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Multimodal temporal-clinical note network for mortality prediction

BACKGROUND: Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and customize early intervention programs for the patients in need. With the develo...

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Detalles Bibliográficos
Autores principales: Yang, Haiyang, Kuang, Li, Xia, FengQiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885612/
https://www.ncbi.nlm.nih.gov/pubmed/33588949
http://dx.doi.org/10.1186/s13326-021-00235-3
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author Yang, Haiyang
Kuang, Li
Xia, FengQiang
author_facet Yang, Haiyang
Kuang, Li
Xia, FengQiang
author_sort Yang, Haiyang
collection PubMed
description BACKGROUND: Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and customize early intervention programs for the patients in need. With the development of the electronic medical records, deep learning methods are introduced to deal with the prediction task. In the electronic medical records, clinical notes always contain rich and diverse medical information, including the clinical histories and reports during admission. Mortality prediction methods mostly rely on the temporal events such as medical examinations and ignore the related reports and history information in the clinical notes. We hope that we can utilize both temporal events and clinical notes information to get better mortality prediction results. RESULTS: We propose a multimodal temporal-clinical note network to model both temporal and clinical notes. Specifically, the clinical text are further processed for differentiating the chronic illness patients in the historical information of clinical notes from non-chronic illness patients. In order to further mine the information related to the mortality in the text, we learn the time series embedding with Long Short Term Memory networks and the clinical notes embedding with a label aware convolutional neural network. We also propose a scoring function to measure the importance of clinical note sections. Our approach achieved a better AUCPR and AUCROC than competing methods and visual explanations for word importance showed the interpretability improvement of the model. CONCLUSIONS: We have tested our methodology on the MIMIC-III dataset. Contributions of different clinical note sections were uncovered by visualization methods. Our work demonstrates that the introduction of the medical history related information can improve the performance of the mortality prediction. Using label aware convolutional neural networks can further improve the results.
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spelling pubmed-78856122021-02-22 Multimodal temporal-clinical note network for mortality prediction Yang, Haiyang Kuang, Li Xia, FengQiang J Biomed Semantics Research BACKGROUND: Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and customize early intervention programs for the patients in need. With the development of the electronic medical records, deep learning methods are introduced to deal with the prediction task. In the electronic medical records, clinical notes always contain rich and diverse medical information, including the clinical histories and reports during admission. Mortality prediction methods mostly rely on the temporal events such as medical examinations and ignore the related reports and history information in the clinical notes. We hope that we can utilize both temporal events and clinical notes information to get better mortality prediction results. RESULTS: We propose a multimodal temporal-clinical note network to model both temporal and clinical notes. Specifically, the clinical text are further processed for differentiating the chronic illness patients in the historical information of clinical notes from non-chronic illness patients. In order to further mine the information related to the mortality in the text, we learn the time series embedding with Long Short Term Memory networks and the clinical notes embedding with a label aware convolutional neural network. We also propose a scoring function to measure the importance of clinical note sections. Our approach achieved a better AUCPR and AUCROC than competing methods and visual explanations for word importance showed the interpretability improvement of the model. CONCLUSIONS: We have tested our methodology on the MIMIC-III dataset. Contributions of different clinical note sections were uncovered by visualization methods. Our work demonstrates that the introduction of the medical history related information can improve the performance of the mortality prediction. Using label aware convolutional neural networks can further improve the results. BioMed Central 2021-02-15 /pmc/articles/PMC7885612/ /pubmed/33588949 http://dx.doi.org/10.1186/s13326-021-00235-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Yang, Haiyang
Kuang, Li
Xia, FengQiang
Multimodal temporal-clinical note network for mortality prediction
title Multimodal temporal-clinical note network for mortality prediction
title_full Multimodal temporal-clinical note network for mortality prediction
title_fullStr Multimodal temporal-clinical note network for mortality prediction
title_full_unstemmed Multimodal temporal-clinical note network for mortality prediction
title_short Multimodal temporal-clinical note network for mortality prediction
title_sort multimodal temporal-clinical note network for mortality prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885612/
https://www.ncbi.nlm.nih.gov/pubmed/33588949
http://dx.doi.org/10.1186/s13326-021-00235-3
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AT kuangli multimodaltemporalclinicalnotenetworkformortalityprediction
AT xiafengqiang multimodaltemporalclinicalnotenetworkformortalityprediction