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Detecting Signals of Adverse Drug Reactions with Data from Online Health Community |
Guo Jinjing1,Xia Guanghui2(),Huang Qi1,He Liyun3,Zhang Huabing3 |
1School of Information Management, Nanjing University, Nanjing 210023, China 2Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China 3Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China |
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Abstract [Objective] Online health communities provide new information for detecting adverse drug reaction (ADR) signals. This study identifies ADR signals from patients’ reviews and generates early warnings for potential side-effects of antidiabetic drugs. [Methods] First, we retrieved patients’ reviews (adverse reactions) on antidiabetic drugs from Ask a Patient website. Then, we combined natural language processing techniques and lexicons (UMLS and MedDRA) to normalize and map these reviews. Third, we constructed a drug-ADR co-occurrence matrix and used the PRR method to identify drug-ADR pairs meeting the signal detection threshold. Finally, we invited expert to interpret the extracted results, which were evaluated with Drugs.com standards. [Results] A total of 539 drug-ADR pairs were identified, with an overall identification accuracy of 85% and recall of 82%. [Limitations] The accuracy of identifying ADR terms was affected by the inclusion of non-ADR terms, such as examination, surgical operation, and social environment from MedDRA. [Conclusions] The proposed model enriches the data sources and methods of ADR signal detection.
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Received: 04 November 2021
Published: 24 August 2022
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Fund:National Natural Science Foundation of China(91846106);Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(2019XK320029) |
Corresponding Authors:
Xia Guanghui,ORCID:0000-0003-4587-0344
E-mail: xiagh@imicams.ac.cn
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