iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients

Abstract Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of...

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Autores principales:	Jun Wang, Chen Liu, Jingwen Li, Cheng Yuan, Lichi Zhang, Cheng Jin, Jianwei Xu, Yaqi Wang, Yaofeng Wen, Hongbing Lu, Biao Li, Chang Chen, Xiangdong Li, Dinggang Shen, Dahong Qian, Jian Wang
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/a6e6a3508738431586a63408365d0e7e
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spelling	oai:doaj.org-article:a6e6a3508738431586a63408365d0e7e2021-12-02T15:10:53ZiCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients10.1038/s41746-021-00496-32398-6352https://doaj.org/article/a6e6a3508738431586a63408365d0e7e2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00496-3https://doaj.org/toc/2398-6352Abstract Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions.Jun WangChen LiuJingwen LiCheng YuanLichi ZhangCheng JinJianwei XuYaqi WangYaofeng WenHongbing LuBiao LiChang ChenXiangdong LiDinggang ShenDahong QianJian WangNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-13 (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 Jun Wang Chen Liu Jingwen Li Cheng Yuan Lichi Zhang Cheng Jin Jianwei Xu Yaqi Wang Yaofeng Wen Hongbing Lu Biao Li Chang Chen Xiangdong Li Dinggang Shen Dahong Qian Jian Wang iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients
description	Abstract Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions.
format	article
author	Jun Wang Chen Liu Jingwen Li Cheng Yuan Lichi Zhang Cheng Jin Jianwei Xu Yaqi Wang Yaofeng Wen Hongbing Lu Biao Li Chang Chen Xiangdong Li Dinggang Shen Dahong Qian Jian Wang
author_facet	Jun Wang Chen Liu Jingwen Li Cheng Yuan Lichi Zhang Cheng Jin Jianwei Xu Yaqi Wang Yaofeng Wen Hongbing Lu Biao Li Chang Chen Xiangdong Li Dinggang Shen Dahong Qian Jian Wang
author_sort	Jun Wang
title	iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients
title_short	iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients
title_full	iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients
title_fullStr	iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients
title_full_unstemmed	iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients
title_sort	icovid: interpretable deep learning framework for early recovery-time prediction of covid-19 patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a6e6a3508738431586a63408365d0e7e
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iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients

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