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Artificial Intelligence in Healthcare and Health Services
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Artificial Intelligence in Healthcare and Health Services

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 9087

Special Issue Editors

Prof. Dr. Kyung-Eun (Anna) Choi

E-Mail Website
Guest Editor
1. Health Services Research Research Group, Research Center MIAAI (Medical Image Analysis & Artificial Intelligence), Faculty of Medicine/Dentistry, Danube Private University, 3500 Krems-Stein, Austria
2. Center of Health Services Research Brandenburg, Brandenburg Medical School Theodor Fontane, 16816 Neuruppin, Germany
Interests: health services research; work and health; rehabilitation; patient preferences; new technologies; diversity in medicine and inclusion of persons with disabilities; integrative medicine / integrative oncology; mixed-methods
Dr. Sepideh Hatamikia

E-Mail Website
Guest Editor
1. Computational Imaging, Research Center MIAAI (Medical Image Analysis & Artificial Intelligence), Faculty of Medicine/Dentistry, Danube Private University, Krems-Stein, Austria
2. Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria
Interests: artificial intelligence; machine learning; deep learning; computer-aided systems; medical image analysis; cancer research; pattern recognition, computer-based diagnostic systems; classification; image and signal processing, computer vision, supervised and unsupervised learning; statistical learning; biomarker feature extraction

Special Issue Information

Dear Colleagues,

The use of artificial intelligence (AI) in medicine is rapidly expanding and promises to initialize a revolution in patient care and the management of health services. Users’ reactions to the innovations in this area are two-fold: some reservations (e.g., data protection, limited transparency, and a lack of knowledge) are still slowing down the process of AI implementation, while various early adopters of many disciplines (e.g., radiology and surgical pathology) have been fast to find new solutions in diagnosis, prognosis, treatment, and management. AI innovations in the health domain promise to lead to more precise medical interventions and applications, improved health outcomes, and greater efficiency in management and allocation.

This Special Issue focuses on the current state of knowledge regarding the nexus of artificial intelligence application and health services research. Subthemes may include the use of aggregated big data, new eHealth applications, or the targeting of vulnerable groups via special assistive robotics, as well as quality and risk management. New original research papers, reviews, and case series, using quantitative, qualitative, or mixed methods, are welcome contributions. Methodological papers dealing with new theoretical approaches are also appreciated if they have a practical focus on healthcare improvement.

We welcome manuscripts from different disciplines, including medical technology, medicine, health services research, implementation science, psychology, behavioral sciences, epidemiology, sociology, medical computer science, biomedical engineering, bioinformatics, medical physics, and medical ethics.

Prof. Dr. Kyung-Eun (Anna) Choi
Dr. Sepideh Hatamikia
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. 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

  • artificial intelligence
  • machine learning
  • deep learning
  • medical technology
  • bioinformatics
  • medical computer-assisted systems
  • robotics
  • diagnosis
  • automation
  • management
  • prediction
  • quality management
  • reporting
  • virtual assistants
  • digital health
  • patient preferences
  • individualization

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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

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16 pages, 817 KiB  
Article
Medication Regimen Complexity Index Score at Admission as a Predictor of Inpatient Outcomes: A Machine Learning Approach
by Yves Paul Vincent Mbous, Todd Brothers and Mohammad A. Al-Mamun
Int. J. Environ. Res. Public Health 2023, 20(4), 3760; https://doi.org/10.3390/ijerph20043760 - 20 Feb 2023
Viewed by 2298
Abstract
Background: In the intensive care unit, traditional scoring systems use illness severity and/or organ failure to determine prognosis, and this usually rests on the patient’s condition at admission. In spite of the importance of medication reconciliation, the usefulness of home medication histories as [...] Read more.
Background: In the intensive care unit, traditional scoring systems use illness severity and/or organ failure to determine prognosis, and this usually rests on the patient’s condition at admission. In spite of the importance of medication reconciliation, the usefulness of home medication histories as predictors of clinical outcomes remains unexplored. Methods: A retrospective cohort study was conducted using the medical records of 322 intensive care unit (ICU) patients. The predictors of interest included the medication regimen complexity index (MRCI) at admission, the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Sequential Organ Failure Assessment (SOFA) score, or a combination thereof. Outcomes included mortality, length of stay, and the need for mechanical ventilation. Machine learning algorithms were used for outcome classification after correcting for class imbalances in the general population and across the racial continuum. Results: The home medication model could predict all clinical outcomes accurately 70% of the time. Among Whites, it improved to 80%, whereas among non-Whites it remained at 70%. The addition of SOFA and APACHE II yielded the best models among non-Whites and Whites, respectively. SHapley Additive exPlanations (SHAP) values showed that low MRCI scores were associated with reduced mortality and LOS, yet an increased need for mechanical ventilation. Conclusion: Home medication histories represent a viable addition to traditional predictors of health outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare and Health Services)
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17 pages, 681 KiB  
Article
Digital Case Manager—A Data-Driven Tool to Support Family Caregivers with Initial Guidance
by Paul Wunderlich, Frauke Wiegräbe and Helene Dörksen
Int. J. Environ. Res. Public Health 2023, 20(2), 1215; https://doi.org/10.3390/ijerph20021215 - 10 Jan 2023
Cited by 4 | Viewed by 2715
Abstract
Due to the demographic aging of society, the demand for skilled caregiving is increasing. However, the already existing shortage of professional caregivers will exacerbate in the future. As a result, family caregivers must shoulder a heavier share of the care burden. To ease [...] Read more.
Due to the demographic aging of society, the demand for skilled caregiving is increasing. However, the already existing shortage of professional caregivers will exacerbate in the future. As a result, family caregivers must shoulder a heavier share of the care burden. To ease the burden and promote a better work-life balance, we developed the Digital Case Manager. This tool uses machine learning algorithms to learn the relationship between a care situation and the next care steps and helps family caregivers balance their professional and private lives so that they are able to continue caring for their family members without sacrificing their own jobs and personal ambitions. The data for the machine learning model are generated by means of a questionnaire based on professional assessment instruments. We implemented a proof-of-concept of the Digital Case Manager and initial tests show promising results. It offers a quick and easy-to-use tool for family caregivers in the early stages of a care situation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare and Health Services)
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Review

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14 pages, 2188 KiB  
Review
Diagnostic Accuracy of Machine-Learning Models on Predicting Chemo-Brain in Breast Cancer Survivors Previously Treated with Chemotherapy: A Meta-Analysis
by Adina Turcu-Stiolica, Maria Bogdan, Elena Adriana Dumitrescu, Daniela Luminita Zob, Victor Gheorman, Madalina Aldea, Venera Cristina Dinescu, Mihaela-Simona Subtirelu, Dana-Lucia Stanculeanu, Daniel Sur and Cristian Virgil Lungulescu
Int. J. Environ. Res. Public Health 2022, 19(24), 16832; https://doi.org/10.3390/ijerph192416832 - 15 Dec 2022
Cited by 1 | Viewed by 3052
Abstract
We performed a meta-analysis of chemo-brain diagnostic, pooling sensitivities, and specificities in order to assess the accuracy of a machine-learning (ML) algorithm in breast cancer survivors previously treated with chemotherapy. We searched PubMed, Web of Science, and Scopus for eligible articles before 30 [...] Read more.
We performed a meta-analysis of chemo-brain diagnostic, pooling sensitivities, and specificities in order to assess the accuracy of a machine-learning (ML) algorithm in breast cancer survivors previously treated with chemotherapy. We searched PubMed, Web of Science, and Scopus for eligible articles before 30 September 2022. We identified three eligible studies from which we extracted seven ML algorithms. For our data, the χ2 tests demonstrated the homogeneity of the sensitivity’s models (χ2 = 7.6987, df = 6, p-value = 0.261) and the specificities of the ML models (χ2 = 3.0151, df = 6, p-value = 0.807). The pooled area under the curve (AUC) for the overall ML models in this study was 0.914 (95%CI: 0.891–0.939) and partial AUC (restricted to observed false positive rates and normalized) was 0.844 (95%CI: 0.80–0.889). Additionally, the pooled sensitivity and pooled specificity values were 0.81 (95% CI: 0.75–0.86) and 0.82 (95% CI: 0.76–0.86), respectively. From all included ML models, support vector machine demonstrated the best test performance. ML models represent a promising, reliable modality for chemo-brain prediction in breast cancer survivors previously treated with chemotherapy, demonstrating high accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare and Health Services)
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