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Tsinghua Science and Technology 2023, 28(1): 117-130 https://doi.org/10.26599/TST.2021.9010093
Open Access | Issue | Published: 21 July 2022

KTI-RNN: Recognition of Heart Failure from Clinical Notes

Show Author's Information Dengao Li1( ),  Huiting Ma1,  Wenjing Li2,  Baofeng Zhao3,  Jumin Zhao4,  Yi Liu4,  Jian Fu1
College of Data Science, Taiyuan University of Technology, Jinzhong 030600, China
Department of Statistics and Applied Probability, University of California, Santa Barbara, CA 93106, USA
College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China
Keywords:
deep learning, diagnosis, heart failure, text classification
Cite this article:
Li D, Ma H, Li W, et al. KTI-RNN: Recognition of Heart Failure from Clinical Notes. Tsinghua Science and Technology, 2023, 28(1): 117-130. https://doi.org/10.26599/TST.2021.9010093
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Abstract Full text About this article

Although deep learning methods have recently attracted considerable attention in the medical field, analyzing large-scale electronic health record data is still a difficult task. In particular, the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment decisions. This study uses data from the Medical Information Mart for Intensive Care database. Compared with structured data, unstructured data contain abundant patient information. However, this type of data has unsatisfactory characteristics, e.g., many colloquial vocabularies and sparse content. To solve these problems, we propose the KTI-RNN model for unstructured data recognition. The proposed model overcomes sparse content and obtains good classification results. The term frequency-inverse word frequency (TF-IWF) model is used to extract the keyword set. The latent dirichlet allocation (LDA) model is adopted to extract the topic word set. These models enable the expansion of the medical record text content. Finally, we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network (BiRNN) model and the output layer. We call it gated-attention-BiRNN (GA-BiRNN) and use it to identify heart failure from extensive medical texts. Results show that the F⁢1 score of the proposed KTI-RNN model is 85.57%, and the accuracy rate of the proposed KTI-RNN model is 85.59%.


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About this article

KTI-RNN: Recognition of Heart Failure from Clinical Notes

Show Author's information Dengao Li1( ), Huiting Ma1, Wenjing Li2, Baofeng Zhao3, Jumin Zhao4, Yi Liu4, Jian Fu1
College of Data Science, Taiyuan University of Technology, Jinzhong 030600, China
Department of Statistics and Applied Probability, University of California, Santa Barbara, CA 93106, USA
College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China

Abstract

Although deep learning methods have recently attracted considerable attention in the medical field, analyzing large-scale electronic health record data is still a difficult task. In particular, the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment decisions. This study uses data from the Medical Information Mart for Intensive Care database. Compared with structured data, unstructured data contain abundant patient information. However, this type of data has unsatisfactory characteristics, e.g., many colloquial vocabularies and sparse content. To solve these problems, we propose the KTI-RNN model for unstructured data recognition. The proposed model overcomes sparse content and obtains good classification results. The term frequency-inverse word frequency (TF-IWF) model is used to extract the keyword set. The latent dirichlet allocation (LDA) model is adopted to extract the topic word set. These models enable the expansion of the medical record text content. Finally, we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network (BiRNN) model and the output layer. We call it gated-attention-BiRNN (GA-BiRNN) and use it to identify heart failure from extensive medical texts. Results show that the F⁢1 score of the proposed KTI-RNN model is 85.57%, and the accuracy rate of the proposed KTI-RNN model is 85.59%.

Keywords: deep learning, diagnosis, heart failure, text classification

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Publication history

Received: 08 November 2021
Accepted: 22 November 2021
Published: 21 July 2022
Issue date: February 2023

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© The author(s) 2023.

Acknowledgements

This work was supported by the National Major Scientific Research Instrument Development Project (No. 62027819): High-Speed Real-Time Analyzer for Laser Chip’s Optical Catastrophic Damage Process; the General Object of the National Natural Science Foundation (No. 62076177): Study on the Risk Assessment Model of Heart Failure by Integrating Multi-Modal Big Data; and Shanxi Province Key Technology and Generic Technology R&D Project (No. 2020XXX007): Energy Internet Integrated Intelligent Data Management and Decision Support Platform.

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