Latent Semantic Analysis of Electronic Medical Record Text for Clinical Decision Making
Li Guolei1,Chen Xianlai1,2,3,Xia Dong4,Yang Rong5()
1Information Security and Big Data Research Institute, Central South University, Changsha 410013, China 2Key Laboratory of Medical Information Research (Central South University), College of Hunan Province, Changsha 410013, China 3Hunan Province Cooperative Innovation Center of Medical Big Data, Changsha 410013, China 4Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu 610041, China 5Xiangya Hospital, Central South University, Changsha 410078, China
[Objective] This study aims to extract knowledge for clinical decision from electronic medical records through semantic analysis. [Methods] We first extracted clinical terms from the training samples by the word segmentation algorithm with the help of custom dictionary and statistical method. Then, we used latent semantic analysis to find the potential correlations between clinical terms and treatment plans. Finally, we established a latent semantic model to support gastric cancer treatments. [Results] We successfully extracted 605 treatment plans from 1000 test samples based on the discharge summary texts. [Limitations] Only discharge record texts were examined for this study. [Conclusions] The latent semantic analysis could effectively process electronic medical records to assist doctors’ clinical decision-making work, which posed positive effects to the development of electronic medical record applications.
李国垒, 陈先来, 夏冬, 杨荣. 面向临床决策的电子病历文本潜在语义分析*[J]. 数据分析与知识发现, 2016, 32(3): 50-57.
Li Guolei, Chen Xianlai, Xia Dong, Yang Rong. Latent Semantic Analysis of Electronic Medical Record Text for Clinical Decision Making. Data Analysis and Knowledge Discovery, 2016, 32(3): 50-57.
Landauer T K.A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge[J]. Psychological Review, 1997, 104(2): 211-240.
[2]
Cohen T, Blatter B, Patel V.Simulating Expert Clinical Comprehension: Adapting Latent Semantic Analysis to Accurately Extract Clinical Concepts from Psychiatric Narrative[J]. Journal of Biomedical Informatics, 2008, 41(6): 1070-1087.
[3]
Cohen T, Blatter B, Patel V.Exploring Dangerous Neighborhoods: Latent Semantic Analysis and Computing Beyond the Bounds of the Familiar [C]. In: Proceedings of the Annual Symposium of American Medical Informatics Association. 2005: 151-155.
[4]
Ginter F, Suominen H, Pyysalo S, et al.Combining Hidden Markov Models and Latent Semantic Analysis for Topic Segmentation and Labeling: Method and Clinical Application[J]. International Journal of Medical Informatics, 2009, 78(12): 1-6.
[5]
Wild F, Haley D.Using Latent-Semantic Analysis and Network Analysis for Monitoring Conceptual Development[J]. Journal for Language Technology and Computational Linguistics, 2011, 26(1): 9-21.
[6]
Wang J, Sun X P, Nahavandi S, et al.Multichannel Biomedical Time Series Clustering via Hierarchical Probabilistic Latent Semantic Analysis[J]. Computer Methods and Programs in Biomedicine, 2014, 117(2): 238-246.
[7]
Abate F, Acquaviva A, Ficarra E, et al.A New Latent Semantic Analysis Based Methodology for Knowledge Extraction from Biomedical Literature and Biological Pathways Databases [C]. In: Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, Rome, Italy. 2011: 66-74.
(Gan Yanfang, Ni Ziwei, Lin Fan. The Application of LSA in Traditional Chinese Medicine Syndromes Classification [J]. Journal of Xiamen University: Natural Science, 2012, 51(6): 991-994).
(Lei Lei, Zhang Zaohua, Wen Xianrong, et al. Study on Application of Probability Latent Semantic Analysis (PLSA) in Herbal Prescription Development [J]. World Science and Technology (Modernization of Traditional Chinese Medicine and Materi Medica), 2012(5): 1976-1980).
(National Health and Family Planning Commission of the People’s Republic of China. Gastric Standardized Treatment Guidelines (Trial)[J]. Chinese Journal of the Frontiers of Medical Science (Electronic Version), 2013, 5(8): 29-36.)
[11]
王思力. 面向大规模信息检索的中文分词技术研究[D]. 北京: 中国科学院研究生院, 2006.
[11]
(Wang Sili.Research on Chinese Word Segmentation for Large Scale Information Retrieval [D]. Beijing: Graduate School of Chinese Academy of Sciences, 2006.)
[12]
Chung Y M, Lee J Y.A Corpus-based Approach to Comparative Evaluation of Statistical Term Association Measure[J]. Journal of the American Society for Information Science and Technology, 2001, 52(4): 283-296.
Bendersky M, Croft W B.Discovering Key Concepts in Verbose Queries [C]. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008: 491-498.
(Li Guolei, Chen Xianlai.The Application of Latent Semantic Analysis to Construction of Keyword-Descriptor Comparison System[J]. Information Studies: Theory & Application, 2014, 37(4): 127-130, 133.)
(Xia Dong, Xiao Xiaodan, Li Guolei, et al.Research on Correspondence Between Keyword and Chinese Library Classification Based on Latent Semantic Analysis[J]. New Technology of Library and Information Service, 2014(12): 92-96.)
