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Medicina
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Artificial Intelligence Research in Healthcare
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Artificial Intelligence Research in Healthcare

A special issue of Medicina (ISSN 1648-9144).

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 22509

Special Issue Editors

Dr. Thomas T. H. Wan

E-Mail Website
Guest Editor
School of Global Health Management and Informatics, University of Central Florida, Orlando, FL 32816, USA
Interests: health informatics; decision analysis and modeling; health service outcomes and evaluation; artificial intelligence in healthcare; population health management; patient-centric care management for chronic conditions; shared decision making in healthcare; aging and health; evidence-based healthcare management
Special Issues, Collections and Topics in MDPI journals
Dr. Varadraj Gurupur

E-Mail Website
Co-Guest Editor
Department of Health Management and Informatics, University of Central Florida, Orlando, FL, USA
Interests: healthcare informatics; decision support systems; artificial intelligence; semantic networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human factors play an important role in healing design and process, particularly for chronic conditions. Systematic review and meta analysis of clinical trial studies on reducing hospitalization and readmission can yield valuable information or positive proofs of the beneficial effect of specific care management strategies that may alter patients’ knowledge, motivation, attitude, preventive practice and outcomes.

The primary aim of this special issue on Artificial Intelligence Research and Applications in Healthcare is twofold: 1) identification of the transdisciplinary convergence in theoretical formulations for accounting for the variability in healthcare outcomes; and 2) demonstration of empirical or methodological approaches to AI applications. Interventions with human factor principles reduce readmissions among patients can be designed to reduce the risk for readmissions and to improve better outcomes.

Ultimately, this special issue may direct the future research direction on AI applications in healthcare and help reconfigure the design, implementation, and evaluation of clinical practice for prevention, diagnosis, treatment and rehabilitation of chronic illnesses at the patient and population levels.

Dr. Thomas Wan
Prof. Varadraj Prabhu Gurupur
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. Medicina 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 2200 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

  • chronic care management
  • patient-centered care
  • AI applications
  • systematic review and meta analysis
  • convergence research
  • AI methods or techniques
  • outcomes research
  • population health management
  • transdisciplinary approaches
  • software design
  • care modalities
  • health informatics and evaluation

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Published Papers (6 papers)

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Research

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9 pages, 497 KiB  
Article
The Combined Effects of Sports Smart Bracelet and Multi-Component Exercise Program on Exercise Motivation among the Elderly in Macau
by Cheuk Kei Lao, Bing Long Wang, Richard S. Wang and Hsiao Yun Chang
Medicina 2021, 57(1), 34; https://doi.org/10.3390/medicina57010034 - 2 Jan 2021
Cited by 10 | Viewed by 4663
Abstract
Background and objectives: Faced with the serious problem of an aging population, exercise is one of the most effective ways to maintain the health of the elderly. In recent years, with the popularization of smartphones, the elderly have increasingly accepted technological products that [...] Read more.
Background and objectives: Faced with the serious problem of an aging population, exercise is one of the most effective ways to maintain the health of the elderly. In recent years, with the popularization of smartphones, the elderly have increasingly accepted technological products that incorporate artificial intelligence (AI). However, there is not much research on using artificial intelligence bracelets to enhance elders’ motivation and participation in exercise. Therefore, the purpose of this study is to evaluate the effectiveness of the combination of sports smart bracelets and multi-sport training programs on the motivation of the elderly in Macau. Materials and Methods: The study was conducted with a randomized trial design in a 12 week multi-sport exercise training intervention. According to the evaluation, a total of sixty elders’ pre- and post-test data were included in this study. Results: After 12 weeks of multi-sport exercise training, the evaluation scores on the exercise motivation scale (EMS) increased significantly in the group wearing exercise bracelets and those taking part in the multi-component exercise program, and the degree of progress reached a statistically significant level, but the control group did not show any statistically significant difference. The influence of the combination of sports smart bracelets and multi-sport training programs on elders’ motivation is clearer. Conclusions: The use of sports smart bracelets by elderly people in conjunction with diverse exercise training can effectively enhance elders’ motivation and increase their participation in regular exercise. The combination of sports smart bracelets and multi-sport training programs is worth promoting in the elderly population. Full article
(This article belongs to the Special Issue Artificial Intelligence Research in Healthcare)
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10 pages, 496 KiB  
Article
Predicting Old-age Mortality Using Principal Component Analysis: Results from a National Panel Survey in Korea
by Jaeyong Shin, Kwang-Soo Lee and Jae-Hyun Kim
Medicina 2020, 56(7), 360; https://doi.org/10.3390/medicina56070360 - 18 Jul 2020
Cited by 5 | Viewed by 2459
Abstract
Background and Objectives: This study aimed to group diseases classified by the International Classification of Diseases using principal component analysis, and discuss a systematic approach to reducing the preventable death rate from a perspective of public health. Materials and Methods: Using a 10-year [...] Read more.
Background and Objectives: This study aimed to group diseases classified by the International Classification of Diseases using principal component analysis, and discuss a systematic approach to reducing the preventable death rate from a perspective of public health. Materials and Methods: Using a 10-year follow-up analysis of the Korean Longitudinal Study of Aging (KLoSA) data, this study obtained de-identified data including participants’ data of community-dwelling individuals aged ≥45 years from 2006 to 2016. Participants were randomly selected using a multistage, stratified probability sampling based on geographical area and housing type. We excluded 37 participants with missing information at baseline and included 10,217 study participants. This study used the principal component analysis to extract comorbidity patterns, and chi-square test and Cox proportional hazards models for analyzing the association between the factors of interest. Results: Principal component 1 (diabetes, heart disease, and hypertension) was associated with an increased hazard ratio (HR) of 1.079 (95% confidence interval (CI) 1.031–1.129, p = 0.001). Principal component 3 (psychiatric and cerebrovascular diseases) was related to an increased HR of 1.134 (95% CI 1.094–1.175, p < 0.0001). Moreover, principal component 4 was associated with a high HR of 1.172 (95% CI 1.130–1.215, p < 0.0001). However, among participants aged between 45 and 64 years, principal component 4 showed a meaningfully increased HR of 1.262 (95% CI 1.184–1.346, p < 0.001). In this study, among the four principal components, three were statistically associated with increased mortality. Conclusions: The principal component analysis for predicting mortality may become a useful tool, and artificial intelligence (AI) will improve a value-based healthcare strategy, along with developing a clinical decision support model. Full article
(This article belongs to the Special Issue Artificial Intelligence Research in Healthcare)
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8 pages, 1446 KiB  
Article
Combination of a Self-Regulation Module and Mobile Application to Enhance Treatment Outcome for Patients with Acne
by Yi-Shan Liu, Nan-Han Lu, Po-Chuen Shieh and Cheuk-Kwan Sun
Medicina 2020, 56(6), 276; https://doi.org/10.3390/medicina56060276 - 4 Jun 2020
Cited by 8 | Viewed by 2764
Abstract
Background and Objectives: Acne, an inflammatory disorder of the pilosebaceous unit associated with both physiological and psychological morbidities, should be considered a chronic disease. The application of self-regulation theory and therapeutic patient education has been widely utilized in different health-related areas to help [...] Read more.
Background and Objectives: Acne, an inflammatory disorder of the pilosebaceous unit associated with both physiological and psychological morbidities, should be considered a chronic disease. The application of self-regulation theory and therapeutic patient education has been widely utilized in different health-related areas to help patient with a chronic disease to attain better behavioral modification. The present study aims at investigating the treatment efficacy of combining a self-regulation-based patient education module with mobile application in acne patients. Materials and Methods: This was one-grouped pretest–posttest design at a single tertiary referral center with the enrollment of 30 subjects diagnosed with acne vulgaris. Relevant information was collected before (week 0) and after (week 4) treatment in the present study, including the Acne Self-Regulation Inventory (ASRI), Cardiff Acne Disability Index (CADI), and Dermatology Life Quality Index (DLQI) that involved a questionnaire-based subjective evaluation of the patient’s ability in self-regulation and quality of life as well as clinical Acne Grading Scores (AGS) that objectively assessed changes in disease severity. To reinforce availability and feasibility, an individualized platform was accessible through mobile devices for real-time problem solving between hospital visits. Results: Thirty subjects completed the designed experiment. An analysis of the differences between scores of pretest and posttest of ASRI demonstrated substantial elevations (p < 0.001). The questionnaire survey of CADI and DLQI dropped significantly after the application of a self-regulation-based patient education module with a mobile application, revealing substantial reductions in both parameters (p < 0.001). The sign test demonstrated a remarkably significant difference in AGS (Z = −7.38, p < 0.001), indicating notable improvement in the clinical severity of acne after treatment. Conclusions: After incorporating modern mobile application, a self-regulation-based therapeutic patient education module could significantly improve treatment outcomes among acne patients. Full article
(This article belongs to the Special Issue Artificial Intelligence Research in Healthcare)
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17 pages, 6934 KiB  
Article
Stress in the Volunteer Caregiver: Human-Centric Technology Can Support Both Caregivers and People with Dementia
by Barbara Huelat and Sharon T. Pochron
Medicina 2020, 56(6), 257; https://doi.org/10.3390/medicina56060257 - 26 May 2020
Cited by 8 | Viewed by 3730
Abstract
Background and Objectives: Currently, one in eight people over the age of 65 have dementia, and approximately 75% of caregiving is provided by volunteer family members with little or no training. This study aimed to quantify points of stress for home-based caregivers [...] Read more.
Background and Objectives: Currently, one in eight people over the age of 65 have dementia, and approximately 75% of caregiving is provided by volunteer family members with little or no training. This study aimed to quantify points of stress for home-based caregivers with the aim of reducing stress for them while concurrently supporting quality of life for the people with dementia whom they cared for. The overreaching purpose was to increase our knowledge of the caregiver stress burden and explore potential technologies and behaviors to ease it. Materials and Methods: We interviewed home-based and professional caregivers regarding causes of emotional and physical stress and methods they used to alleviate it. Results: This study found that: (1) dementia symptoms created a burden of stress for home-based caregivers primarily in the areas of medication management, memory loss, hygiene care and disruptive behaviors; (2) home-based caregivers identified “finding available resources” as the most important source of stress relief; (3) a minority of home-based caregivers possessed a resource network and knew how to find resources but all professional caregivers were able to find resources and support; (4) home-based caregivers combated dementia symptoms with positive distractions and human touch with little use of technology, since it was mostly unknown; and (5) facility-based caregivers were knowledgeable and readily used dementia-based technology. Conclusion: Since professional caregivers have access to technological resources that our home-based caregivers lack, one might logically conclude that we should transfer technology used by professionals to those with dementia. However, great caution needs to be in place before we take that step. Successful technology should address the human experience as home-based caregivers try to use new technologies. Human-centric technology addresses the needs of both people with dementia and the home-based caregiver. Full article
(This article belongs to the Special Issue Artificial Intelligence Research in Healthcare)
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10 pages, 298 KiB  
Article
Artificial Neural Network and Cox Regression Models for Predicting Mortality after Hip Fracture Surgery: A Population-Based Comparison
by Cheng-Yen Chen, Yu-Fu Chen, Hong-Yaw Chen, Chen-Tsung Hung and Hon-Yi Shi
Medicina 2020, 56(5), 243; https://doi.org/10.3390/medicina56050243 - 19 May 2020
Cited by 15 | Viewed by 2848
Abstract
This study purposed to validate the accuracy of an artificial neural network (ANN) model for predicting the mortality after hip fracture surgery during the study period, and to compare performance indices between the ANN model and a Cox regression model. A total of [...] Read more.
This study purposed to validate the accuracy of an artificial neural network (ANN) model for predicting the mortality after hip fracture surgery during the study period, and to compare performance indices between the ANN model and a Cox regression model. A total of 10,534 hip fracture surgery patients during 1996–2010 were recruited in the study. Three datasets were used: a training dataset (n = 7374) was used for model development, a testing dataset (n = 1580) was used for internal validation, and a validation dataset (1580) was used for external validation. Global sensitivity analysis also was performed to evaluate the relative importances of input predictors in the ANN model. Mortality after hip fracture surgery was significantly associated with referral system, age, gender, urbanization of residence area, socioeconomic status, Charlson comorbidity index (CCI) score, intracapsular fracture, hospital volume, and surgeon volume (p < 0.05). For predicting mortality after hip fracture surgery, the ANN model had higher prediction accuracy and overall performance indices compared to the Cox model. Global sensitivity analysis of the ANN model showed that the referral to lower-level medical institutions was the most important variable affecting mortality, followed by surgeon volume, hospital volume, and CCI score. Compared with the Cox regression model, the ANN model was more accurate in predicting postoperative mortality after a hip fracture. The forecasting predictors associated with postoperative mortality identified in this study can also bae used to educate candidates for hip fracture surgery with respect to the course of recovery and health outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence Research in Healthcare)

Review

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5 pages, 231 KiB  
Review
Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare
by Varadraj Gurupur and Thomas T. H. Wan
Medicina 2020, 56(3), 141; https://doi.org/10.3390/medicina56030141 - 20 Mar 2020
Cited by 24 | Viewed by 5191
Abstract
The objective of this article is to discuss the inherent bias involved with artificial intelligence-based decision support systems for healthcare. In this article, the authors describe some relevant work published in this area. A proposed overview of solutions is also presented. The authors [...] Read more.
The objective of this article is to discuss the inherent bias involved with artificial intelligence-based decision support systems for healthcare. In this article, the authors describe some relevant work published in this area. A proposed overview of solutions is also presented. The authors believe that the information presented in this article will enhance the readers’ understanding of this inherent bias and add to the discussion on this topic. Finally, the authors discuss an overview of the need to implement transdisciplinary solutions that can be used to mitigate this bias. Full article
(This article belongs to the Special Issue Artificial Intelligence Research in Healthcare)
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