A deep learning solution to recommend laboratory reduction strategies in ICU.

ABSTRACT:

Objective

To build a machine-learning model that predicts laboratory test results and provides a promising lab test reduction strategy, using spatial-temporal correlations.

Materials and methods

We developed a global prediction model to treat laboratory testing as a series of decisions by considering contextual information over time and across modalities. We validated our method using a critical care database (MIMIC III), which includes 4,570,709 observations of 12 standard laboratory tests, among 38,773 critical care patients. Our deep-learning model made real-time laboratory reduction recommendations and predicted the properties of lab tests, including values, normal/abnormal (whether labs were within the normal range) and transition (normal to abnormal or abnormal to norm

SUBMITTER: Yu L 

PROVIDER: S-EPMC10777357 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature