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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 normal from the latest lab test). We reported area under the receiver operating characteristic curve (AUC) for predicting normal/abnormal, evaluated accuracy and absolute bias on prediction vs. observation against lab test reduction proportion. We compared our model against baseline models and analyzed the impact of variations on the recommended reduction strategy. RESULTS:Our best model offered a 20.26 % reduction in the number of laboratory tests. By applying the recommended reduction policy on the hold-out dataset (7755 patients), our model predicted normality/abnormality of laboratory tests with a 98.27 % accuracy (AUC, 0.9885; sensitivity, 97.84 %; specificity, 98.80 %; PPV, 99.01 %; NPV, 97.39 %) on 20.26 % reduced lab tests, and recommended 98.10 % of transitions to be checked. Our model performed better than the greedy models, and the recommended reduction strategy was robust. DISCUSSION:Strong spatial and temporal correlations between laboratory tests can be used to optimize policies for reducing laboratory tests throughout the hospital course. Our method allows for iterative predictions and provides a superior solution for the dynamic decision-making laboratory reduction problem. CONCLUSION:This work demonstrates a machine-learning model that assists physicians in determining which laboratory tests may be omitted.

journal_name

Int J Med Inform

journal_title

International journal of medical informatics

authors

Yu L,Li L,Bernstam E,Jiang X

doi

10.1016/j.ijmedinf.2020.104282

subject

Has Abstract

pub_date

2020-12-01 00:00:00

pages

104282

eissn

1386-5056

issn

1872-8243

pii

S1386-5056(20)30598-0

journal_volume

144

pub_type

杂志文章

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