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Open Access

Fusion Model for Tentative Diagnosis Inference Based on Clinical Narratives

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
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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 diagnosisclinical narrativeBidirectional Long Short-Term Memory (BiLSTM)Term Frequency-Inverse Document Frequency (TF-IDF)fusion strategy

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Tsinghua Science and Technology
Volume 28 Issue 4,
August 2023
Pages 686-695
DOI: 10.26599/TST.2022.9010049
Cite this article:
Yu Y, Duan J, Li M. Fusion Model for Tentative Diagnosis Inference Based on Clinical Narratives. Tsinghua Science and Technology, 2023, 28(4): 686-695. https://doi.org/10.26599/TST.2022.9010049

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Received: 08 August 2022
Revised: 04 September 2022
Accepted: 10 October 2022
Published: 06 January 2023
© The author(s) 2023.

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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