Using machine learning probabilities to identify effects of COVID-19

Vijendra Ramlall; Undina Gisladottir; Jenna Kefeli; Yutaro Tanaka; Benjamin May; Nicholas Tatonetti

doi:10.1016/j.patter.2023.100889

Using machine learning probabilities to identify effects of COVID-19

Patterns (N Y). 2023 Dec 1;4(12):100889. doi: 10.1016/j.patter.2023.100889. eCollection 2023 Dec 8.

Authors

Vijendra Ramlall^{1

2

3

4}, Undina Gisladottir^{1

2

3}, Jenna Kefeli², Yutaro Tanaka^{1

2

3

5}, Benjamin May⁶, Nicholas Tatonetti^{1

2

3

6

7}

Affiliations

¹ Department of Biomedical Informatics, Columbia University, Columbia University Irving Medical Center, New York, NY 10032, USA.
² Department of Systems Biology, Columbia University, Columbia University Irving Medical Center, New York, NY 10032, USA.
³ Department of Medicine, Columbia University, Columbia University Irving Medical Center, New York, NY 10032, USA.
⁴ Department of Physiology and Cellular Biophysics, Columbia University, Columbia University Irving Medical Center, New York, NY 10032, USA.
⁵ Department of Applied Physics and Applied Mathematics, Fu Foundation School of Engineering and Applied Sciences, Columbia University, New York, NY 10027, USA.
⁶ Herbert Irving Comprehensive Cancer Center, New York Presbyterian/Columbia University Irving Medical Center, New York, NY 10032, USA.
⁷ Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90069, USA.

Abstract

Coronavirus disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has had extensive economic, social, and public health impacts in the United States and around the world. To date, there have been more than 600 million reported infections worldwide with more than 6 million reported deaths. Retrospective analysis, which identified comorbidities, risk factors, and treatments, has underpinned the response. As the situation transitions to an endemic, retrospective analyses using electronic health records will be important to identify the long-term effects of COVID-19. However, these analyses can be complicated by incomplete records, which makes it difficult to differentiate visits where the patient had COVID-19. To address this issue, we trained a random Forest classifier to assign a probability of a patient having been diagnosed with COVID-19 during each visit. Using these probabilities, we found that higher COVID-19 probabilities were associated with a future diagnosis of myocardial infarction, urinary tract infection, acute renal failure, and type 2 diabetes.

Keywords: COVID-19 effects; algorithm development; clinical informatics; machine learning; survival analysis.

Abstract

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