[HTML][HTML] Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records
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 …
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 …
Summary
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).
Elsevier
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