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Characterizing and Predicting Adverse Drug Events Outside the Hospital

This study aims to employ machine learning methods to develop predictive models for adverse drug events, or ADEs, that result in patients using the emergency department and/or being hospitalized (non-nosocomial ADEs, NNADEs). Based on work with a pharmacist expert panel that will include examination of literature as well as KPNC ADE data, six candidate drugs that are currently not the focus of preventive efforts will be selected. A population database that will include geographic information system data as well as ADEs will then be built. Finally, using machine learning methods, the investigators will attempt to develop predictive models for ADEs involving the six selected candidate drugs.

Investigator: Escobar, Gabriel

Funder: Gordon and Betty Moore Foundation

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