Combining deep learning and fuzzy logic to predict rare ICD-10 codes from clinical notes

Chomutare, Taridzo; Budrionis, Andrius; Dalianis, Hercules

doi:10.1109/icdh55609.2022.00033

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Another limiting factor is that many close codes have semantically similar descriptions. An example of 2 semantically similar codes is “Other diseases of tongue (K14.8)” and “Disease of tongue, unspecified (K14.9).” It is challenging for a model to discriminate the two, since they both appear to describe some unnamed condition of the tongue [ 14 ]. Combined with other limiting factors such as poor data quality and availability, these factors make the design of automatic ICD-10 coding systems nearly unattainable, especially for minor languages such as Norwegian and Swedish.…”

Section: Introductionmentioning

confidence: 99%

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial

Chomutare,

Lamproudis,

Budrionis

et al. 2024

JMIR Res Protoc

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Background Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. Objective The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. Methods The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. Results We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence–based CAC innovations to improve coding practice. Expected results to be published summer 2024. Conclusions The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. Trial Registration clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865 International Registered Report Identifier (IRRID) DERR1-10.2196/54593

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Section: Introductionmentioning

confidence: 99%

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial

Chomutare,

Lamproudis,

Budrionis

et al. 2024

JMIR Res Protoc

Self Cite

View full text Add to dashboard Cite

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Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial (Preprint)

Chomutare,

Lamproudis,

Budrionis

et al. 2023

Preprint

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BACKGROUND Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. OBJECTIVE The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. METHODS The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. RESULTS We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence–based CAC innovations to improve coding practice. Expected results to be published summer 2024. CONCLUSIONS The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. CLINICALTRIAL clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/54593

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GPT-Enhanced Hierarchical Deep Learning Model for Automated ICD Coding

Carberry,

2024

Adv. sci. technol. eng. syst. j.

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In healthcare, accurate communication is critical, and medical coding, especially coding using the ICD (International Classification of Diseases) standards, plays a vital role in achieving this accuracy. Traditionally, ICD coding has been a time-consuming manual process performed by trained professionals, involving the assignment of codes to patient records, such as doctor's notes. In this paper, we present an automated ICD coding approach using deep learning models and demonstrate the feasibility and effectiveness of the approach across subsets of ICD codes. The proposed method employs a fine-grained approach that individually predicts the appropriate medical code for each diagnosis. In order to utilize sufficient evidence to enhance the classification capabilities of our deep leaning models, we integrate GPT-4 to extract semantically related sentences for each diagnosis from doctor's notes. Furthermore, we introduce a hierarchical classifier to handle the large label space and complex classification inherent in the ICD coding task. This hierarchical approach decomposes the ICD coding task into smaller, more manageable subclassification tasks, thereby improving tractability and addressing the challenges posed by the high number of unique labels associated with ICD coding.

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Combining deep learning and fuzzy logic to predict rare ICD-10 codes from clinical notes

Cited by 3 publications

References 14 publications

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial (Preprint)

GPT-Enhanced Hierarchical Deep Learning Model for Automated ICD Coding

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