期刊
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
卷 23, 期 4, 页码 766-772出版社
OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocw041
关键词
precision medicine; disease-mutation relationship; automated extraction; machine learning; text mining; breast cancer; prostate cancer
类别
资金
- NIH Intramural Research Program
- National Library of Medicine
- NIH Medical Research Scholars Program
- NIH
Objective Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine. Materials and Methods We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about mutation-disease associations. The model was subsequently used to extract disease-related mutations from larger biomedical literature corpora. Results The performance of the proposed approach was assessed using a benchmarking dataset. Results show that our proposed approach gains significant improvement over the previous state of the art and obtains F-measures of 0.880 and 0.845 for prostate and breast cancer mutations, respectively. Discussion To demonstrate its utility, we applied our approach to all abstracts in PubMed for 3 diseases (including a non-cancer disease). The mutations extracted were then manually validated against human-curated databases. The validation results show that the proposed approach is useful in a real-world setting to extract uncurated disease mutations from the biomedical literature. Conclusions The proposed approach improves the state of the art for mutation-disease extraction from text. It is scalable and generalizable to identify mutations for any disease at a PubMed scale.
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