<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient’s clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines.</p>
</abstract>

The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient’s clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines.

Diagnostics Based Patient Classification for Clinical Decision Support Systems

SVKM’s, Narsee Monjee Institute of Management Studies

Choithram Interventional Spine & Pain Centre

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Diagnostics Based Patient Classification for Clinical Decision Support Systems"}

The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving...

Diagnostics Based Patient Classification for Clinical Decision Support Systems

Data publikacji: 23 cze 2024

Zakres stron: 83 - 96

Otrzymano: 28 gru 2022

Przyjęty: 13 cze 2023

DOI: https://doi.org/10.14313/jamris/2-2024/16

Słowa kluczowe
multiclass patient classification, multi-label patient classification, electronic healthcare records, MIMIC III, natural language processing, deep learning

© 2024 Gaurav Paliwal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Diagnostics Based Patient Classification for Clinical Decision Support Systems

Data publikacji: 23 cze 2024

Zakres stron: 83 - 96

Otrzymano: 28 gru 2022

Przyjęty: 13 cze 2023

DOI: https://doi.org/10.14313/jamris/2-2024/16

Słowa kluczowemulticlass patient classification, multi-label patient classification, electronic healthcare records, MIMIC III, natural language processing, deep learning

© 2024 Gaurav Paliwal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
multiclass patient classification, multi-label patient classification, electronic healthcare records, MIMIC III, natural language processing, deep learning