The 10th Anniversary of Healthcare—Health Informatics and Big Data

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1779

Special Issue Editors


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Guest Editor
Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: public health; health informatics; chronic disease management; e-health; aged care

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Co-Guest Editor
Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA
Interests: health Informatics; health Information systems; medical standards and ontologies; ontology based data integration; ontology guided machine learning; databases and data warehousing

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Co-Guest Editor
Department of Public Health Sciences, College of Behavioral, Social, and Health Sciences, Clemson University, Clemson, SC 29634, USA
Interests: clinical decision support systems; data-driven hypothesis generation & visual analytics; evidence-based clinical decision making; health IT for rural and under-served populations knowledge bases; knowledge

Special Issue Information

Dear Colleagues,

As we reach the the 10th anniversary of Healthcare (ISSN 2227-9032), we extend a warm invitation for you to join us to celebrate this momentous occasion. Healthcare is an international, scientific, peer-reviewed, open-access journal on health care systems, industry, technology, policy, and regulation. It is published online by MDPI, Basel, Switzerland. Its inaugural Issue was released in 2013; in 2021, we published the 1000th paper in this journal. Healthcare is indexed within Scopus, SCIE and SSCI (Web of Science), PubMed, PMC, and other databases. The journal achieved the Impact Factor of 3.160, and is ranked 50/109 (Q2) in “HEALTH CARE SCIENCES & SERVICES” and 35/88 (Q2) in “HEALTH POLICY & SERVICES ” in Web of Science.

To celebrate this significant milestone, we are launching a Special Issue entitled “The 10th Anniversary of Healthcare—Health Informatics and Big Data’. This Special Issue will welcome high-quality papers reporting topics within the broad scope of digital innovation in healthcare. It is our pleasure to invite you to help shape it by contributing an original research paper or a comprehensive review article for peer review and possible publication in Healthcare

Prof. Dr. Ping Yu
Dr. Hua Min
Dr. Xia Jing
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. Healthcare 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 2700 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.

Published Papers (2 papers)

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Research

21 pages, 5890 KiB  
Article
Doctor’s Orders—Why Radiologists Should Consider Adjusting Commercial Machine Learning Applications in Chest Radiography to Fit Their Specific Needs
by Frank Philipp Schweikhard, Anika Kosanke, Sandra Lange, Marie-Luise Kromrey, Fiona Mankertz, Julie Gamain, Michael Kirsch, Britta Rosenberg and Norbert Hosten
Healthcare 2024, 12(7), 706; https://doi.org/10.3390/healthcare12070706 - 23 Mar 2024
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Abstract
This retrospective study evaluated a commercial deep learning (DL) software for chest radiographs and explored its performance in different scenarios. A total of 477 patients (284 male, 193 female, mean age 61.4 (44.7–78.1) years) were included. For the reference standard, two radiologists performed [...] Read more.
This retrospective study evaluated a commercial deep learning (DL) software for chest radiographs and explored its performance in different scenarios. A total of 477 patients (284 male, 193 female, mean age 61.4 (44.7–78.1) years) were included. For the reference standard, two radiologists performed independent readings on seven diseases, thus reporting 226 findings in 167 patients. An autonomous DL reading was performed separately and evaluated against the gold standard regarding accuracy, sensitivity and specificity using ROC analysis. The overall average AUC was 0.84 (95%-CI 0.76–0.92) with an optimized DL sensitivity of 85% and specificity of 75.4%. The best results were seen in pleural effusion with an AUC of 0.92 (0.885–0.955) and sensitivity and specificity of each 86.4%. The data also showed a significant influence of sex, age, and comorbidity on the level of agreement between gold standard and DL reading. About 40% of cases could be ruled out correctly when screening for only one specific disease with a sensitivity above 95% in the exploratory analysis. For the combined reading of all abnormalities at once, only marginal workload reduction could be achieved due to insufficient specificity. DL applications like this one bear the prospect of autonomous comprehensive reporting on chest radiographs but for now require human supervision. Radiologists need to consider possible bias in certain patient groups, e.g., elderly and women. By adjusting their threshold values, commercial DL applications could already be deployed for a variety of tasks, e.g., ruling out certain conditions in screening scenarios and offering high potential for workload reduction. Full article
(This article belongs to the Special Issue The 10th Anniversary of Healthcare—Health Informatics and Big Data)
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32 pages, 3311 KiB  
Article
Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors
by Wai Lim Ku and Hua Min
Healthcare 2024, 12(6), 625; https://doi.org/10.3390/healthcare12060625 - 10 Mar 2024
Viewed by 877
Abstract
Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies [...] Read more.
Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms—a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes—in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms’ performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen’s kappa score, and positive precision for both MDD and GAD. This highlights the CNN’s superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties. Full article
(This article belongs to the Special Issue The 10th Anniversary of Healthcare—Health Informatics and Big Data)
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