This paper is in the following e-collection/theme issue:

Artificial Intelligence (1323) Clinical Informatics (986) Clinical Information and Decision Making (1373) Electronic Health Records (1031) Machine Learning (1433) Decision Support for Health Professionals (1162) Secondary Use of Clinical Data for Research and Surveillance (358) Cardiology (151)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54363, first published .
Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

Authors of this article:

Zhenyue Gao1 Author Orcid Image ;   Xiaoli Liu2 Author Orcid Image ;   Yu Kang3 Author Orcid Image ;   Pan Hu2 Author Orcid Image ;   Xiu Zhang3 Author Orcid Image ;   Wei Yan2 Author Orcid Image ;   Muyang Yan2 Author Orcid Image ;   Pengming Yu3 Author Orcid Image ;   Qing Zhang3 Author Orcid Image ;   Wendong Xiao1 Author Orcid Image ;   Zhengbo Zhang2 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Journals

  1. Guo Y, Yu F, Jiang F, Yin S, Jiang M, Li Y, Yang H, Chen L, Cai W, He G. Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period. Journal of Translational Medicine 2024;22(1) View