Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data.

ABSTRACT:

Background

Oncologists use patients' life expectancy to guide decisions and may benefit from a tool that accurately predicts prognosis. Existing prognostic models generally use only a few predictor variables. We used an electronic medical record dataset to train a prognostic model for patients with metastatic cancer.

Methods

The model was trained and tested using 12 588 patients treated for metastatic cancer in the Stanford Health Care system from 2008 to 2017. Data sources included provider note text, labs, vital signs, procedures, medication orders, and diagnosis codes. Patients were divided randomly into a training set used to fit the model coefficients and a test set used to evaluate model performance (80%/20% split). A regularized Cox model with 4126 predictor variables

SUBMITTER: Gensheimer MF 

PROVIDER: S-EPMC6579743 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature