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In general, physicians make a preliminary diagnosis based on patients’ admission narratives and admission conditions, largely depending on their experiences and professional knowledge. An automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians, particularly in the shortage of medical resources. Despite its great value, little work has been conducted on this diagnosis method. Thus, in this study, we propose a fusion model that integrates the semantic and symptom features contained in the clinical text. The semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network. The symptom concepts, recognized from the input text, are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and diseases. Finally, two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases code. Model training and evaluation are performed on a public clinical dataset. The results show that both fusion strategies achieved a promising performance, in which the best performance obtained a top-3 accuracy of 0.7412.


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Fusion Model for Tentative Diagnosis Inference Based on Clinical Narratives

Show Author's information Ying Yu1,3Junwen Duan2( )Min Li2
School of Computer Science and Engineering, Central South University, Changsha 410083, China
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
School of Computer Science, University of South China, Hengyang 421001, China

Abstract

In general, physicians make a preliminary diagnosis based on patients’ admission narratives and admission conditions, largely depending on their experiences and professional knowledge. An automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians, particularly in the shortage of medical resources. Despite its great value, little work has been conducted on this diagnosis method. Thus, in this study, we propose a fusion model that integrates the semantic and symptom features contained in the clinical text. The semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network. The symptom concepts, recognized from the input text, are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and diseases. Finally, two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases code. Model training and evaluation are performed on a public clinical dataset. The results show that both fusion strategies achieved a promising performance, in which the best performance obtained a top-3 accuracy of 0.7412.

Keywords:

tentative diagnosis, clinical narrative, Bidirectional Long Short-Term Memory (BiLSTM), Term Frequency-Inverse Document Frequency (TF-IDF), fusion strategy
Received: 08 August 2022 Revised: 04 September 2022 Accepted: 10 October 2022 Published: 06 January 2023 Issue date: August 2023
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Publication history

Received: 08 August 2022
Revised: 04 September 2022
Accepted: 10 October 2022
Published: 06 January 2023
Issue date: August 2023

Copyright

© The author(s) 2023.

Acknowledgements

We thank the anonymous reviewers for their helpful comments. This work was supported in part by the Science and Technology Major Project of Changsha (No. kh2202004) and the National Natural Science Foundation of China (No. 62006251). We are grateful for resources from the High-Performance Computing Center of Central South University.

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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