah)

Pushing the boundaries of AI/ML in health

BIOMEDIN 215: Data Science for Medicine (with Nigam Shah)

* Brain imaging research at the Imaging Genetics Center* Diffusion MRI / Tractography* Neurodegenerative disease

Brain imaging research at the Imaging Genetics Center