MTLNFM: A Multi-Task Framework Using Neural Factorization Machines to Predict Patient Clinical Outcomes

MTLNFM: A Multi-Task Framework Using Neural Factorization Machines to Predict Patient Clinical Outcomes

Accurately predicting patient clinical outcomes is a complex task that requires integrating diverse factors, including individual characteristics, treatment histories, and environmental influences. This challenge is further exacerbated by missing data and inconsistent data quality, which often hinde...

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Bibliographic Details
Main Authors: Rui Yin, Jiaxin Li, Qiang Yang, Xiangyu Chen, Xiang Zhang, Mingquan Lin, Jiang Bian, Ashwin Subramaniam
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Applied Sciences
Subjects:
multi-task learning
neural factorization machine
machine learning
clinical outcome prediction
Online Access:https://www.mdpi.com/2076-3417/15/15/8733
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