Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records.

Journal: STAR protocols
Published Date: Sep 17, 2021

Abstract

The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients. Here, we present Timesias, a machine learning pipeline which predicts outcomes from real-time sequential clinical data. The strategy implemented in Timesias is the first-place solution in the crowd-sourcing DII (discover, innovate, impact) National Data Science Challenge involving more than 100,000 patients, achieving 0.85 as evaluated by AUROC (area under receiver operator characteristic curve) in predicting the early onset of sepsis status. Timesias is freely available via PyPI and GitHub. For complete details on the use and execution of this protocol, please refer to Guan et al. (2021).

Authors

  • Hanrui Zhang
    Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
  • Daiyao Yi
    Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
  • Yuanfang Guan
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. gyuanfan@umich.edu.

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