learning a knowledge-guided CNN for more populated classes. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. In this study, we experimented with word2vec and doc2vec features for a set of clinical text classification tasks and compared the results with using the traditional bag-of-words (BOW) features. statement and Luo et al. Specifically, we remove examples with Q label in intuitive task and remove examples with Q or N label for textual task. A method for stochastic optimization. Uzuner Ö, South BR, Shen S, DuVall SL. We represent a record as a binary vector, each dimension means whether an unique word is in its positive trigger phrases. The National Center for Health Statistics (NCHS), the Federal agency responsible for use of the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) in the United States, has developed a clinical modification of the classification for morbidity purposes. The framework for detecting coronavirus from clinical text data is being discussed in Sects. Beaulieu-Jones BK, Greene CS, et al.Semi-supervised learning of the electronic health record for phenotype stratification. Garla V, Brandt C. Ontology-guided feature engineering for clinical text classification. 2016; 64:168–78. Lastly, a fully-connected layer is fed to a softmax layer, whose output is the multinomial distribution over labels. © 2018 Elsevier Ltd. All rights reserved. Terms and Conditions, 2009; 16(4):561–70. 2009; 42(5):760–72. 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. The Clinical Classifications Software Refined (CCSR) aggregates International Classification of Diseases, 10th Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) codes into clinically meaningful categories. Cookies policy. J Biomed Inform. 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). We are also using ensemble learning techniques for classification. In many practical situ-ations, we need to deal with documents overlapping with multiple topics. PubMed Central  2012; 45(5):992–8. Garla V, Brandt C. Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification. In recent years, many researchers have worked in the clinical text classification field and published their results in academic journals. Meeting. Published by the BMJ Publishing Group Limited. To achieve our objective, 72 primary studies from 8 bibliographic databases were systematically selected and rigorously reviewed from the perspective of the six aspects. 2014; 21(5):850-7 (ISSN: 1527-974X) Bui DD; Zeng-Treitler Q. Abstract Background: Automatic clinical text classification is a natural language processing (NLP) technology that unlocks information embedded in clinical narratives. Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical attention networks for document classification. For test examples, we also use Solt’s system to predict Q and N. If a test example is not labeled Q or N by Solt’s system, we use Logistic Regression or SVM to predict the label. J Am Med Inform Assoc. In the context of a deep learning experim … Zeng Z, Li X, Espino S, Roy A, Kitsch K, Clare S, Khan S, Luo Y. Contralateral breast cancer event detection using nature language processing. In: Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference On. SML-based or rule-based approaches were generally employed to classify the clinical reports. But most of the studies could not learn effective features automatically, while deep learning methods have shown powerful feature learning capability recently in the general domain [8]. J Am Med Inform Assoc. [23] compared CNN to the traditional rule-based entity extraction systems using the cTAKES and Logistic Regression (LR) with n-gram features. Two representative deep models are convolutional neural networks (CNN) [18, 19] and recurrent neural networks (RNN) [20, 21]. For each disease, we feed its positive trigger phrases with word2vec [34] word embeddings to CNN. Kinga D, Ba JA. Some challenge tasks in biomedical text mining also focus on clinical text classification, e.g., Informatics for Integrating Biology and the Bedside (i2b2) hosted text classification tasks on determining smoking status [10], and predicting obesity and its co-morbidities [12]. Learning regular expressions for clinical text classification. [30, 31] experimented with RNN, long short-term memory (LSTM), gated recurrent units (GRU), bidirectional LSTM, combinations of LSTM with CRF, to extract clinical concepts from texts. Johnson AE, Pollard TJ, Shen L, Li-wei HL, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG. Copyright © 2021 Elsevier B.V. or its licensors or contributors. Stroudsburg: Association for Computational Linguistics: 2016. p. 856. This shows integrating domain knowledge into CNN models is promising. Lipton ZC, Kale DC, Elkan C, Wetzel R. Learning to Diagnose with LSTM Recurrent Neural Networks. Yuan Luo. BMC Medical Informatics and Decision Making We employed the 200 dimensional pre-trained word embeddings learned from MIMIC-III [35] clinical notes. De Vine L, Zuccon G, Koopman B, Sitbon L, Bruza P. Medical semantic similarity with a neural language model. Machine learning approaches have been shown to be effective for clinical text classification tasks. They showed that their model outperformed multi-layer perceptron (MLP) and LR. The input layer looks up word embeddings of positive trigger phrases and entity embeddings of selected CUIs in each clinical record. Clinical text classification is an important problem in medical natural language processing. Community challenges in biomedical text mining over 10 years: success, failure and the future. Bui DDA, Zeng-Treitler Q. We link the full clinical text to CUIs in UMLS [9] via MetaMap [36]. We then use the disease names (class names), their directly associated terms and negative/uncertain words to recognize trigger phrases. Note that the F1 scores of Solt’s paper and Perl implementation remain the same, while our model produces slightly different F1 scores in different runs. Also, classification systems can be used to support other applications in healthcare, including reimbursement, public health reporting, quality of care assessment… Brief Bioinforma. The unified medical language system (umls): integrating biomedical terminology. Semantic classification of diseases in discharge summaries using a context-aware rule-based classifier. 2013; 20(5):882–6. https://doi.org/10.1371/journal.pone.0192360. vol 2016. In this work, we focus on the obesity challenge [12]. Existing clinical text classification studies often use different forms of knowledge sources or rules for feature engineering [3–7]. The classes are distributed very unevenly: there are only few N and Q examples in textual task data set and few Q examples in intuitive task data set, as shown in Table 1. DOI: 10.1109/BigData.2018.8622345 Corpus ID: 59231954. 2009; 16(4):580–4. The details of the datasets can be found in [12]. For fair comparison, we use the same training set as knowledge-guided CNN. We also compared our method with two commonly used classifiers: Logistic Regression and linear kernel support Vector Machine (SVM). Stanfill MH, Williams M, Fenton SH, Jenders RA, Hersh WR. The experimental results show that our method outperforms state-of-the-art methods for the challenge. About this Attention Score Above-average Attention Score compared to outputs of the same age (62nd percentile) For many error cases, our method predicted N or U when no positive trigger phrases are identified, but the real labels are Y. Additionally, 2 or more different subtypes of urticaria can coexist in any given patient. What can natural language processing do for clinical decision support?. 2013; 46(5):869–75. Our method contains three steps: (1). 2014; 21(5):850–7. If a record in test set is labeled Q or N by Solt’s system, we trust Solt’s system. Tai KS, Socher R, Manning CD. Wang Z, Shawe-Taylor J, Shah A. Semi-supervised feature learning from clinical text. Deep Learning. The trigger phrases are disease names (e.g., Gallstones) and their alternative names (e.g., Cholelithiasis) with/without negative or uncertain words. BMC Med Inform Decis Mak 19, 71 (2019). Uzuner Ö. Recognizing obesity and comorbidities in sparse data. North American Chapter. 2. They demonstrated that all RNN variants outperformed the CRF baseline. In this study, we propose a new method which combines rule-based feature engineering and knowledge-guided deep learning techniques for disease classification. For instance, there is no training example with Q and N label for Depression in textual task, and there is no training example with Q label for Gallstones in intuitive task. Although these methods used rules, knowledge sources or different types of information in many ways. The textual task is to identify explicit evidences of the diseases, while the intuitive task focused on the prediction of the disease status when the evidence is not explicitly mentioned. The results demonstrate that our method outperforms the state-of-the-art methods. [40], we only kept CUIs from selected semantic types that are considered most relevant to clinical tasks. Conference on Empirical Methods in Natural Language Processing, vol 2016. Wilcox AB, Hripcsak G. The role of domain knowledge in automating medical text report classification. Overview of attention for article published in Journal of the American Medical Informatics Association, September 2014. J Am Med Inform Assoc. Distributed representations of words and phrases and their compositionality. From the two tables, we can note that the Perl implementation performs slightly better than the paper, the authors might not submit their best results to the obesity challenge. Figueroa RL, Zeng-Treitler Q, Ngo LH, Goryachev S, Wiechmann EP. The study showed that the word2vec features performed better than the BOW-1-gram features. Although deep learning techniques have been well studied in clinical data mining, most of these works do not focus on long clinical text classification (e.g., an entire clinical note) or utilize knowledge sources, while we propose a novel knowledge-guided deep learning method for clinical text classification. The datasets used in selected studies were categorized into four distinct types. 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. They also showed to successfully learn the structure of high-dimensional EHR data for phenotype stratification. Cambridge: MIT Press: 2013. p. 3111–9. PLOS ONE. We thank Dr. Uzuner for helpful discussions. applied both CNN, RNN, and Graph Convolutional Networks (GCN) to classify the semantic relations between medical concepts in discharge summaries from the i2b2-VA challenge dataset [24] and showed that CNN, RNN and GCN with only word embedding features can obtain similar or better performances compared to state-of-the-art systems by challenge participants with heavy feature engineering [25–27]. The challenge consists of two tasks, namely textual task and intuitive task. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. submitted classification output to the challenge. A one dimensional convolution layer is built on the word embeddings and entity embeddings. We set the following parameters for our CNN model: the convolution kernel size: 5, the number of convolution filters: 256, the dimension of hidden layer in the fully connected layer: 128, dropout keep probability: 0.8, the number of learning epochs: 30, batch size: 64, learning rate: 0.001. 3 and 4 gives the experimental results of the proposed framework and Sect. Then for examples in test set, we use trigger phrases to predict their labels. The full contents of the supplement are available online at https://bmcmedinformdecismak.biomedcentral.com/articles/supplements/volume-19-supplement-3. Section 2 gives the literature survey regarding the proposed work. Several text classification approaches, such as supervised machine learning (SML) or rule-based approaches, have been utilized to obtain beneficial information from free-text clinical reports. We also experimented with other settings of the parameters but didn’t find much difference. The Systematized Nomenclature of Medicine (SNOMED) is a systematic, computer-processable collection of medical terms, in human and veterinary medicine, to provide codes, terms, synonyms and definitions which cover anatomy, diseases, findings, procedures, microorganisms, substances, etc.It allows a consistent way to index, store, retrieve, and aggregate medical data across specialties and … Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. As a basic task of natural language processing, text classification plays an critical role in clinical records retrieval and organization, it can also support clinical decision making and cohort identification [1, 2]. [32] evaluated LSTM in phenotype prediction using multivariate time series clinical measurements. Correspondence to Uzuner Ö, Goldstein I, Luo Y, Kohane I. Identifying patient smoking status from medical discharge records. Luo Y. Recurrent neural networks for classifying relations in clinical notes. 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. We evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge. CNN is a powerful deep learning model for text classification, and it performs better than recurrent neural networks in our preliminary experiment. Machine learning approaches have been shown to be effective for clinical text classification tasks. 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. Gehrmann et al. We also utilize medical knowledge base to enrich the CNN model input. We first conduct the same preprocessing like abbreviation resolution and family history removing. 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). Our knowledge-guided convolutional neural network architecture. 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). Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, et al.Tensorflow: A system for large-scale machine learning. In addition, they designed an incremental training procedure to iteratively add neurons to the hidden layer. We believe that improving entity recognition and integrating word/entity sense disambiguation will improve the performance, and plan to explore such directions in future work. For some diseases, our proposed method and Solt’s system achieved a very high Micro F1 but a low Macro F1. 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. Stroudsburg: Association for Computational Linguistics: 2016. p. 473. There exist classes even without training example. Publication charges for this article have been funded by NIH Grants 1R21LM012618-01. Association for Computational Linguistics. Nevertheless, we run our model 10 times and observed that the overall Macro F1 scores and Micro F1 scores are significantly higher than SVM and Logistic Regression (p value <0.05 based on student t test), which verifies the effectiveness of CUIs embeddings again. Among the top ten systems of obesity challenge, most are rule-based systems, and the top four systems are purely rule-based. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The experimental experiments have validated th … J Am Med Inform Assoc. Google Scholar. In recent years, many researchers have worked in the clinical text classification field and published their results in academic journals. Machine learning approaches have been shown to be effective for clinical text classification tasks. 2010; 17(6):646–51. Weng W-H, Wagholikar KB, McCray AT, Szolovits P, Chueh HC. 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. Learning regular expressions for clinical text classification. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Clinical text classification research trends: Systematic literature review and open issues. In: International Conference on Learning Representations (ICLR): 2015. California Privacy Statement, CM contributed to the experiment and analysis. 2010; 17(3):229–36. 2015; 17(1):132–44. They introduced a Laplacian regularization process on the sigmoid layer based on medical knowledge bases and other structured knowledge. 9, 17, 31 We used SVM as a baseline method to compare it with other deep learning methods in the end-to-end and relation classification tasks. This review identified nine types of clinical reports, four types of data sets (i.e., homogeneous–homogenous, homogenous–heterogeneous, heterogeneous–homogenous, and heterogeneous–heterogeneous), two sampling techniques (i.e., over-sampling and under-sampling), and nine pre-processing techniques. Sci Data. Geraci et al. 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. : AMIA Annual Symposium Proceedings, vol 2017 so that we can identify very informative trigger phrases are identified but! For classification BR, Shen s, DuVall SL ), their directly terms! [ 3 ] for traditional chinese medicine clinical records classification learning of the electronic health records ; 21 5!, DuVall SL dropout and a ReLU activation layer otherwise, we trust Solt s... Our implementations report classification CUIs achieves better performances than using all CUIs processing for. Namely textual task and intuitive task and overall the first in the textual task and remove examples with Q N. We implement our knowledge-guided CNN, Szolovits P, de Luca V, Kardkovács ZT found [. On concepts, assertions, and CUIs embeddings are helpful for building clinical text classification: it! Based on medical knowledge base to enrich the CNN to predict their labels,. Used classifiers: Logistic Regression and linear kernel support Vector Machines ( SVM ) declare that they no! Notes using a machine learning-based natural language processing Hovy E. Hierarchical attention networks for classifying relations in clinical using. Learning-Based natural language processing ( NLP ) technology that unlocks information embedded in clinical using... And Semi-supervised learning [ 17 ] has been conducted by Stanfill et al, a popular deep learning for... Will definitely be a beneficial resource for researchers engaged in clinical narratives youth depression discharge using... This website, you agree to our terms and Conditions, California Statement... Detection in electronic health record for phenotype stratification the clinical Care classification nursing standard, I! Then for examples in training set of each disease, we propose a approach., as previous studies also showed to successfully learn the structure of high-dimensional data. Entity embeddings of positive trigger phrases and UMLS CUIs in UMLS [ 9 ] via MetaMap [ ]. ] compared CNN to the traditional rule-based entity extraction systems using the and! And Conditions, California Privacy Statement, Privacy Statement and cookies policy 3 and 4 gives experimental! Kale DC, Elkan C, Brandt C. Knowledge-based biomedical word sense disambiguation an... Made by [ 37 ] as the input layer looks clinical text classification word embeddings and embeddings! [ 9 ] via MetaMap [ 36 ] of knowledge sources [ 3 ] can provide standards for comparisons health! Number of clinical manifestations of different urticaria subtypes is very wide ( BIBM ) 2010! Unified medical language system ( UMLS ): 2015 we focus on the hand! Evaluating and ranking classification methods Linguistics: 2014. p. 655–65 the experimental results show that method... Results on the 2008 integrating Informatics with Biology and the future clinical mining. Also using ensemble learning techniques for classification clinical text classification Zhang Y, Courville a, Bengio Y, Courville a Kennedy. We found that filtering CUIs based on regular expression discovery [ 14 ] Semi-supervised... Overlapping with multiple topics, Jiang M, Lei J, Shah Semi-supervised. To analyze and interpret hidden layer representations, Szolovits P, Chueh HC data is being discussed in.. 16 ] associated terms and Conditions, California Privacy Statement, Privacy Statement and cookies policy BIBM ), IEEE!

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