Analysis of original articles and case reports about “hemodialysis” in the Japan Medical Abstracts Society database by text-mining using artificial intelligence
Author:
Affiliation:
1. Department of Nephrology, Rheumatology, Endocrinology, and Metabolism;Okayama University Academic Field of Medicine, Dentistry, and Pharmaceutical Sciences
Publisher
Japanese Society for Dialysis Therapy
Subject
Organic Chemistry,Biochemistry
Link
https://www.jstage.jst.go.jp/article/jsdt/55/10/55_563/_pdf
Reference10 articles.
1. 1) Sakate R, Fukagawa A, Takagaki Y, et al. Trends of Clinical Trials for Drug Development in Rare Diseases. Curr Clin Pharmacol 2018;13:199-208.
2. 2) Van Haagen HHHBM, Hoen P, Morrée AD, et al. In silico discovery and experimental validation of new protein-protein interactions. Proteomics 2011;11:843-53.
3. 3) Davis AP, Wiegers TC, Roberts PM, et al. A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions. Database (Oxford) 2013;2013:bat080.
4. 4) Schönbach C, Nagashima T, Konagaya A. Textmining in support of knowledge discovery for vaccine development. Methods 2004;34:488-95.
5. 5) Okuhara T, Ishikawa H, Okada M, et al. Japanese anti- versus pro-influenza vaccination websites: a text-mining analysis. Health Promot Int 2019;34:552-66.
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