Machine Learning Approaches on Diagnostic Term Encoding With the ICD for Clinical Documentation

Atutxa, Aitziber; Pérez, Alicia; Casillas, Arantza

doi:10.1109/jbhi.2017.2743824

Cited by 19 publications

(15 citation statements)

References 9 publications

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“…While the data included some standard codes, developing automated assistance in assigning codes for other non-English data sets is still a challenge. Two papers by the same research team, one of which was selected as a Best Paper 10 , address this issue for clinical documentation in Spanish 10 , 11 . Continued research on both automated methods for assigning standard codes for non-English clinical text as well as methods to link different code sets to each other and to analytic approaches will facilitate exchange of information for both operational and research uses.…”

Section: Resultsmentioning

confidence: 99%

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Bloomrosen¹,

Berner

2019

Yearb Med Inform

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Objectives: To summarize the recent literature and research and present a selection of the best papers published in 2018 in the field of Health Information Management (HIM) and Health Informatics. Methods: A systematic review of the literature was performed, with the help of a medical librarian, by the two editors of the HIM section of the International Medical Informatics Association (IMIA) Yearbook. In order to include papers that would address the special theme of the 2019 Yearbook on artificial intelligence (AI) as well as HIM, we searched bibliographic databases for HIM-related papers with an AI focus using both Medical Subject Headings (MeSH) descriptors and keywords in titles and abstracts. A shortlist of 15 candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results: While there were a significant number of manuscripts that addressed issues relevant to HIM, there were virtually none with MeSH headings indicating an HIM focus. Manuscripts that were considered related to the HIM field in terms of the practice of health information management as well as the profession included those that examined using machine learning and other AI approaches to identify protected health information in clinical text to aid with de-identification, automated coding approaches to translate free-text into standardized codes, and natural language processing approaches to extract clinical data to assist with populating cancer and other registries. Conclusions: The papers discussed in the HIM section reflect the special theme of the use of AI in healthcare on issues particularly relevant to the field of HIM. This synopsis discusses these papers and recommends that HIM practitioners be more involved in research and that researchers in AI and related areas recognize the applicability and relevance of their work to the field of HIM.

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Section: Resultsmentioning

confidence: 99%

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Bloomrosen¹,

Berner

2019

Yearb Med Inform

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“…Use of development set 16 (21%) [12,29,31,34,37,49,55,60,63,69,74,80,87,90,94,95] Not listed 4 (5.2%) [30,82,83,101] publications did not meet our criteria, of which 3 publications in which the algorithm was not evaluated, resulting in 77 included articles describing 77 studies. Reference checking did not provide any additional publications.…”

Section: Development Of Algorithmmentioning

confidence: 99%

Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies

et al. 2020

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Background Free-text descriptions in electronic health records (EHRs) can be of interest for clinical research and care optimization. However, free text cannot be readily interpreted by a computer and, therefore, has limited value. Natural Language Processing (NLP) algorithms can make free text machine-interpretable by attaching ontology concepts to it. However, implementations of NLP algorithms are not evaluated consistently. Therefore, the objective of this study was to review the current methods used for developing and evaluating NLP algorithms that map clinical text fragments onto ontology concepts. To standardize the evaluation of algorithms and reduce heterogeneity between studies, we propose a list of recommendations. Methods Two reviewers examined publications indexed by Scopus, IEEE, MEDLINE, EMBASE, the ACM Digital Library, and the ACL Anthology. Publications reporting on NLP for mapping clinical text from EHRs to ontology concepts were included. Year, country, setting, objective, evaluation and validation methods, NLP algorithms, terminology systems, dataset size and language, performance measures, reference standard, generalizability, operational use, and source code availability were extracted. The studies’ objectives were categorized by way of induction. These results were used to define recommendations. Results Two thousand three hundred fifty five unique studies were identified. Two hundred fifty six studies reported on the development of NLP algorithms for mapping free text to ontology concepts. Seventy-seven described development and evaluation. Twenty-two studies did not perform a validation on unseen data and 68 studies did not perform external validation. Of 23 studies that claimed that their algorithm was generalizable, 5 tested this by external validation. A list of sixteen recommendations regarding the usage of NLP systems and algorithms, usage of data, evaluation and validation, presentation of results, and generalizability of results was developed. Conclusion We found many heterogeneous approaches to the reporting on the development and evaluation of NLP algorithms that map clinical text to ontology concepts. Over one-fourth of the identified publications did not perform an evaluation. In addition, over one-fourth of the included studies did not perform a validation, and 88% did not perform external validation. We believe that our recommendations, alongside an existing reporting standard, will increase the reproducibility and reusability of future studies and NLP algorithms in medicine.

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“…A few works are tackling the classification task using information retrieval techniques , statistical classifiers and document similarity [1], as well as machine learning techniques [6]. Another approach is to view the problem as a multi-label classification task and use neural networks like CNN, LSTM/BiLSTM and HA-GRU [19], or applying BERT which has shown good results on this task in German [3].…”

Section: Related Workmentioning

confidence: 99%

Automatic ICD-10 codes association to diagnosis: Bulgarian case

Velichkov

Gerginov

Panayotov

et al. 2020

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics

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This paper presents an approach for the automatic association of diagnoses in Bulgarian language to ICD-10 codes. Since this task is currently performed manually by medical professionals, the ability to automate it would save time and allow doctors to focus more on patient care. The presented approach employs a fine-tuned language model (i.e. BERT) as a multi-class classification model. As there are several different types of BERT models, we conduct experiments to assess the applicability of domain and language specific model adaptation. To train our models we use a big corpora of about 350,000 textual descriptions of diagnosis in Bulgarian language annotated with ICD-10 codes. We conduct experiments comparing the accuracy of ICD-10 code prediction using different types of BERT language models. The results show that the MultilingualBERT model (Accuracy Top 1-81%; Macro F1-86%, MRR Top 5-88%) outperforms other models. However, all models seem to suffer from the class imbalance in the training dataset. The achieved accuracy of prediction in the experiments can be evaluated as very high, given the huge amount of classes and noisiness of the data. The result also provides evidence that the collected dataset and the proposed approach can be useful in building an application to help medical practitioners with this task and encourages further research to improve the prediction

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Machine Learning Approaches on Diagnostic Term Encoding With the ICD for Clinical Documentation

Cited by 19 publications

References 9 publications

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies

Automatic ICD-10 codes association to diagnosis: Bulgarian case

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