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Special Issue "Artificial Intelligence for Sustainable Services and Applications"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Health, Well-Being and Sustainability".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 1473

Special Issue Editors

Dr. Muhammad Syafrudin
E-Mail Website
Guest Editor
Department of Artificial Intelligence, Sejong University, Seoul, Korea
Interests: industrial AI; information systems; IoT; big data; health informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Ganjar Alfian
E-Mail Website
Guest Editor
Industrial Artificial Intelligence (AI) Research Center, Nano Information Technology Academy, Dongguk University, Seoul 04626, Korea
Interests: machine learning; RFID; Internet of Things; health informatics; carsharing service
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Anshari
E-Mail Website
Guest Editor
School of Business & Economics, Universiti Brunei Darussalam, Gadong BE1410, Brunei
Interests: business information systems; e-health and mobile health; digital business and social computing; big data in business; ICT in education; knowledge management
Special Issues, Collections and Topics in MDPI journals
Dr. Tony Hadibarata
E-Mail Website
Guest Editor
Department of Environmental Engineering, Curtin University Malaysia, Miri 98009, Malaysia
Interests: biodegradation; microbial processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • health informatics
  • artificial intelligence in medicine
  • sustainable healthcare
  • E-health
  • mobile health
  • clinical decision support systems
  • healthcare applications and services
  • IoT and big data in healthcare

Published Papers (1 paper)

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Research

Article
Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability
Sustainability 2021, 13(21), 11909; https://doi.org/10.3390/su132111909 - 28 Oct 2021
Cited by 1 | Viewed by 811
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 [...] Read more.
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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