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Artificial Intelligence for Sustainable Services and Applications

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Dear Colleagues,

We have recently seen rapid development of healthcare technologies, along with the extensive adoption of the Internet, mobile technologies, data analytics, and artificial intelligence (AI) in healthcare. These advancements have resulted in highly multi-disciplinary research in digital, mobile, and smart health, and have also led the move in the direction of more personalized care. Sustainability simultaneously has the ability to meet our present needs without compromising future generations meeting their own needs (Brundtland Commission 1987), and it covers the following three aspects: environmental, social, and economic. Furthermore, the healthcare industry has consumed a tremendous amount of energy and water, and has produced a considerable amount of waste. Thus, it is necessary for the healthcare industry to be more responsible by adopting sustainable practices in order to efficiently utilize its resources and minimize environmental impacts.

This Special Issue aims to cover recent advances in artificial intelligence (AI) for healthcare with a sustainability perspective in mind, from both academic researchers and industry developers. Any type of article aligned with the journal (original research, case study, technical report, short communication, and reviews) is welcome for this Special Issue. Topics of interests include, but are not limited to, the following:

Health informatics
Artificial intelligence in healthcare
Personalized healthcare
Incorporating sustainable practices in healthcare
Sustainable healthcare services and applications
Electronic and mobile health
Clinical decision support systems
IoT and big data in healthcare
Machine learning and deep learning in healthcare
Descriptive, diagnostic, predictive analytics in healthcare
Data security and privacy in healthcare
Machine learning to understand human behavior and well-being
New algorithms for medical and healthcare data analytics
Predictive analysis in personalized healthcare

Dr. Muhammad Syafrudin
Dr. Ganjar Alfian
Prof. Dr. Muhammad Anshari
Assoc. Prof. Dr. Tony Hadibarata
Guest Editors

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22 pages, 1960 KiB

Open AccessArticle

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

by Ming-Hsia Hsu, Chia-Mei Chen, Wang-Chuan Juang, Zheng-Xun Cai and Tsuang Kuo

Sustainability 2021, 13(21), 11909; https://doi.org/10.3390/su132111909 - 28 Oct 2021

Cited by 3 | Viewed by 1992

Abstract

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. Full article

(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services and Applications)

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