Medical knowledge infused convolutional neural networks for cohort selection in clinical trials.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Nov 1, 2019

Abstract

OBJECTIVE: In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data representation model entitled medical knowledge-infused convolutional neural network (MKCNN), which is used for learning the clinical trial criteria eligibility status of patients to participate in cohort studies.

Authors

Chi-Jen Chen

Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
Neha Warikoo

Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.
Yung-Chun Chang

Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
Jin-Hua Chen

Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
Wen-Lian Hsu

Institute of Information Science, Academia Sinica, Taiwan.

Keywords

Clinical Trials as Topic Data Mining Deep Learning Humans Natural Language Processing Neural Networks, Computer Patient Selection Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (31390470)

Medical knowledge infused convolutional neural networks for cohort selection in clinical trials.

Abstract

Authors

Keywords

External Resources

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