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Description

Abstract: Using electronic medical records from a random sample of 30,000 sepsis patients, we identified medications administered within the first 24 hours of hospitalization. We applied Latent Dirichlet Allocation to generate 10 topics based on medication co-occurrence and frequency. Adding medication topic composition to a logistic regression model of hospital mortality improved the c-statistic from 0.81 to 0.83 (p<0.01), explaining 23% of variability. Topic modeling using detailed EMR data identified distinct clinical phenotypes of sepsis.

Learning Objective 1: Apply the unsupervised topic modeling method Latent Dirichlet Allocation to detailed electronic medical record data to identify distinct clinical phenotypes among patients with similar diagnoses.

Authors:

Alison Fohner (Presenter)
Kaiser Permanente

John Greene, Kaiser Permanente
Jonathan Chen, Stanford University
Gabriel Escobar, Kaiser Permanente
Vincent Liu, Kaiser Permanente

Presentation Materials:

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