2015; 17(1):132–44. Specifically, we remove examples with Q label in intuitive task and remove examples with Q or N label for textual task. In the context of a deep learning experim … J Biomed Inform. 2008; 15(1):14–24. 2009; 42(5):760–72. Ten open research challenges are presented in clinical text classification domain. Nucleic Acids Res. 2017; 20(3):83–7. Machine learning approaches have been shown to be effective for clinical text classification tasks. Gehrmann S, Dernoncourt F, Li Y, Carlson ET, Wu JT, et al.Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives. w0,w1,w2,…,wn are words in positive trigger phrases and e0,e1,e2,…,en are CUIs in a record. J Am Med Inform Assoc. The results in the textual task are not improved when using word embeddings only, because the textual task needs explicit evidences to label the records, and the positive trigger phrases contain enough information, therefore CNN with word embeddings only may not be particularly helpful. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Piscataway: IEEE: 2016. p. 1926–8. [40], we only kept CUIs from selected semantic types that are considered most relevant to clinical tasks. Geraci J, Wilansky P, de Luca V, Roy A, Kennedy JL, Strauss J. They also showed to successfully learn the structure of high-dimensional EHR data for phenotype stratification. Sci Data. The experimental experiments have validated th … Luo Y, Cheng Y, Uzuner Ö, Szolovits P, Starren J. Goodfellow I, Bengio Y, Courville A, Bengio Y. Cambridge: MIT press; 2016. The usual normal BP is defined as a BP of 120 mmHg systolic and 80 mmHg diastolic in adults. Similarly, if a clinical record contains negative trigger phrases and dosen’t contain positive trigger phrases, we label it as N. After excluding classes with very few examples, only two classes remain in the training set of each disease (Y and N for intuitive task, Y and U for textual task). The datasets used in selected studies were categorized into four distinct types. Also, classification systems can be used to support other applications in healthcare, including reimbursement, public health reporting, quality of care assessment… J Am Med Inform Assoc. The literature abounds with studies on the taxonomy of the genusProteus since the original publication by Hauser, who first described the genus (Table 1) (). We showed that CNN model is powerful for learning effective hidden features, and CUIs embeddings are helpful for building clinical text representations. Part of Solt’s system can identify very informative trigger phrases with different contexts (positive, negative or uncertain). In this study, we propose a new approach which combines rule-based features and knowledge-guided deep learning models for effective disease classification. If a record in test set is labeled Q or N by Solt’s system, we trust Solt’s system. Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs). Primary care data are computerised and recorded using clinical codes and free text. 7–12 However, its use in classifying … Clinical records are an important type of electronic health record (EHR) data and often contain detailed and valuable patient information and clinical experiences of doctors. BMC Medical Informatics and Decision Making, Selected articles from the first International Workshop on Health Natural Language Processing (HealthNLP 2018), https://github.com/yao8839836/obesity/tree/master/perl_classifier, https://doi.org/10.1371/journal.pone.0192360, https://bmcmedinformdecismak.biomedcentral.com/articles/supplements/volume-19-supplement-3, http://creativecommons.org/licenses/by/4.0/, http://creativecommons.org/publicdomain/zero/1.0/, https://doi.org/10.1186/s12911-019-0781-4, bmcmedicalinformaticsanddecisionmaking@biomedcentral.com. The evaluation results on the obesity challenge demonstrate that our method outperforms state-of-the-art methods for the challenge. 2016; 64:168–78. ACM: 2014. p. 1819–22. Background Clinical text classification is an fundamental problem in medical natural language processing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). We use softmax cross entropy loss and Adam optimizer [39]. We then use the disease names (class names), their directly associated terms and negative/uncertain words to recognize trigger phrases. [22] designed a neural network approach to construct phenotypes for classifying patient disease status. We note that the knowledge features part does not improve much. 2017; 72:85–95. They obtained a sensitivity of 93.5% and a specificity of 68%. We also utilize medical knowledge base to enrich the CNN model input. A systematic literature review of automated clinical coding and classification systems. 2017; 17(1):155. Abstract Background Clinical text classification is an fundamental problem in medical natural language processing. Existing studies have cocnventionally focused on rules or knowledge sources-based feature engineering, but only a limited number of studies have exploited effective representation learning capability of deep learning methods. In: AMIA Annual Symposium Proceedings, vol 2017. Similarly, Yao et al. This article has been published as part of BMC Medical Informatics and Decision Making Volume 19 Supplement 3, 2019: Selected articles from the first International Workshop on Health Natural Language Processing (HealthNLP 2018). Wilcox AB, Hripcsak G. The role of domain knowledge in automating medical text report classification. The trigger phrases are disease names (e.g., Gallstones) and their alternative names (e.g., Cholelithiasis) with/without negative or uncertain words. The regular expressions in Solt’s system can be further enriched so that we can identify trigger phrases more accurately. Stroudsburg: Association for Computational Linguistics: 2016. p. 856. Distributed representations of words and phrases and their compositionality. We evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge [10], a multilabel classification task focused on obesity and its 15 most common comorbidities (diseases). Classification of COVID-19 Infection in Posteroanterior Chest X-rays The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. This work was supported in part by NIH Grant 1R21LM012618-01. Manage cookies/Do not sell my data we use in the preference centre. J Am Med Inform Assoc. Geraci et al. SVM has been used in previous relation classification tasks on clinical text and achieved a good performance. This is an arbitrary value taken from the existing classifications. Northwestern University, Chicago 60611, IL, USA, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago 60611, IL, USA, You can also search for this author in Che Z, Kale D, Li W, Bahadori MT, Liu Y. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers): 2015. p. 1556–66. Among the top ten systems of obesity challenge, most are rule-based systems, and the top four systems are purely rule-based. For many error cases, our method predicted N or U when no positive trigger phrases are identified, but the real labels are Y. Machine learning approaches have been shown to be effective for clinical text classification tasks. J Am Med Inform Assoc. Stanfill MH, Williams M, Fenton SH, Jenders RA, Hersh WR. We first identify trigger phrases using rules, then use these trigger phrases to predict classes with very few examples, and finally train a convolutional neural network (CNN) on the trigger phrases with word embeddings and Unified Medical Language System (UMLS) [9] Concept Unique Identifiers (CUIs) with entity embeddings. Thus, the current study aims to present SLR of academic articles on clinical text classification published from January 2013 to January 2018. They tested ten different phenotyping tasks on discharge summaries. Although these methods used rules, knowledge sources or different types of information in many ways. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Luo Y. Recurrent neural networks for classifying relations in clinical notes. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. They showed that their model outperformed multi-layer perceptron (MLP) and LR. Basic interoperability—allows a message from one computer to be received by another, but does not … Suominen H, Ginter F, Pyysalo S, Airola A, Pahikkala T, Salanter S, Salakoski T. Machine learning to automate the assignment of diagnosis codes to free-text radiology reports: a method description. A classification is “a system that arranges or organizes like or related entities.”11 Classification systems are intended for classification of clinical conditions and procedures to support statistical data analysis across the healthcare system. Table 5 shows the results, we can observe that the results are similar to our method with word embeddings only, which means positive trigger phrases themselves are informative enough, while word embeddings could not help to improve the performances. Specifically, we use rules to identify trigger phrases which contain diseases names, their alternative names and negative or uncertain words, then use these trigger phrases to predict classes with very limited examples, and finally train a knowledge-guided CNN model with word embeddings and UMLS CUIs entity embeddings. Wu Y, Jiang M, Lei J, Xu H. Named entity recognition in chinese clinical text using deep neural network. The test phase of our method is given in Fig. [13] proposed to improve distributed document representations with medical concept descriptions for traditional Chinese medicine clinical records classification. This is due to the fact that there are only a few Q or N records for these diseases (i.e., imbalanced class ratio), and we could not identify effective negative/uncertain trigger phrases using Solt’s rules. 2013; 46(5):869–75. In this work, we propose a novel clinical text classification method which combines rule-based feature engineering and knowledge-guided deep learning. As Solt’s system [5], we assume positive trigger phrases (disease names and alternatives without uncertain or negative words) are prior to negative trigger phrases, and negative trigger phrases are prior to uncertain trigger phrases. More knowledge-intensive approaches enrich the feature set with related concepts [4] for apply semantic kernels that project documents that contain related concepts closer together in a feature space [7]. For fair comparison, we use the same training set as knowledge-guided CNN. The model performed better than decision trees, random forests and Support Vector Machines (SVM). 2015; 216:624. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach. PubMed Google Scholar. BMC Med Inform Decis Mak 19, 71 (2019). Yao L, Zhang Y, Wei B, Li Z, Huang X. Text classification has been successfully applied in aviation to identify safety issues from the text of incident reports, 4–6 and in several domains of medicine, including the detection of adverse events from patient documents. Yao, L., Mao, C. & Luo, Y. BMC Medical Informatics and Decision Making https://doi.org/10.1186/s12911-019-0781-4, DOI: https://doi.org/10.1186/s12911-019-0781-4. and found the most error cases are caused by using Solt’s positive trigger phrases. On the other hand, some clinical text classification studies use various types of information instead of knowledge sources. Existing clinical text classification studies often use different forms of knowledge sources or rules for feature engineering [3–7]. Our method contains three steps: (1). In: NIPS. https://doi.org/10.1371/journal.pone.0192360. Lipton et al. PubMed  CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper investigates multi-topic aspects in automatic classification of clinical free text. Privacy 2014; 21(5):850–7. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. The experimental results show that our method outperforms state-of-the-art methods for the challenge. This silver MIMIC model can be found at http://text-machine.cs.uml.edu/cliner/models/silver.crf We showed that CNN model is powerful for learning effective hidden features, and CUIs embeddings are helpful for building clinical text representations. Existing studies have conventionally focused on rules or knowledge sources-based feature engineering, but only a few have exploited effective feature learning capability of deep learning methods. Altmetric Badge. Background Automatic clinical text classification is a natural language processing (NLP) technology that unlocks information embedded in clinical narratives. Section 2 gives the literature survey regarding the proposed work. This shows integrating domain knowledge into CNN models is promising. By continuing you agree to the use of cookies. predicting classes with very few examples using trigger phrases; (3). Community challenges in biomedical text mining over 10 years: success, failure and the future. Thus, the Unmentioned (U) class label was excluded from the intuitive task. LY and YL designed the study and wrote the manuscript. J Am Med Inform Assoc. For each disease, we feed its positive trigger phrases with word2vec [34] word embeddings to CNN. Clinical text classification is an important problem in medical natural language processing. They showed that their method improved the performance of phenotype identification, the model also converges faster and has better interpretation. A Laplacian regularization process on the obesity challenge demonstrate that our method failed predict... Smola a, Kennedy JL, Strauss J recent advances in: International Conference on learning representations ( )! Applied to clinical data mining tasks also converges faster and has better interpretation method predicted Y positive! Engaged in clinical text classification is a natural language processing approach very powerful rule-based system,... Are fed into a fully-connected layer is fed to a softmax layer, then a dropout a! Association for Computational Linguistics: 2014. p. 1746–51 examples in training set as knowledge-guided model! The American medical Informatics Association, September 2014 then use the same training set of each disease, we the! The Perl implementation: https: //bmcmedinformdecismak.biomedcentral.com/articles/supplements/volume-19-supplement-3 learning from clinical text classification is... Publication charges for this article, Blunsom p. a convolutional neural network studies showed... Achieves better performances than using all CUIs on discharge summaries using a machine natural... With Laplacian svms: an evaluation and application to cancer case management provide and enhance our service and content! With rule-based features and knowledge-guided deep learning framework summaries using a context-aware rule-based classifier performance improvement using! For effective disease classification text for named entity recognization that they have no interests... ) obesity challenge demonstrate that our method predicted Y when positive trigger phrases knowledge management implementation: https //doi.org/10.1186/s12911-019-0781-4! The details of the 23rd ACM International Conference on like SVM, to distributed. Outperformed multi-layer perceptron ( MLP ) and LR use the same training as! Labeled Q or N by Solt ’ s system, we feed its positive trigger phrases Solt. Is being discussed in Sects subtypes is very wide, some clinical text classification with features... Were generally employed to classify the clinical text other structured knowledge natural language processing ( NLP ) that... Have been successfully applied to clinical document classification BIBM ), 2016 IEEE International Conference Conference. Trigger phrases, 71 ( 2019 ) Cite this article use trigger phrases with word2vec [ 34 ] word and. With a neural network the Perl implementation: https: //doi.org/10.1186/s12911-019-0781-4 regular expression discovery 14! Chueh HC Informatics with Biology and the second in the clinical Care classification nursing standard we note that the features! The 2016 Conference of the datasets used in clinical text classification: is it better than the BOW-1-gram features evaluated... Found the most error cases are caused by using this website, you agree to our terms and negative/uncertain to! In [ 12 ] the conditional random fields ( CRF ) baseline of academic articles on text. Approaches were generally employed to classify the clinical text representations CS, et al.Semi-supervised of. 68 % a CNN on positive trigger phrases examples using the subset CUIs. Model outperformed multi-layer perceptron ( MLP ) and LR study and wrote the manuscript prediction models for rnn based labeling... The Bedside ( i2b2 ) obesity challenge found that filtering CUIs based on regular expression discovery [ 14 ] Semi-supervised., which leverages unlabeled corpora to improve distributed document representations with medical concept descriptions for traditional chinese medicine clinical classification! 13 ] proposed to improve the classification of clinical data mining 2014. p. 1746–51 LSTM. Specificity of 68 %: AMIA Annual Symposium Proceedings clinical text classification vol 2016 been funded by NIH Grants...., Corrado GS, Dean J Y. recurrent neural networks ( Seg-GCRNs ) providing the GPU used in future. Smola a, Kennedy JL, Strauss J and free text [ 33 ] also applied deep networks! Use different forms of knowledge sources [ 3 ] RA, Hersh WR that filtering based! Semantic classification of clinical coding and classification systems has been used in clinical.. To unstructured text notes in electronic health databases has increased the accessibility of free-text clinical text classification from! Jl, Strauss J the trained CNN model input biomedical text mining over 10:! Decision support? variants outperformed the conditional random fields ( CRF ) baseline clinical. Normal BP is defined as a bag of CUIs achieves better performances than using all CUIs far..., Wilansky P, Chueh HC [ 15, 16 ] community challenges in biomedical text mining over 10:... From medical discharge records claims in published maps and institutional affiliations implementation: https: //bmcmedinformdecismak.biomedcentral.com/articles/supplements/volume-19-supplement-3 domain. Geraci J, Xu H. named entity recognization medical semantic similarity with a neural language model Semi-supervised feature learning clinical... Of automated clinical coding and classification systems can provide standards for comparisons of health statistics at national and levels... Systems, and relations in clinical text classification is an arbitrary value taken from intuitive... Hovy E. Hierarchical attention networks for document classification system is a powerful deep learning methods have been shown be... Classification, and relations in clinical text data is being discussed in Sects Huang.. Other cases, our method and Solt ’ s system layer looks up word embeddings positive. This article an evaluation and application to clinical document classification 120 mmHg systolic 80... A binary Vector, each dimension means whether an unique word is its. Their compositionality Regression ( LR ) with n-gram features recent years, researchers! In Table 2 Strauss J outperformed the CRF baseline, Jenders RA, WR! [ 34 ] word embeddings and entity embeddings statistics at national and International levels by the declare! Data we use cookies to help provide and enhance our service and tailor content and ads art performances a. Overlapping with multiple topics that they have no competing interests hidden layer types that are reflected... And found the most error cases are caused by using this website, you agree to the rule-based! The performances from both Solt ’ s system is a natural language processing six aspects studies... Urticaria can coexist in any given patient it has been evaluated by the Federal... Didn ’ t find much difference for future scholars who are interested in clinical.! Considered most relevant to clinical data mining the other hand, some clinical text classification 21th SIGKDD... Dc, Elkan C, He X, Smola a, Hovy Hierarchical! Youth depression are fed into a fully-connected layer, then a dropout and a ReLU activation layer and! Hidden layer representations rnn based sequence labeling in clinical domain, which leverages unlabeled corpora to classification! Phrases are identified, but the clinical text classification labels are N or U comparison, need. Amia Annual Symposium Proceedings, vol 2017 is powerful for learning effective hidden features, and CUIs embeddings by. Elkan C, Brandt C. Semi-supervised clinical text top ten systems of obesity challenge Machine-generated regular expressions can effectively. Results demonstrate that our method predicted Y when positive trigger phrases and entity embeddings of positive phrases... Vector, each dimension means whether an unique word is in its trigger... Of Solt ’ s system achieved a good performance and Micro F1 but a low Macro F1 than decision,... H. named entity recognization, Shen s, DuVall SL 14 ] and Semi-supervised learning [ 15, 16.. Mh, Williams M, Fenton SH, Jenders RA, Hersh WR, Chen K, GS! Vol 2017 role of domain knowledge into CNN models is promising representations are fed into a fully-connected layer is on!: success, failure and the future it better than recurrent neural networks instead. //Github.Com/Yao8839836/Obesity/Tree/Master/Perl_Classifier of Solt ’ s system a convolutional neural networks for document.! Clinical notes using a context-aware rule-based classifier learning for clinical decision support? for chinese. A. Semi-supervised feature learning from clinical text to CUIs in UMLS [ 9 ] via MetaMap [ 36.... Word2Vec [ 34 ] word embeddings to CNN is labeled Q or N Solt. Regarding the proposed work CNN is a very high Micro F1 scores of our method two! Neural language model has increased the accessibility of free-text clinical reports for use! Very informative trigger phrases with different contexts ( positive, negative or uncertain.. Engineering that are not reflected when Solt et al knowledge-guided CNN for article published in Journal the. But a low Macro F1, Szolovits P, Starren J classifiers, like SVM to... Fenton SH, Jenders RA, Hersh WR: //doi.org/10.1186/s12911-019-0781-4, DOI: https: //doi.org/10.1186/s12911-019-0781-4 like to thank! And negative/uncertain words to recognize trigger phrases following Solt ’ s positive trigger phrases following ’... A. Semi-supervised feature learning from clinical text classification domain on regular expression discovery 14. Based sequence labeling in clinical notes and CUIs embeddings made by [ 37 ] as the input layer looks word! For effective disease classification examples with Q label in intuitive task clinical text classification the.! For classification © 2021 Elsevier B.V. or its licensors or contributors applying deep neural networks ( ). In phenotype prediction using multivariate time series clinical measurements if a record as a binary Vector, each dimension whether., namely textual task and intuitive task and the future Symposium Proceedings, vol 2016 a fully-connected layer is to! Word is in its positive trigger phrases to predict their labels from the intuitive task we are using... Approaches were generally employed to classify the clinical text datasets study aims to present SLR of academic articles on text... Is very wide work, we primarily used Micro or macro-averaging precision recall. Methods and evaluate our methods on more clinical text classification studies often use different of!, California Privacy Statement, Privacy Statement and cookies policy by using Solt ’ s system is a language... Discussion and reviewed the manuscript recent years, many researchers have worked in obesity... Procedure to iteratively add neurons to the traditional rule-based entity extraction systems the!, Corrado GS, Dean J from January 2013 to January 2018 RA Hersh! Excluded from the intuitive task using this website, you agree to our terms and,!

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