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Open Access

Lesson Learned from COVID-19 Retrospective Study: An Entropy-Based Clinical-Interpretable Scorecard for Mortality Risk Control at ICU Admission

Laboratory of Network Information Security, Beihang University, Beijing 100191, China
Andrew and Erna Viterbi School of Engineering, University of Southern California, Los Angles, CA 90089, USA
Beijing Goodwill Information and Technology Co., Ltd., and Mobile Health Laboratory, Peking University, Beijing 100871, China
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Abstract

With severe acute respiratory syndrome coronavirus 2 spreading globally and causing 2019 coronavirus disease (COVID-19), a challenge that we unprepared for was about how to optimally plan and distribute limited top-medical resources for patients in need of urgent care. To address this challenge, physicians desperately needed a scientific tool to methodically differentiate between cases with varying severity. In this study, the unique data of COVID-19 intensive care unit (ICU) patients provided by the national medical team in Wuhan were classified into discrete and continuous variable types. All continuous data were discretized using an entropy-based method and transformed into serial information margins, in which each information margin is related to a specific symptom or clinical meaning. Finally, all these native and processed discrete data were used to configure a readable scorecard through logistic regression, which is the desired scientific tool aforementioned. A total of 322 ICU patients (age: [median: 64, interquartile range: 54–75], males: 178 [55.28%], and death: 72 [22.36%]) were included in the study. Probabilities of mortality in COVID-19 patients can be evaluated using a scorecard model (calibration slope: 1.343, Brier: 0.048, Dxy = 0.972, and population stability index = 0.071), with desired model performances (accuracy = 0.948, area under curve = 0.99, sensitivity = 1, and specificity = 0.939). This new model can interpret clinical meanings from complex data, and compare it with existing machine learning methods through a black-box mechanism. This new data-information model answers a critical question of how a computing algorithm produces clinically meaningful results that will help physicians logically allocate medical resources for COVID-19 patients. Notably, this tool has limitations, giving that this research is a retrospective study. Hopefully, this tool will be tested further and optimized for adaptation to similar clinical cases in the future.

Keywords

COVID-19scorecardclinical-interpretablemachine learningICU admission control

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Tsinghua Science and Technology
Volume 29 Issue 1,
February 2024
Pages 34-45
DOI: 10.26599/TST.2023.9010042
Cite this article:
Yao C, Huangqi C, Huang A. Lesson Learned from COVID-19 Retrospective Study: An Entropy-Based Clinical-Interpretable Scorecard for Mortality Risk Control at ICU Admission. Tsinghua Science and Technology, 2024, 29(1): 34-45. https://doi.org/10.26599/TST.2023.9010042

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Received: 27 November 2022
Revised: 27 March 2023
Accepted: 09 May 2023
Published: 21 August 2023
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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