EHR Coding with Multi-scale Feature Attention and Structured Knowledge Graph Propagation

Xie, Xiancheng; Xiong, Yun; Yu, Philip S.; Zhu, Yu-Xian

doi:10.1145/3357384.3357897

Cited by 71 publications

(55 citation statements)

References 9 publications

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“…Out of the 256 publications, we excluded 65 publications, as the described Natural Language Processing algorithms in those publications were not evaluated. The full text of the remaining 191 publications was assessed and 114 a As reported by authors [11,30,33,35,37,41,44,46,49,55,58,64,68,69,72,73,75,76,83,87,90,95,98,100,104] Included reference to dataset 21 (27%) [11,30,35,37,41,44,46,49,55,58,64,72,75,76,83,87,90,95,98,100,104] Training of algorithm Trained 47 (61%) [11, 12, 29, 31, 32, 34, 37, 39, 41, 42, 44, 45, 48-53, 55-59, 62, 63, 65, 66, 68, 69, 73, 74, 76, 78-84, 87, 88, 90, 95, 96, 98, 99, 104] Not listed 3 (3.9%) …”

Section: Resultsmentioning

confidence: 99%

“…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%

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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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Section: Resultsmentioning

confidence: 99%

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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“…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%

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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“…1 "003.21" (ICD code of salmonella meningitis) and "003.22" (ICD code of salmonella pneumonia) share common characteristics (i.e., they are all caused by salmonella infections) and may be strongly related to each other. Third, few publications [24,39] consider such relations and only focus on the relations between parent-child ICD codes.…”

Section: Introductionmentioning

confidence: 99%

Coding Electronic Health Records with Adversarial Reinforcement Path Generation

Wang

Ren

Chen

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Electronic Health Record (EHR) coding is the task of assigning one or more International Classification of Diseases (ICD) codes to every EHR. Most previous work either ignores the hierarchical nature of the ICD codes or only focuses on parent-child relations. Moreover, existing EHR coding methods predict ICD codes from the leaf level with the greatest ICD number and the most fine-grained categories, which makes it difficult for models to make correct decisions. In order to address these problems, we model EHR coding as a path generation task. For this approach, we need to address two main challenges: (1) How to model relations between EHR and ICD codes, and relations between ICD codes? (2) How to evaluate the quality of generated ICD paths in order to obtain a signal that can be used to supervise the learning? We propose a coarse-tofine ICD path generation framework, named Reinforcement Path Generation Network (RPGNet), that implements EHR coding with a Path Generator (PG) and a Path Discriminator (PD). We address challenge (1) by introducing a Path Message Passing (PMP) module in the PG to encode three types of relation: between EHRs and ICD codes, between parent-child ICD codes, and between sibling ICD codes. To address challenge (2), we propose a PD component that estimates the reward for each ICD code in a generated path. RPGNet is trained with Reinforcement Learning (RL) in an adversarial manner. Experiments on the MIMIC-III benchmark dataset show that RPGNet significantly outperforms state-of-the-art methods in terms of micro-averaged F1 and micro-averaged AUC. CCS CONCEPTS• Information systems → Content analysis and feature selection; • Applied computing → Health care information systems; • Computing methodologies → Adversarial learning; Reinforcement learning.

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EHR Coding with Multi-scale Feature Attention and Structured Knowledge Graph Propagation

Cited by 71 publications

References 9 publications

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

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

Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters

Coding Electronic Health Records with Adversarial Reinforcement Path Generation

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