GenHPF: General Healthcare Predictive Framework for Multi-Task Multi-Source Learning-Reference-Cited by-同舟云学术

GenHPF: General Healthcare Predictive Framework for Multi-Task Multi-Source Learning

Published:2024-01 Issue:1 Volume:28 Page:502-513
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Hur Kyunghoon¹^ORCID,Oh Jungwoo¹^ORCID,Kim Junu¹^ORCID,Kim Jiyoun¹^ORCID,Lee Min Jae¹^ORCID,Cho Eunbyeol¹^ORCID,Moon Seong-Eun²^ORCID,Kim Young-Hak³^ORCID,Atallah Louis⁴^ORCID,Choi Edward¹^ORCID

Affiliation:

1. Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

2. Naver AI Lab, Seongnam-si, South Korea

3. Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea

4. Philips Research, Cambridge, MA, USA

Funder

KAIST-NAVER Hyper-Creative AI Center, Institute of Information and Communications Technology Planning and Evaluation

National Research Foundation of Korea

Korea Medical Device Development Fund

Korea Health Industry Development Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10380963/10298642.pdf?arnumber=10298642

Reference46 articles.

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2. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach

3. Early prediction of septic shock in hospitalized patients

4. PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT

5. Readmission prediction using deep learning on electronic health records

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