Interpretable deep learning to map diagnostic texts to ICD-10 codes

Atutxa, Aitziber; Ilarraza, Arantza Díaz de; Gojenola, Koldo; Oronoz, Maite; Perez-de-Viñaspre, Olatz

doi:10.1016/j.ijmedinf.2019.05.015

Cited by 46 publications

(49 citation statements)

References 16 publications

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“…ICD-10 is the 10th edition of the International statistical Classification of Diseases, a repository maintained by the World Health Organization to provide a standardized system of diagnostic codes for classifying diseases (Atutxa et al, 2019;Baumel et al, 2018). These classification codes are vastly used in clinical research and are a part of the electronic health records (EHRs) in the University Medical Center Utrecht (UMCU), The Netherlands.…”

Section: Introductionmentioning

confidence: 99%

“…The ICD coding task is challenging due to the use of free-text, multi-label setting of diagnosis codes and the large number of codes (Atutxa et al, 2019;Boytcheva 2011). Several attempts have been made to automatically assign ICD codes to medical documents, ranging from rule-based (Baghdadi et al, 2019;Boytcheva 2011;Koopman et al, 2015a;Nguyen et al, 2018) to machine learning approaches (Atutxa et al, 2019;Baumel et al, 2018;Cao et al, 2019;Chen et al, 2017;Du et al, 2019;Duarte et al, 2018;Karimi et al, 2017;Kemp et al, 2019;Koopman et al, 2015b;Lin et al, 2019;Liu et al, 2018;Miranda et al, 2018;Mujtaba et al, 2017;Mullenbach et al, 2018;Nigam et al, 2016;Pakhomov et al, 2006;Shing et al, 2019;Xie et al, 2019;Zweigenbaum and Lavergne, 2016). Rulebased methods have good performance when: (1) the terms to be categorized follow regular patterns, (2) the number of ICD labels is quite small, and (3) the task is limited to single-label classification (Atutxa et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…When a coded dataset is available and the range of the ICDs to label is large, machine learning based techniques have been successful (Atutxa et al, 2019;Baumel et al, 2018;Cao et al, 2019;Duarte et al, 2018;Miranda et al, 2018;Nigam et al, 2016). An approach for automatic matching of ICD-10 classification of Bulgarian free text (Boytcheva, 2011) was based on support vector machines (SVM).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, deep learning methods boosted benchmarked results in various text mining studies (Gargiulo et al, 2018;Shickel et al, 2017;Subramanyam and Sivanesan, 2020;Xiao, 2018), including in automated ICD coding (Atutxa et al, 2019;Baumel et al, 2018;Du et al, 2019;Duarte et al, 2018;Karimi et al, 2017;Lin et al, 2019;Liu et al, 2018;Miranda et al, 2018;Mujtaba et al, 2017;Mullenbach et al, 2018;Nigam et al, 2016;Shing et al, 2019). Karimi et al (Karimi et al, 2017) described a deep learning method for ICD coding, reporting on tests over a dataset of radiology reports.…”

Section: Introductionmentioning

confidence: 99%

“…In (Nigam et al, 2016), recurrent 1 https://www.who.int/classifications/icd/ neural networks (RNNs) have been applied to the multi-label classification task for assigning ICD-9 labels to medical notes, finding that an RNN with long short-term memory (LSTM) units shows an improvement over the binary relevance logistic regression model. Atutxa et al (Atutxa et al, 2019) evaluated different architectures of neural networks for multi-class document classification as a language modeling problem. In their experiments, the results of ICD-10 coding using the RNN-CNN architecture outperformed alternative approaches.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters

Bagheri

Sammani

Heijden

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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The international classification of diseases (ICD) is a widely used tool to describe patient diagnoses. At University Medical Center Utrecht (UMCU), for example, trained medical coders translate information from hospital discharge letters into ICD-10 codes for research and national disease epidemiology statistics, at considerable cost. To mitigate these costs, automatic ICD coding from discharge letters would be useful. However, this task has proven challenging in practice: it is a multi-label task with a large number of very sparse categories, presented in a hierarchical structure. Moreover, existing ICD systems have been benchmarked only on relatively easier versions of this task, such as single-label performance and performance on the higher "chapter" level of the ICD hierarchy, which contains fewer categories. In this study, we benchmark the state-of-the-art ICD classification systems and two baseline systems on a large dataset constructed from Dutch cardiology discharge letters at UMCU hospital. Performance of all systems is evaluated for both the easier chapter-level ICD codes and single-label version of the task found in the literature, as well as for the lower-level ICD hierarchy and multi-label task that is needed in practice. We find that stateof-the-art methods outperform the baseline for the single-label version of the task only. For the multi-label task, the baselines are not defeated by any state-of-the-art system, with the exception of HA-GRU, which does perform best in the most difficult task on accuracy. We conclude that practical performance may have been somewhat overstated in the literature, although deep learning techniques are sufficiently good to complement, though not replace, human ICD coding in our application.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters

Bagheri

Sammani

Heijden

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare

Yuan,

Yu,

Xie

et al. 2024

Medicine Advances

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Machine learning (ML) has achieved substantial success in performing healthcare tasks in which the configuration of every part of the ML pipeline relies heavily on technical knowledge. To help professionals with borderline expertise to better use ML techniques, Automated ML (AutoML) has emerged as a prospective solution. However, most models generated by AutoML are black boxes that are challenging to comprehend and deploy in healthcare settings. We conducted a systematic review to examine AutoML with interpretation systems for healthcare. We searched four databases (MEDLINE, EMBASE, Web of Science, and Scopus) complemented with seven prestigious ML conferences (AAAI, ACL, ICLR, ICML, IJCAI, KDD, and NeurIPS) that reported AutoML with interpretation for healthcare before September 1, 2023. We included 118 articles related to AutoML with interpretation in healthcare. First, we illustrated AutoML techniques used in the included publications, including automated data preparation, automated feature engineering, and automated model development, accompanied by a real‐world case study to demonstrate the advantages of AutoML over classic ML. Then, we summarized interpretation methods: feature interaction and importance, data dimensionality reduction, intrinsically interpretable models, and knowledge distillation and rule extraction. Finally, we detailed how AutoML with interpretation has been used for six major data types: image, free text, tabular data, signal, genomic sequences, and multi‐modality. To some extent, AutoML with interpretation provides effortless development and improves users' trust in ML in healthcare settings. In future studies, researchers should explore automated data preparation, seamless integration of automation and interpretation, compatibility with multi‐modality, and utilization of foundation models.

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