Patient-specific COVID-19 resource utilization prediction using fusion AI model

Abstract The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based...

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Autores principales:	Amara Tariq, Leo Anthony Celi, Janice M. Newsome, Saptarshi Purkayastha, Neal Kumar Bhatia, Hari Trivedi, Judy Wawira Gichoya, Imon Banerjee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/36af332facc2437695b030d5732ceb23
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spelling	oai:doaj.org-article:36af332facc2437695b030d5732ceb232021-12-02T18:24:55ZPatient-specific COVID-19 resource utilization prediction using fusion AI model10.1038/s41746-021-00461-02398-6352https://doaj.org/article/36af332facc2437695b030d5732ceb232021-06-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00461-0https://doaj.org/toc/2398-6352Abstract The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1–86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.Amara TariqLeo Anthony CeliJanice M. NewsomeSaptarshi PurkayasthaNeal Kumar BhatiaHari TrivediJudy Wawira GichoyaImon BanerjeeNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Amara Tariq Leo Anthony Celi Janice M. Newsome Saptarshi Purkayastha Neal Kumar Bhatia Hari Trivedi Judy Wawira Gichoya Imon Banerjee Patient-specific COVID-19 resource utilization prediction using fusion AI model
description	Abstract The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1–86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.
format	article
author	Amara Tariq Leo Anthony Celi Janice M. Newsome Saptarshi Purkayastha Neal Kumar Bhatia Hari Trivedi Judy Wawira Gichoya Imon Banerjee
author_facet	Amara Tariq Leo Anthony Celi Janice M. Newsome Saptarshi Purkayastha Neal Kumar Bhatia Hari Trivedi Judy Wawira Gichoya Imon Banerjee
author_sort	Amara Tariq
title	Patient-specific COVID-19 resource utilization prediction using fusion AI model
title_short	Patient-specific COVID-19 resource utilization prediction using fusion AI model
title_full	Patient-specific COVID-19 resource utilization prediction using fusion AI model
title_fullStr	Patient-specific COVID-19 resource utilization prediction using fusion AI model
title_full_unstemmed	Patient-specific COVID-19 resource utilization prediction using fusion AI model
title_sort	patient-specific covid-19 resource utilization prediction using fusion ai model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/36af332facc2437695b030d5732ceb23
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_version_	1718378087602716672

Patient-specific COVID-19 resource utilization prediction using fusion AI model

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