A Comparison of Deep Learning Methods for ICD Coding of Clinical Records

Moons, Elias; Khanna, Aditya; Akkasi, Abbas; Moens, Marie‐Francine

doi:10.3390/app10155262

Cited by 43 publications

(37 citation statements)

References 29 publications

(44 reference statements)

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“…Finally, it should be noted that most of the models presented to the CodiEsp-X subtask used NLP algorithms from outside the scope of ML [16]. As a consequence of the line of work starting from the evaluation shared tasks of the CodiEsp track, some studies have emerged in recent months that make use of the CodiEsp corpus or of a specific clinical corpus in Spanish to address the problem of automatic clinical coding, using various methodologies, among which, the DL techniques stand out [12].…”

Section: B Clinical Coding Shared Tasksmentioning

confidence: 99%

“…Traditionally, natural language processing (NLP) strategies have been applied to the problem of automatic clinical coding [2], [5]- [7], although more recent studies focus on the use of rule-based approaches, machine learning (ML) strategies, and deep learning (DL) models [8]- [12]. However, most of the previous works in the literature focus on texts written in English due to the limited availability of annotated corpora with standardized clinical coding labels and additional linguistic resources in languages other than English.…”

Section: Introductionmentioning

confidence: 99%

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Transformers for Clinical Coding in Spanish

et al. 2021

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Automatic clinical coding is an essential task in the process of extracting relevant information from unstructured documents contained in electronic health records (EHRs). However, most research in the development of computer-based methods for clinical coding focuses on texts written in English due to the limited availability of medical linguistic resources in languages other than English. With nearly 500 million native speakers, there is a worldwide interest in processing healthcare texts in Spanish. In this study, we systematically analyzed transformer-based models for automatic clinical coding in Spanish. Using a transfer-learning-based approach, the three existing transformer architectures that support the Spanish language, namely, multilingual BERT (mBERT), BETO and XLM-RoBERTa (XLM-R), were first pretrained on a corpus of real-world oncology clinical cases with the goal of adapting transformers to the particularities of Spanish medical texts. The resulting models were fine-tuned on three distinct clinical coding tasks, following a multilabel sentence classification strategy. For each analyzed transformer, the domain-specific version outperformed the original general domain model across those tasks. Moreover, the combination of the developed strategy with an ensemble approach leveraging the predictive capacities of the three distinct transformers yielded the best obtained results, with MAP scores of 0.662, 0.544 and 0.884 on CodiEsp-D, CodiEsp-P and Cantemist-Coding shared tasks, which remarkably improved the previous state-of-the-art performance by 11.6%, 10.3% and 4.4%, respectively. We publicly release the mBERT, BETO and XLMR transformers adapted to the Spanish clinical domain at https://github.com/guilopgar/ClinicalCodingTransformerES, providing the clinical natural language processing community with advanced deep learning methods for performing medical coding and other tasks in the Spanish clinical domain.INDEX TERMS Clinical coding, deep learning, natural language processing, text classification, transformers.

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Section: B Clinical Coding Shared Tasksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transformers for Clinical Coding in Spanish

et al. 2021

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“…The paper A Comparison of Deep Learning Methods for ICD Coding of Clinical Records authored by Moons and colleagues [13] presents a survey of various deep learning methods for text classification in a hierarchical framework for the domain of medical documents. Methods based on exploiting the taxonomy structure and also flat methods are discussed.…”

Section: Complex Machine Learning and Deep Learning Predictive Algorithmsmentioning

confidence: 99%

Applications of Medical Informatics and Data Analysis Methods

Nieminen¹

2020

Applied Sciences

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“…This approach achieved a high accuracy of 0.841. Moons et al [ 21 ] have applied multiple deep learning approaches on the classification of ICD-9 code. Their deep learning approaches utilized the discharge summary for the classification in which self-attention, regularization, and the loss function with attention included a convolutional neural network showed to be valuable.…”

Section: Introductionmentioning

confidence: 99%

“…Note that the authors in [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] used patient profile, clinical examination reports and physician diagnosis results to establish models for the prediction of certain diseases that are commonly seen or with high mortality rates. On the contrary, in this paper, we only make use of patients’ self-report data (i.e., the subjective component in the progress note of EMR) to establish a predictive model for a variety of diseases.…”

Section: Introductionmentioning

confidence: 99%

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records

Hsu

Hsieh³

et al. 2020

Sensors

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The International Statistical Classification of Disease and Related Health Problems (ICD) is an international standard system for categorizing and reporting diseases, injuries, disorders, and health conditions. Most previously-proposed disease predicting systems need clinical information collected by the medical staff from the patients in hospitals. In this paper, we propose a deep learning algorithm to classify disease types and identify diagnostic codes by using only the subjective component of progress notes in medical records. In this study, we have a dataset, consisting of about one hundred and sixty-eight thousand medical records, from a medical center, collected during 2003 and 2017. First, we apply standard text processing procedures to parse the sentences and word embedding techniques for vector representations. Next, we build a convolution neural network model on the medical records to predict the ICD-9 code by using a subjective component of the progress note. The prediction performance is evaluated by ten-fold cross-validation and yields an accuracy of 0.409, recall of 0.409 and precision of 0.436. If we only consider the “chapter match” of ICD-9 code, our model achieves an accuracy of 0.580, recall of 0.580, and precision of 0.582. Since our diagnostic code prediction model is solely based on subjective components (mainly, patients’ self-report descriptions), the proposed approach could serve as a remote and self-diagnosis assistance tool, prior to seeking medical advice or going to the hospital. In addition, our work may be used as a primary evaluation tool for discomfort in the rural area where medical resources are restricted.

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A Comparison of Deep Learning Methods for ICD Coding of Clinical Records

Cited by 43 publications

References 29 publications

Transformers for Clinical Coding in Spanish

Transformers for Clinical Coding in Spanish

Applications of Medical Informatics and Data Analysis Methods

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records

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