ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Learning Doctors' Medicine Prescription Pattern for Chronic Disease Treatment by Mining Electronic Health Records: A Multi-Task Learning Approach.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma.Journal of translational medicine · 2022Article
- Machine learning in patient flow: a review.Progress in biomedical engineering (Bristol, England) · 2021Review
- Microbial source tracking using metagenomics and other new technologies.Journal of microbiology (Seoul, Korea) · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Increasing learning ability from massive medical data and building learning methods robust to data quality issues are key factors toward building data-driven clinical decision support systems for medicine prescription decision support. Here, we attempted accordingly to address the factors using a multi-task neural network approach, benefiting from multi-task learning's advantage in modeling commonalities to increase learning performance and neural network's robustness to imprecise data. By mining electronic health record data, we learned medicine prescription patterns of multiple correlated antidiabetic agents in blood glucose control and antihypertensive drugs in blood pressure control scenarios. We achieved AUC increases of 0.02 to 0.06 in single drug prescription and an accuracy increase of 0.05 in prescription pattern prediction compared to logistic regression, demonstrating the efficacy of multi-task neural network approach in learning medicine prescription patterns.
Indexed as
Identifiers
29854254PMC5977645What OpenQuestion holds
Registered trials
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