Special Issue "The State of the Art of Health Data Science: Precision Medicine, Predictive Models and Clinical Decision Support Systems"

Special Issue Editors

Dr. Madhan Balasubramanian
E-Mail Website
Guest Editor
Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
Interests: health services and health systems (including practice patterns, service provision, health workforce, integrated care); future trends in health care (including e-health, electronic health records, innovations); methodological issues (including mixed methods, national surveys, data linkage)
Special Issues and Collections in MDPI journals
Dr. Benjumin Hsu
E-Mail Website
Guest Editor
Centre for Big Data Research in Health, The University of New South Wales, Sydney, NSW 2052, Australia
Interests: epidemiology; health data linkage; health service use; cardiovascular disease; ageing

Special Issue Information

Dear Colleagues,

“Big Data” is rapidly changing every facet of health service delivery, whilst also bringing a daunting level of complexity in decision-making. Health practitioners, management staff and policy-makers are faced with daily challenges on making sense of the vast amounts of data already produced.

The role of data scientists is to simplify and enable data-driven decisions within a fast-paced digitally connected healthcare environment. Health data science is an emerging discipline arising at the intersection of health, biostatistics and computer science. Already labelled as the “sexiest job of the 21st century”, health data scientists generate data-driven solutions to solve complex real-world health problems by employing critical thinking, analytics and modelling to derive knowledge from big data.

This Special Issue is dedicated to exploring the state of art of health data science. We intend to bring together cutting-edge research on data science related to healthcare and health services. We encourage submissions on a wide range of issues including, but not limited to, health informatics, electronic health records, telehealth, data linkage, data warehousing, biomolecular data, public health records, clinical data, mobile solutions, internet of things, and other innovations in digital health. We welcome both conceptual/theoretical articles as well as empirical research papers.

We, specifically, are keen on and encourage submissions that use Big Data and Health Information Technology for precision medicine, predictive modeling, and decision support systems.

Precision medicine aims to understand how a person's genetics, environment, and lifestyle can help determine the best approach to prevent or treat disease.

Predictive modelling broadly involves using data mining and machine learning algorithms to identify patterns in data and recognize the chance of outcomes occurring in future.

Clinical decision support are solutions inbuilt within electronic or clinical information systems that are both accessible to health practitioners and decision-makers, enabling them to make evidence-informed decisions at the point of care.

For this Special Issue, we plan to be health discipline-neutral and encourage data science solutions that cover a range of health disciplines (such as medicine, nursing, pharmacy, dentistry, allied health and health management degrees). We encourage both quantitative and qualitative research articles, as well as systematic reviews. Well-written articles displaying methodological rigor will be preferred. We welcome articles from different countries (low-, middle- and high-income) as well as different contexts (populations, technologies or diseases).

Dr. Madhan Balasubramanian
Dr. Benjumin Hsu
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 papers will be 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 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 2300 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

  • Big Data
  • precision medicine
  • predictive modelling
  • electronic health records
  • clinical decision support systems
  • data linkage
  • data science
  • health systems
  • sustainability
  • innovations

Published Papers (2 papers)

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Research

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Article
Diagnostic Agreement between Physicians and a Consultation–Liaison Psychiatry Team at a General Hospital: An Exploratory Study across 20 Years of Referrals
Int. J. Environ. Res. Public Health 2021, 18(2), 749; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18020749 - 17 Jan 2021
Cited by 1 | Viewed by 780
Abstract
Consultation–liaison psychiatry (CLP) manages psychiatric care for patients admitted to a general hospital (GH) for somatic reasons. We evaluated patterns in psychiatric morbidity, reasons for referral and diagnostic concordance between referring doctors and CL psychiatrists. Referrals over the course of 20 years (2000–2019) [...] Read more.
Consultation–liaison psychiatry (CLP) manages psychiatric care for patients admitted to a general hospital (GH) for somatic reasons. We evaluated patterns in psychiatric morbidity, reasons for referral and diagnostic concordance between referring doctors and CL psychiatrists. Referrals over the course of 20 years (2000–2019) made by the CLP Service at Modena GH (Italy) were retrospectively analyzed. Cohen’s kappa statistics were used to estimate the agreement between the diagnoses made by CL psychiatrist and the diagnoses considered by the referring doctors. The analyses covered 18,888 referrals. The most common referral reason was suspicion of depression (n = 4937; 32.3%), followed by agitation (n = 1534; 10.0%). Psychiatric diagnoses were established for 13,883 (73.8%) referrals. Fair agreement was found for depressive disorders (kappa = 0.281) and for delirium (kappa = 0.342), which increased for anxiety comorbid depression (kappa = 0.305) and hyperkinetic delirium (kappa = 0.504). Moderate agreement was found for alcohol or substance abuse (kappa = 0.574). Referring doctors correctly recognized psychiatric conditions due to their exogenous etiology or clear clinical signs; in addition, the presence of positive symptoms (such as panic or agitation) increased diagnostic concordance. Close daily collaboration between CL psychiatrists and GH doctors lead to improvements in the ability to properly detect comorbid psychiatric conditions. Full article

Review

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Review
How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic
Int. J. Environ. Res. Public Health 2021, 18(14), 7648; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147648 - 19 Jul 2021
Viewed by 442
Abstract
The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, [...] Read more.
The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence—which is probably the most complex and misunderstood by non-specialists—in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Electronic Health Records in Oral Health Services Research and Policy: A Scoping Review
Authors: Balasubramaian, M., Yacooub, A. and Sohn, W.
Affiliations: Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; et al.

Title: Validation of the Hospital Frailty Risk Score in Community-Dwelling Older Men: Concord Health and Ageing in Men Project
Authors: Hsu, B., Blyth, F.M., Le Couteur, D.G., Waite, L.M., Seibel, M.J., Handelsman, D.J. and Naganathan, V.
Affiliations: Centre for Big Data Research in Health, The University of New South Wales, Sydney, NSW 2052, Australia; et al.

Title: Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data—An Interpretable Machine Learning Approach
Authors: Adrian Richter 1, Julia Truthmann 2, Jean‑François Chenot 2 and Carsten Oliver Schmidt 1
Affiliations: 1 Department SHIP‑KEF, Institute for Community Medicine, Greifswald University Medical Center, Walther Rathenau Str. 48, 17475 Greifswald, Germany; 2 Department of Family Medicine, Institute for Community Medicine, Fleischmannstr. 42, 17475 Greifswald, Germany

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