iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients.
NPJ Digit Med
; 4(1): 124, 2021 Aug 16.
Article
in English
| MEDLINE | ID: covidwho-1360212
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
Language:
English
Journal:
NPJ Digit Med
Year:
2021
Document Type:
Article
Affiliation country:
S41746-021-00496-3
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