Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a syn...

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Autores principales:	Ming-Hsia Hsu, Chia-Mei Chen, Wang-Chuan Juang, Zheng-Xun Cai, Tsuang Kuo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning in healthcare personalized healthcare big data analysis recurrent neural networks Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/bc64a0ada52a41ec9ee29b7542662d38
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spelling	oai:doaj.org-article:bc64a0ada52a41ec9ee29b7542662d382021-11-11T19:36:00ZAnalyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability10.3390/su1321119092071-1050https://doaj.org/article/bc64a0ada52a41ec9ee29b7542662d382021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11909https://doaj.org/toc/2071-1050The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a synonym for complete patient care to respond to a patient’s physical, emotional, social, economic, and spiritual needs, and, as such, an efficient prediction system for comprehensive care suggestions could help physicians and healthcare providers in making clinical judgement. The experiment dataset contained a total of 2.9 million electrical medical records (EMRs) from 250 thousand hospitalized patients collected retrospectively from a first-tier medical center in Taiwan, where the EMRs were de-identified and anonymized and where 949 cases had received comprehensive care. Recurrent neural networks (RNNs) are designed for analyzing time-series data but are still lacking in studying predicting personalized healthcare. Furthermore, in most cases, the collected evaluation data are imbalanced with a small portion of positive cases. This study examined the impact of imbalanced data in model training and suggested an effective approach to handle such a situation. To address the above-mentioned research issue, this study analyzed the care need in the different patient groupings, proposed a personalized care suggestion system by applying RNN models, and developed an efficient model training scheme for building AI-assisted prediction models. This study observed several findings: (1) the data resampling schemes could mitigate the impact of imbalanced data on model training, and the under-sampling scheme achieved the best performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602, while the model trained with the original data had a very low PPV of 6.42% and a low F1 score of 0.1116; (2) patient clustering with multi-classier could predict comprehensive care needs efficiently with an ACC of 99.87%, a PPV of 77.90%, an NPV of 99.90%, a recall of 92.19%, and an F1 score of 0.8404; (3) the proposed long short-term memory (LSTM) prediction model achieved the best overall performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602.Ming-Hsia HsuChia-Mei ChenWang-Chuan JuangZheng-Xun CaiTsuang KuoMDPI AGarticledeep learning in healthcarepersonalized healthcarebig data analysisrecurrent neural networksEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11909, p 11909 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning in healthcare personalized healthcare big data analysis recurrent neural networks Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	deep learning in healthcare personalized healthcare big data analysis recurrent neural networks Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Ming-Hsia Hsu Chia-Mei Chen Wang-Chuan Juang Zheng-Xun Cai Tsuang Kuo Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability
description	The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a synonym for complete patient care to respond to a patient’s physical, emotional, social, economic, and spiritual needs, and, as such, an efficient prediction system for comprehensive care suggestions could help physicians and healthcare providers in making clinical judgement. The experiment dataset contained a total of 2.9 million electrical medical records (EMRs) from 250 thousand hospitalized patients collected retrospectively from a first-tier medical center in Taiwan, where the EMRs were de-identified and anonymized and where 949 cases had received comprehensive care. Recurrent neural networks (RNNs) are designed for analyzing time-series data but are still lacking in studying predicting personalized healthcare. Furthermore, in most cases, the collected evaluation data are imbalanced with a small portion of positive cases. This study examined the impact of imbalanced data in model training and suggested an effective approach to handle such a situation. To address the above-mentioned research issue, this study analyzed the care need in the different patient groupings, proposed a personalized care suggestion system by applying RNN models, and developed an efficient model training scheme for building AI-assisted prediction models. This study observed several findings: (1) the data resampling schemes could mitigate the impact of imbalanced data on model training, and the under-sampling scheme achieved the best performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602, while the model trained with the original data had a very low PPV of 6.42% and a low F1 score of 0.1116; (2) patient clustering with multi-classier could predict comprehensive care needs efficiently with an ACC of 99.87%, a PPV of 77.90%, an NPV of 99.90%, a recall of 92.19%, and an F1 score of 0.8404; (3) the proposed long short-term memory (LSTM) prediction model achieved the best overall performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602.
format	article
author	Ming-Hsia Hsu Chia-Mei Chen Wang-Chuan Juang Zheng-Xun Cai Tsuang Kuo
author_facet	Ming-Hsia Hsu Chia-Mei Chen Wang-Chuan Juang Zheng-Xun Cai Tsuang Kuo
author_sort	Ming-Hsia Hsu
title	Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability
title_short	Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability
title_full	Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability
title_fullStr	Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability
title_full_unstemmed	Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability
title_sort	analyzing groups of inpatients’ healthcare needs to improve service quality and sustainability
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bc64a0ada52a41ec9ee29b7542662d38
work_keys_str_mv	AT minghsiahsu analyzinggroupsofinpatientshealthcareneedstoimproveservicequalityandsustainability AT chiameichen analyzinggroupsofinpatientshealthcareneedstoimproveservicequalityandsustainability AT wangchuanjuang analyzinggroupsofinpatientshealthcareneedstoimproveservicequalityandsustainability AT zhengxuncai analyzinggroupsofinpatientshealthcareneedstoimproveservicequalityandsustainability AT tsuangkuo analyzinggroupsofinpatientshealthcareneedstoimproveservicequalityandsustainability
_version_	1718431489538916352

Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

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