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Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam
Overcrowding in hospitals in Vietnam has caused many disadvantages in receiving and treating patients. Especially at the stage of receiving and diagnosing procedures taking patients to the treatment departments in the hospital takes up much time. This study proposes a text-based disease diagnosis us...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184959/ https://www.ncbi.nlm.nih.gov/pubmed/37360317 http://dx.doi.org/10.1007/s41870-023-01283-x |
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author | Nguyen, Hai Thanh Dang Le, Khoa Dang Pham, Ngoc Huynh Le Hoang Tran, Chi |
author_facet | Nguyen, Hai Thanh Dang Le, Khoa Dang Pham, Ngoc Huynh Le Hoang Tran, Chi |
author_sort | Nguyen, Hai Thanh |
collection | PubMed |
description | Overcrowding in hospitals in Vietnam has caused many disadvantages in receiving and treating patients. Especially at the stage of receiving and diagnosing procedures taking patients to the treatment departments in the hospital takes up much time. This study proposes a text-based disease diagnosis using text processing techniques (such as Bag of Words, Term Frequency- Inverse Document Frequency, and Tokenizer) combined with classifiers (such as Random Forests (RF), Multi-Layer Perceptron (MLP), Embeddings and Bidirectional Long Short-term memory (LSTM)) on symptoms. As observed from the results, deep Bidirectional LSTM can reach 0.982 in AUC in the classification of 10 diseases on 230,457 samples of pre-diagnosis collected from Vietnam hospitals used in the training and testing phases. The proposed approach is expected to provide a way to automate patient flow in hospitals to improve healthcare in the future. |
format | Online Article Text |
id | pubmed-10184959 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-101849592023-05-16 Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam Nguyen, Hai Thanh Dang Le, Khoa Dang Pham, Ngoc Huynh Le Hoang Tran, Chi Int J Inf Technol Original Research Overcrowding in hospitals in Vietnam has caused many disadvantages in receiving and treating patients. Especially at the stage of receiving and diagnosing procedures taking patients to the treatment departments in the hospital takes up much time. This study proposes a text-based disease diagnosis using text processing techniques (such as Bag of Words, Term Frequency- Inverse Document Frequency, and Tokenizer) combined with classifiers (such as Random Forests (RF), Multi-Layer Perceptron (MLP), Embeddings and Bidirectional Long Short-term memory (LSTM)) on symptoms. As observed from the results, deep Bidirectional LSTM can reach 0.982 in AUC in the classification of 10 diseases on 230,457 samples of pre-diagnosis collected from Vietnam hospitals used in the training and testing phases. The proposed approach is expected to provide a way to automate patient flow in hospitals to improve healthcare in the future. Springer Nature Singapore 2023-05-15 /pmc/articles/PMC10184959/ /pubmed/37360317 http://dx.doi.org/10.1007/s41870-023-01283-x Text en © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Nguyen, Hai Thanh Dang Le, Khoa Dang Pham, Ngoc Huynh Le Hoang Tran, Chi Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam |
title | Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam |
title_full | Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam |
title_fullStr | Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam |
title_full_unstemmed | Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam |
title_short | Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam |
title_sort | deep bidirectional lstm for disease classification supporting hospital admission based on pre-diagnosis: a case study in vietnam |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184959/ https://www.ncbi.nlm.nih.gov/pubmed/37360317 http://dx.doi.org/10.1007/s41870-023-01283-x |
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