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Artificial Intelligence in Health Care
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Artificial Intelligence in Health Care

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Communication and Informatics".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 15433

Special Issue Editor

Dr. Eloisa Vargiu

E-Mail Website
Guest Editor
Eurecat Centre Tecnòlogic, eHealth Unit, Barcelona, Spain
Interests: computer science; artificial intelligence; eHealth; integrated care; information retrieval and filtering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A Topical Collection on “Artificial Intelligence in Healthcare”, in the International Journal of Environmental Research and Public Health, is being organized. For detailed information on the journal, please refer you to the following link: https://www.mdpi.com/journal/ijerph.

Artificial intelligence (AI) is still a relatively new technology in healthcare, and its adoption remains low. Several studies have been done to show the efficacy of using AI in healthcare and medicine, but only few have been adopted in the real clinical practice. In fact, an active discussion is ongoing about whether AI will eventually replace human physicians in the future. Actually, AI solutions are aimed at gaining information, processing it, and giving a well-defined output to the end-user (the physician, the patient, or the caregiver). The final goal is to give support to physicians in their decisions, and to help patients and caregivers better manage their diseases and therapy.

This Topical Collection is open to the subject area of artificial intelligence applied to healthcare. The keywords listed below provide an outline of some of the possible areas of interest.

The following kinds of papers will be considered:

  • Contributed papers: detailed expositions of new research or applications.
  • Case studies: evaluative and descriptive reviews of existing solutions applied in practice, discussing the experience gained and lessons learnt from using or developing AI systems in the healthcare field.
  • Survey papers/tutorials.

Dr. Eloisa Vargiu
Guest Editor

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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

  • Clinical decision-support systems
  • Patient’s empowerment and self-management
  • Behavioral change
  • Activity recognition
  • Behavior recognition
  • Multi-agent systems in healthcare scenarios
  • Artificial intelligence for an ageing society
  • Deep learning applied to healthcare
  • Big data analytics in medicine
  • Image recognition in the field of medicine
  • Novel software architectures
  • Internet of things for healthcare solutions
  • Self-organizing, emerging, or bio-inspired systems in healthcare
  • Internet science
  • Robotics solutions for assisting living
  • Virtual nurse assistant
  • Assistive technology for cognition
  • Sensor-based monitoring systems
  • Continuous learning and navigation supports for in- and out-door systems
  • Industrial experiences in the application of the above techniques (e.g. case studies or benchmarking exercises)

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

14 pages, 1303 KiB  
Article
Prediction of Myopia in Adolescents through Machine Learning Methods
by Xu Yang, Guo Chen, Yunchong Qian, Yuhan Wang, Yisong Zhai, Debao Fan and Yang Xu
Int. J. Environ. Res. Public Health 2020, 17(2), 463; https://doi.org/10.3390/ijerph17020463 - 10 Jan 2020
Cited by 38 | Viewed by 5024
Abstract
According to literature, myopia has become the second most common eye disease in China, and the incidence of myopia is increasing year by year, and showing a trend of younger age. Previous researches have shown that the occurrence of myopia is mainly determined [...] Read more.
According to literature, myopia has become the second most common eye disease in China, and the incidence of myopia is increasing year by year, and showing a trend of younger age. Previous researches have shown that the occurrence of myopia is mainly determined by poor eye habits, including reading and writing posture, eye length, and so on, and parents’ heredity. In order to better prevent myopia in adolescents, this paper studies the influence of related factors on myopia incidence in adolescents based on machine learning method. A feature selection method based on both univariate correlation analysis and multivariate correlation analysis is used to better construct a feature sub-set for model training. A method based on GBRT is provided to help fill in missing items in the original data. The prediction model is built based on SVM model. Data transformation has been used to improve the prediction accuracy. Results show that our method could achieve reasonable performance and accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Care)
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21 pages, 1277 KiB  
Article
Cold Chain Logistics Management of Medicine with an Integrated Multi-Criteria Decision-Making Method
by Zhi Wen, Huchang Liao, Ruxue Ren, Chunguang Bai, Edmundas Kazimieras Zavadskas, Jurgita Antucheviciene and Abdullah Al-Barakati
Int. J. Environ. Res. Public Health 2019, 16(23), 4843; https://doi.org/10.3390/ijerph16234843 - 2 Dec 2019
Cited by 64 | Viewed by 6687
Abstract
Medicine is the main means to reduce cancer mortality. However, some medicines face various risks during transportation and storage due to the particularity of medicines, which must be kept at a low temperature to ensure their quality. In this regard, it is of [...] Read more.
Medicine is the main means to reduce cancer mortality. However, some medicines face various risks during transportation and storage due to the particularity of medicines, which must be kept at a low temperature to ensure their quality. In this regard, it is of great significance to evaluate and select drug cold chain logistics suppliers from different perspectives to ensure the quality of medicines and reduce the risks of transportation and storage. To solve such a multiple criteria decision-making (MCDM) problem, this paper proposes an integrated model based on the combination of the SWARA (stepwise weight assessment ratio analysis) and CoCoSo (combined compromise solution) methods under the probabilistic linguistic environment. An adjustment coefficient is introduced to the SWARA method to derive criteria weights, and an improved CoCoSo method is proposed to determine the ranking of alternatives. The two methods are extended to the probabilistic linguistic environment to enhance the applicability of the two methods. A case study on the selection of drug cold chain logistics suppliers is presented to demonstrate the applicability of the proposed integrated MCDM model. The advantages of the proposed methods are highlighted through comparative analyses. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Care)
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9 pages, 450 KiB  
Article
Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine
by Haewon Byeon
Int. J. Environ. Res. Public Health 2019, 16(21), 4269; https://doi.org/10.3390/ijerph16214269 - 3 Nov 2019
Cited by 13 | Viewed by 2818
Abstract
Background and Objectives: This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swallowing quality of elderly people’s lives in the future. [...] Read more.
Background and Objectives: This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swallowing quality of elderly people’s lives in the future. Methods and Material: This study sampled 142 elderly people equal to or older than 65 years old who were using a senior welfare center. The swallowing problem associated quality of life was defined by the swallowing quality-of-life (SWAL-QOL). In order to verify the predictive power of the model, this study compared the predictive power of the Gaussian function with that of a linear algorithm, polynomial algorithm, and a sigmoid algorithm. Results: A total of 33.9% of the subjects decreased in swallowing quality-of-life. The swallowing quality-of-life prediction model for the elderly, based on the SVM, showed both preventive factors and risk factors. Risk factors were denture use, experience of using aspiration in the past one month, being economically inactive, having a mean monthly household income <2 million KRW, being an elementary school graduate or below, female, 75 years old or older, living alone, requiring time for finishing one meal on average ≤15 min or ≥40 min, having depression, stress, and cognitive impairment. Conclusions: It is necessary to monitor the high-risk group constantly in order to maintain the swallowing quality-of-life in the elderly based on the prevention and risk factors associated with the swallowing quality-of-life derived from this prediction model. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Care)
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