Back
Top 0.4%
16.7%
Top 15%
11.2%
Top 0.4%
10.4%
Top 2%
8.5%
Top 0.8%
7.4%
Top 4%
6.1%
Top 3%
5.6%
#1
4.7%
Top 4%
4.7%
Top 2%
3.6%
Top 1%
2.6%
Top 90%
2.6%
Top 33%
2.6%
Top 3%
2.6%
Top 4%
1.3%
Top 3%
0.9%
Top 2%
0.9%
Top 15%
0.7%
MTLNFM: A Multi-task Framework Using Neural Factorization Machines to Predict Patient Clinical Outcomes
2025-05-28
health informatics
Title + abstract only
View on medRxiv
Show abstract
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 hinder the effectiveness of traditional single-task learning (STL) models. Multi-Task Learning (MTL) has emerged as a promising paradigm to address these limitations by jointly modeling related prediction ...
Predicted journal destinations
1
npj Digital Medicine
85 training papers
2
Scientific Reports
701 training papers
3
Journal of Biomedical Informatics
37 training papers
4
Journal of the American Medical Informatics Association
53 training papers
5
BMC Medical Informatics and Decision Making
36 training papers
6
PLOS Digital Health
88 training papers
7
JAMIA Open
35 training papers
8
Communications Medicine
63 training papers
9
Journal of Medical Internet Research
81 training papers
10
International Journal of Medical Informatics
25 training papers
11
JMIR Medical Informatics
16 training papers
12
PLOS ONE
1737 training papers
13
Nature Communications
483 training papers
14
Computers in Biology and Medicine
39 training papers
15
BMC Medical Research Methodology
41 training papers
16
Patterns
15 training papers
17
The Lancet Digital Health
25 training papers
18
PLOS Computational Biology
141 training papers