Li Wang1,2, Yaoyun Zhang2, Min Jiang2, Jingqi Wang2, Jiancheng Dong1, Yun Liu3,4, Cui Tao2, Guoqian Jiang5, Yi Zhou6, Hua Xu2. 1. Department of Medical Informatics, Medical School, Nantong University, Nantong, Jiangsu 226001, China. 2. School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA. 3. Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, 210029, China. 4. Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China. 5. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA. 6. Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
Abstract
Objective: In recent years, electronic health record systems have been widely implemented in China, making clinical data available electronically. However, little effort has been devoted to making drug information exchangeable among these systems. This study aimed to build a Normalized Chinese Clinical Drug (NCCD) knowledge base, by applying and extending the information model of RxNorm to Chinese clinical drugs. Methods: Chinese drugs were collected from 4 major resources-China Food and Drug Administration, China Health Insurance Systems, Hospital Pharmacy Systems, and China Pharmacopoeia-for integration and normalization in NCCD. Chemical drugs were normalized using the information model in RxNorm without much change. Chinese patent drugs (i.e., Chinese herbal extracts), however, were represented using an expanded RxNorm model to incorporate the unique characteristics of these drugs. A hybrid approach combining automated natural language processing technologies and manual review by domain experts was then applied to drug attribute extraction, normalization, and further generation of drug names at different specification levels. Lastly, we reported the statistics of NCCD, as well as the evaluation results using several sets of randomly selected Chinese drugs. Results: The current version of NCCD contains 16 976 chemical drugs and 2663 Chinese patent medicines, resulting in 19 639 clinical drugs, 250 267 unique concepts, and 2 602 760 relations. By manual review of 1700 chemical drugs and 250 Chinese patent drugs randomly selected from NCCD (about 10%), we showed that the hybrid approach could achieve an accuracy of 98.60% for drug name extraction and normalization. Using a collection of 500 chemical drugs and 500 Chinese patent drugs from other resources, we showed that NCCD achieved coverages of 97.0% and 90.0% for chemical drugs and Chinese patent drugs, respectively. Conclusion: Evaluation results demonstrated the potential to improve interoperability across various electronic drug systems in China.
Objective: In recent years, electronic health record systems have been widely implemented in China, making clinical data available electronically. However, little effort has been devoted to making drug information exchangeable among these systems. This study aimed to build a Normalized Chinese Clinical Drug (NCCD) knowledge base, by applying and extending the information model of RxNorm to Chinese clinical drugs. Methods: Chinese drugs were collected from 4 major resources-China Food and Drug Administration, China Health Insurance Systems, Hospital Pharmacy Systems, and China Pharmacopoeia-for integration and normalization in NCCD. Chemical drugs were normalized using the information model in RxNorm without much change. Chinese patent drugs (i.e., Chinese herbal extracts), however, were represented using an expanded RxNorm model to incorporate the unique characteristics of these drugs. A hybrid approach combining automated natural language processing technologies and manual review by domain experts was then applied to drug attribute extraction, normalization, and further generation of drug names at different specification levels. Lastly, we reported the statistics of NCCD, as well as the evaluation results using several sets of randomly selected Chinese drugs. Results: The current version of NCCD contains 16 976 chemical drugs and 2663 Chinese patent medicines, resulting in 19 639 clinical drugs, 250 267 unique concepts, and 2 602 760 relations. By manual review of 1700 chemical drugs and 250 Chinese patent drugs randomly selected from NCCD (about 10%), we showed that the hybrid approach could achieve an accuracy of 98.60% for drug name extraction and normalization. Using a collection of 500 chemical drugs and 500 Chinese patent drugs from other resources, we showed that NCCD achieved coverages of 97.0% and 90.0% for chemical drugs and Chinese patent drugs, respectively. Conclusion: Evaluation results demonstrated the potential to improve interoperability across various electronic drug systems in China.
Authors: Ajit A Dhavle; Stacy Ward-Charlerie; Michael T Rupp; John Kilbourne; Vishal P Amin; Joshua Ruiz Journal: J Am Med Inform Assoc Date: 2015-10-28 Impact factor: 4.497
Authors: Matvey B Palchuk; Michael Klumpenaar; Tarang Jatkar; Ralph J Zottola; William G Adams; Aaron H Abend Journal: AMIA Annu Symp Proc Date: 2010-11-13
Authors: George Hripcsak; Jon D Duke; Nigam H Shah; Christian G Reich; Vojtech Huser; Martijn J Schuemie; Marc A Suchard; Rae Woong Park; Ian Chi Kei Wong; Peter R Rijnbeek; Johan van der Lei; Nicole Pratt; G Niklas Norén; Yu-Chuan Li; Paul E Stang; David Madigan; Patrick B Ryan Journal: Stud Health Technol Inform Date: 2015