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Sim JA, Huang X, Horan MR, Stewart CM, Robison LL, Hudson MM, Baker JN, Huang IC. Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. Artif Intell Med 2023; 146:102701. [PMID: 38042599 PMCID: PMC10693655 DOI: 10.1016/j.artmed.2023.102701] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 09/30/2023] [Accepted: 10/29/2023] [Indexed: 12/04/2023]
Abstract
OBJECTIVE Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care. METHODS We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs. RESULTS Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP. CONCLUSIONS This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.
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Affiliation(s)
- Jin-Ah Sim
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States; School of AI Convergence, Hallym University, Chuncheon, Republic of Korea
| | - Xiaolei Huang
- Department of Computer Science, University of Memphis, Memphis, TN, United States
| | - Madeline R Horan
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Christopher M Stewart
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, United States
| | - Leslie L Robison
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Melissa M Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States; Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Justin N Baker
- Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - I-Chan Huang
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States.
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Chang H, Zan H, Zhang S, Zhao B, Zhang K. Construction of cardiovascular information extraction corpus based on electronic medical records. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:13379-13397. [PMID: 37501492 DOI: 10.3934/mbe.2023596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Cardiovascular disease has a significant impact on both society and patients, making it necessary to conduct knowledge-based research such as research that utilizes knowledge graphs and automated question answering. However, the existing research on corpus construction for cardiovascular disease is relatively limited, which has hindered further knowledge-based research on this disease. Electronic medical records contain patient data that span the entire diagnosis and treatment process and include a large amount of reliable medical information. Therefore, we collected electronic medical record data related to cardiovascular disease, combined the data with relevant work experience and developed a standard for labeling cardiovascular electronic medical record entities and entity relations. By building a sentence-level labeling result dictionary through the use of a rule-based semi-automatic method, a cardiovascular electronic medical record entity and entity relationship labeling corpus (CVDEMRC) was constructed. The CVDEMRC contains 7691 entities and 11,185 entity relation triples, and the results of consistency examination were 93.51% and 84.02% for entities and entity-relationship annotations, respectively, demonstrating good consistency results. The CVDEMRC constructed in this study is expected to provide a database for information extraction research related to cardiovascular diseases.
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Affiliation(s)
- Hongyang Chang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Hongying Zan
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
- Peng Cheng Laboratory, Shenzhen, China
| | - Shuai Zhang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Bingfei Zhao
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Kunli Zhang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
- Peng Cheng Laboratory, Shenzhen, China
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Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:1679589. [PMID: 35600940 PMCID: PMC9122692 DOI: 10.1155/2022/1679589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/31/2022] [Accepted: 04/06/2022] [Indexed: 12/12/2022]
Abstract
Background The practice of traditional Chinese medicine (TCM) began several thousand years ago, and the knowledge of practitioners is recorded in paper and electronic versions of case notes, manuscripts, and books in multiple languages. Developing a method of information extraction (IE) from these sources to generate a cohesive data set would be a great contribution to the medical field. The goal of this study was to perform a systematic review of the status of IE from TCM sources over the last 10 years. Methods We conducted a search of four literature databases for articles published from 2010 to 2021 that focused on the use of natural language processing (NLP) methods to extract information from unstructured TCM text data. Two reviewers and one adjudicator contributed to article search, article selection, data extraction, and synthesis processes. Results We retrieved 1234 records, 49 of which met our inclusion criteria. We used the articles to (i) assess the key tasks of IE in the TCM domain, (ii) summarize the challenges to extracting information from TCM text data, and (iii) identify effective frameworks, models, and key findings of TCM IE through classification. Conclusions Our analysis showed that IE from TCM text data has improved over the past decade. However, the extraction of TCM text still faces some challenges involving the lack of gold standard corpora, nonstandardized expressions, and multiple types of relations. In the future, IE work should be promoted by extracting more existing entities and relations, constructing gold standard data sets, and exploring IE methods based on a small amount of labeled data. Furthermore, fine-grained and interpretable IE technologies are necessary for further exploration.
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Privacy-Preserving Mimic Models for clinical Named Entity Recognition in French. J Biomed Inform 2022; 130:104073. [DOI: 10.1016/j.jbi.2022.104073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/09/2022] [Accepted: 04/07/2022] [Indexed: 11/18/2022]
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Chu H, Moon S, Park J, Bak S, Ko Y, Youn BY. The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review. Front Pharmacol 2022; 13:826044. [PMID: 35431917 PMCID: PMC9011141 DOI: 10.3389/fphar.2022.826044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/01/2022] [Indexed: 01/04/2023] Open
Abstract
Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status. Objective: To categorize and seek the current usage of AI in CAM. Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies. Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians. Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.
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Affiliation(s)
- Hongmin Chu
- Daecheong Public Health Subcenter, Incheon, South Korea
| | - Seunghwan Moon
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Jeongsu Park
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Seongjun Bak
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Youme Ko
- National Institute for Korean Medicine Development (NIKOM), Seoul, South Korea
| | - Bo-Young Youn
- Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul, South Korea
- *Correspondence: Bo-Young Youn,
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A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11188319] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.
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Jia Q, Zhang D, Xu H, Xie Y. Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision. JMIR Med Inform 2021; 9:e28219. [PMID: 34125076 PMCID: PMC8240806 DOI: 10.2196/28219] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/14/2021] [Accepted: 04/19/2021] [Indexed: 11/23/2022] Open
Abstract
Background Traditional Chinese medicine (TCM) clinical records contain the symptoms of patients, diagnoses, and subsequent treatment of doctors. These records are important resources for research and analysis of TCM diagnosis knowledge. However, most of TCM clinical records are unstructured text. Therefore, a method to automatically extract medical entities from TCM clinical records is indispensable. Objective Training a medical entity extracting model needs a large number of annotated corpus. The cost of annotated corpus is very high and there is a lack of gold-standard data sets for supervised learning methods. Therefore, we utilized distantly supervised named entity recognition (NER) to respond to the challenge. Methods We propose a span-level distantly supervised NER approach to extract TCM medical entity. It utilizes the pretrained language model and a simple multilayer neural network as classifier to detect and classify entity. We also designed a negative sampling strategy for the span-level model. The strategy randomly selects negative samples in every epoch and filters the possible false-negative samples periodically. It reduces the bad influence from the false-negative samples. Results We compare our methods with other baseline methods to illustrate the effectiveness of our method on a gold-standard data set. The F1 score of our method is 77.34 and it remarkably outperforms the other baselines. Conclusions We developed a distantly supervised NER approach to extract medical entity from TCM clinical records. We estimated our approach on a TCM clinical record data set. Our experimental results indicate that the proposed approach achieves a better performance than other baselines.
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Affiliation(s)
- Qi Jia
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.,Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China
| | - Dezheng Zhang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.,Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China
| | - Haifeng Xu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.,Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China
| | - Yonghong Xie
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.,Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China
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Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8829219. [PMID: 33299537 PMCID: PMC7707942 DOI: 10.1155/2020/8829219] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/26/2020] [Accepted: 11/02/2020] [Indexed: 12/19/2022]
Abstract
Background Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious missing sentence components, and unclear entity boundary. Moreover, the corpus of Chinese electronic medical records is difficult to obtain. Methods Aiming at these characteristics of Chinese electronic medical records, this study proposed a Chinese clinical entity recognition model based on deep learning pretraining. The model used word embedding from domain corpus and fine-tuning of entity recognition model pretrained by relevant corpus. Then BiLSTM and Transformer are, respectively, used as feature extractors to identify four types of clinical entities including diseases, symptoms, drugs, and operations from the text of Chinese electronic medical records. Results 75.06% Macro-P, 76.40% Macro-R, and 75.72% Macro-F1 aiming at test dataset could be achieved. These experiments show that the Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition effect. Conclusions These experiments show that the proposed Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition performance.
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Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine. BMC Med Inform Decis Mak 2020; 20:64. [PMID: 32252745 PMCID: PMC7132896 DOI: 10.1186/s12911-020-1079-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 03/25/2020] [Indexed: 01/04/2023] Open
Abstract
Background In this study, we focus on building a fine-grained entity annotation corpus with the corresponding annotation guideline of traditional Chinese medicine (TCM) clinical records. Our aim is to provide a basis for the fine-grained corpus construction of TCM clinical records in future. Methods We developed a four-step approach that is suitable for the construction of TCM medical records in our corpus. First, we determined the entity types included in this study through sample annotation. Then, we drafted a fine-grained annotation guideline by summarizing the characteristics of the dataset and referring to some existing guidelines. We iteratively updated the guidelines until the inter-annotator agreement (IAA) exceeded a Cohen’s kappa value of 0.9. Comprehensive annotations were performed while keeping the IAA value above 0.9. Results We annotated the 10,197 clinical records in five rounds. Four entity categories involving 13 entity types were employed. The final fine-grained annotated entity corpus consists of 1104 entities and 67,799 tokens. The final IAAs are 0.936 on average (for three annotators), indicating that the fine-grained entity recognition corpus is of high quality. Conclusions These results will provide a foundation for future research on corpus construction and named entity recognition tasks in the TCM clinical domain.
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Comparing Different Methods for Named Entity Recognition in Portuguese Neurology Text. J Med Syst 2020; 44:77. [PMID: 32112285 DOI: 10.1007/s10916-020-1542-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 02/07/2020] [Indexed: 10/24/2022]
Abstract
Electronic Medical Records (EMRs) are written in an unstructured way, often using natural language. Information Extraction (IE) may be used for acquiring knowledge from such texts, including the automatic recognition of meaningful entities, through models for Named Entity Recognition (NER). However, while most work on the previous was made for English, this experience aimed at testing different methods in Portuguese text, more precisely, on the domain of Neurology, and take some conclusions. This paper comprised the comparison between Conditional Random Fields (CRF), bidirectional Long Short-term Memory - Conditional Random Fields (BiLSTM-CRF) and a BiLSTM-CRF with residual learning connections, using not only Portuguese texts from medical journals but also texts from the Coimbra Hospital and Universitary Centre (CHUC) Neurology Service. Furthermore, the performances of BiLSTM-CRF models using word embeddings (WEs) trained with clinical text and WEs trained with general language texts were compared. Deep learning models achieved F1-Scores of nearly 83% and 75%, respectively for relaxed and strict evaluation, on texts extracted from the medical journal. For texts collected from the Hospital, the same achieved F1-Scores of nearly 71% and 62%. This work concludes that deep learning models outperform the shallow learning models and that in-domain WEs get better results than general language WEs, even when the latter are trained with much more text than the former. Furthermore, the results show that it is possible to extract information from Hospital clinical texts with models trained with clinical cases extracted from medical journals, and thus openly available. Nevertheless, such results still require a healthcare technician to check if the information is well extracted.
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Hu Q, Yu T, Li J, Yu Q, Zhu L, Gu Y. End-to-End syndrome differentiation of Yin deficiency and Yang deficiency in traditional Chinese medicine. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 174:9-15. [PMID: 30376987 DOI: 10.1016/j.cmpb.2018.10.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Revised: 09/22/2018] [Accepted: 10/11/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Yin and Yang, two concepts adapted from classical Chinese philosophy, play a diagnostic role in Traditional Chinese Medicine (TCM). The Yin and Yang in harmonious balance indicate health, whereas imbalances to either side indicate unhealthiness, which may result in diseases. Yin-yang disharmony is considered to be the cause of pathological changes. Syndrome differentiation of yin-yang is crucial to clinical diagnosis. It lays a foundation for subsequent medical judgments, including therapeutic methods, and formula, among many others. However, because of the complexities of the mechanisms and manifestations of disease, it is difficult to exactly point out which one, yin or yang, is disharmonious. There has been inadequate research conducted on syndrome differentiation of yin and yang from a computational perspective. In this study, we present a computational method, viz. an end-to-end syndrome differentiation of yin deficiency and yang deficiency. METHODS Unlike most previous studies on syndrome differentiation, which use structured datasets, this study takes unstructured texts in medical records as its inputs. It models syndrome differentiation as a task of text classification. This study experiments on two state-of-the-art end-to-end algorithms for text classification, i.e. a classic convolutional neural network (CNN) and fastText. These two systems take the n-grams of several types of tokens as their inputs, including characters, terms, and words. RESULTS When evaluated on a data set with 7326 modern medical records in TCM, it is observed that CNN and fastText generally give rise to comparable performances. The best accuracy rate of 92.55% comes from the system taking inputs as raw as n-grams of characters. It implies that one can build at least a moderate system for the differentiation of yin deficiency and yang deficiency even if he has no glossary or tokenizer at hand. CONCLUSIONS This study has demonstrated the feasibility of using end-to-end text classification algorithms to differentiate yin deficiency and yang deficiency on unstructured medical records.
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Affiliation(s)
- Qinan Hu
- Institute of Linguistics, Chinese Academy of Social Sciences, Beijing 100732, China; China Multilingual and Multimodal Corpora and Big Data Research Centre, Beijing 100089, China.
| | - Tong Yu
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Jinghua Li
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Qi Yu
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Ling Zhu
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Yueguo Gu
- Institute of Linguistics, Chinese Academy of Social Sciences, Beijing 100732, China; China Multilingual and Multimodal Corpora and Big Data Research Centre, Beijing 100089, China.
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Lopes F, Teixeira C, Gonçalo Oliveira H. Named Entity Recognition in Portuguese Neurology Text Using CRF. PROGRESS IN ARTIFICIAL INTELLIGENCE 2019. [DOI: 10.1007/978-3-030-30241-2_29] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:2548537. [PMID: 29849994 PMCID: PMC5932451 DOI: 10.1155/2018/2548537] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 01/02/2018] [Indexed: 11/26/2022]
Abstract
Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking.
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Su J, He B, Guan Y, Jiang J, Yang J. Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records. BMC Med Inform Decis Mak 2017; 17:117. [PMID: 28789686 PMCID: PMC5549299 DOI: 10.1186/s12911-017-0512-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 07/31/2017] [Indexed: 12/16/2022] Open
Abstract
Background Cardiovascular disease (CVD) has become the leading cause of death in China, and most of the cases can be prevented by controlling risk factors. The goal of this study was to build a corpus of CVD risk factor annotations based on Chinese electronic medical records (CEMRs). This corpus is intended to be used to develop a risk factor information extraction system that, in turn, can be applied as a foundation for the further study of the progress of risk factors and CVD. Results We designed a light annotation task to capture CVD risk factors with indicators, temporal attributes and assertions that were explicitly or implicitly displayed in the records. The task included: 1) preparing data; 2) creating guidelines for capturing annotations (these were created with the help of clinicians); 3) proposing an annotation method including building the guidelines draft, training the annotators and updating the guidelines, and corpus construction. Meanwhile, we proposed some creative annotation guidelines: (1) the under-threshold medical examination values were annotated for our purpose of studying the progress of risk factors and CVD; (2) possible and negative risk factors were concerned for the same reason, and we created assertions for annotations; (3) we added four temporal attributes to CVD risk factors in CEMRs for constructing long term variations. Then, a risk factor annotated corpus based on de-identified discharge summaries and progress notes from 600 patients was developed. Built with the help of clinicians, this corpus has an inter-annotator agreement (IAA) F1-measure of 0.968, indicating a high reliability. Conclusion To the best of our knowledge, this is the first annotated corpus concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk factor annotations from CEMRs were proposed. The obtained document-level annotations can be applied in future studies to monitor risk factors and CVD over the long term.
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Affiliation(s)
- Jia Su
- Language Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China
| | - Bin He
- Language Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China
| | - Yi Guan
- Language Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China.
| | - Jingchi Jiang
- Language Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China
| | - Jinfeng Yang
- School of Software, Harbin University of Science and Technology, Harbin, China
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A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:4898963. [PMID: 29065612 PMCID: PMC5516712 DOI: 10.1155/2017/4898963] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 05/05/2017] [Accepted: 05/16/2017] [Indexed: 12/03/2022]
Abstract
Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-based medication entity recognition. We hypothesized that this approach could avoid the side effects of abundant negative samples and improve the performance of the named entity recognition from admission notes written in Chinese. Therefore, we applied this approach to a test set of 324 Chinese-written admission notes with manual annotation by medical experts. Our data demonstrated that this approach had a score of 94.2% in precision, 92.8% in recall, and 93.5% in F-measure for the recognition of traditional Chinese medicine drug names and 91.2% in precision, 92.6% in recall, and 91.7% F-measure for the recognition of Western medicine drug names. The differences in F-measure were significant compared with those in the baseline systems.
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Zhang R, Liu J, Huang Y, Wang M, Shi Q, Chen J, Zeng Z. Enriching the international clinical nomenclature with Chinese daily used synonyms and concept recognition in physician notes. BMC Med Inform Decis Mak 2017; 17:54. [PMID: 28464923 PMCID: PMC5414139 DOI: 10.1186/s12911-017-0455-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 04/26/2017] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND It has been shown that the entities in everyday clinical text are often expressed in a way that varies from how they are expressed in the nomenclature. Owing to lots of synonyms, abbreviations, medical jargons or even misspellings in the daily used physician notes in clinical information system (CIS), the terminology without enough synonyms may not be adequately suitable for the task of Chinese clinical term recognition. METHODS This paper demonstrates a validated system to retrieve the Chinese term of clinical finding (CTCF) from CIS and map them to the corresponding concepts of international clinical nomenclature, such as SNOMED CT. The system focuses on the SNOMED CT with Chinese synonyms enrichment (SCCSE). The literal similarity and the diagnosis-related similarity metrics were used for concept mapping. Two CTCF recognition methods, the rule- and terminology-based approach (RTBA) and the conditional random field machine learner (CRF), were adopted to identify the concepts in physician notes. The system was validated against the history of present illness annotated by clinical experts. The RTBA and CRF could be combined to predict new CTCFs besides SCCSE persistently. RESULTS Around 59,000 CTCF candidates were accepted as valid and 39,000 of them occurred at least once in the history of present illness. 3,729 of them were accordant with the description in referenced Chinese clinical nomenclature, which could cross map to other international nomenclature such as SNOMED CT. With the hybrid similarity metrics, another 7,454 valid CTCFs (synonyms) were succeeded in concept mapping. For CTCF recognition in physician notes, a series of experiments were performed to find out the best CRF feature set, which gained an F-score of 0.887. The RTBA achieved a better F-score of 0.919 by the CTCF dictionary created in this research. CONCLUSIONS This research demonstrated that it is feasible to help the SNOMED CT with Chinese synonyms enrichment based on physician notes in CIS. With continuous maintenance of SCCSE, the CTCFs could be precisely retrieved from free text, and the CTCFs arranged in semantic hierarchy of SNOMED CT could greatly improve the meaningful use of electronic health record in China. The methodology is also useful for clinical synonyms enrichment in other languages.
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Affiliation(s)
- Rui Zhang
- Department of Medical Informatics, West China School of Medicine/West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Jialin Liu
- Department of Medical Informatics, West China School of Medicine/West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
- Information Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
- Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Yong Huang
- Information Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Miye Wang
- Information Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Qingke Shi
- Information Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Jun Chen
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Zhi Zeng
- Department of Medical Informatics, West China School of Medicine/West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
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He B, Dong B, Guan Y, Yang J, Jiang Z, Yu Q, Cheng J, Qu C. Building a comprehensive syntactic and semantic corpus of Chinese clinical texts. J Biomed Inform 2017; 69:203-217. [DOI: 10.1016/j.jbi.2017.04.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 04/06/2017] [Accepted: 04/07/2017] [Indexed: 11/25/2022]
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18
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An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels. ScientificWorldJournal 2015; 2015:507925. [PMID: 26504897 PMCID: PMC4609520 DOI: 10.1155/2015/507925] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 06/25/2015] [Indexed: 12/02/2022] Open
Abstract
Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2% ± 2.8% measured by zero-one loss for the first evaluation session and 79.6% ± 3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low.
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