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

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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 (30 September 2022) | Viewed by 11694

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Special Issue Editors

Dr. Madhan Balasubramanian

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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, Collections and Topics in MDPI journals

Dr. Benjumin Hsu

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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 21^st 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 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 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 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

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

Published Papers (6 papers)

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Research

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Open AccessArticle

Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records

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Int. J. Environ. Res. Public Health 2022, 19(3), 1319; https://doi.org/10.3390/ijerph19031319 - 25 Jan 2022

Cited by 1 | Viewed by 1464

Abstract

A risk prediction model for major cardiovascular events was developed using population survey data linked to National Health Insurance (NHI) claim data and the death registry. Another set of population survey data were used to validate the model. The model was built using the Nutrition and Health Survey in Taiwan (NAHSIT) collected from 1993–1996 and linked with 10 years of events from NHI data. Major adverse cardiovascular events (MACEs) were identified based on hospital admission or death from coronary heart disease or stroke. The Taiwanese Survey on Hypertension, Hyperglycemia, and Hyperlipidemia (TwSHHH), conducted in 2002 was used for external validation. The NAHSIT data consisted of 1658 men and 1652 women aged 35–70 years. The incidence rates for MACE per 1000 person-years were 13.77 for men and 7.76 for women. These incidence rates for the TwSHHH were 7.27 for men and 3.58 for women. The model had reasonable discrimination (C-indexes: 0.76 for men; 0.75 for women), thus can be used to predict MACE risks in the general population. NHI data can be used to identify disease statuses if the definition and algorithm are clearly defined. Precise preventive health services in Taiwan can be based on this model. Full article

(This article belongs to the Special Issue The State of the Art of Health Data Science: Precision Medicine, Predictive Models and Clinical Decision Support Systems)

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Open AccessArticle

Construction and Evaluation of a High-Frequency Hearing Loss Screening Tool for Community Residents

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Int. J. Environ. Res. Public Health 2021, 18(23), 12311; https://doi.org/10.3390/ijerph182312311 - 23 Nov 2021

Viewed by 1127

Abstract

Early screening and detection of individuals at high risk of high-frequency hearing loss and identification of risk factors are critical to reduce the prevalence at community level. However, unlike those for individuals facing occupational auditory hazards, a limited number of hearing loss screening models have been developed for community residents. Therefore, this study used lasso regression with 10-fold cross-validation for feature selection and model construction on 38 questionnaire-based variables of 4010 subjects and applied the model to training and testing cohorts to obtain a risk score. The model achieved an area under the curve (AUC) of 0.844 in the model validation stage and individuals’ risk scores were subsequently stratified into low-, medium-, and high-risk categories. A total of 92.79% (1094/1179) of subjects in the high-risk category were confirmed to have hearing loss by audiometry test, which was 3.7 times higher than that in the low-risk group (25.18%, 457/1815). Half of the key indicators were related to modifiable contexts, and they were identified as significantly associated with the incident hearing loss. These results demonstrated that the developed model would be feasible to identify residents at high risk of hearing loss via regular community-level health examinations and detecting individualized risk factors, and eventually provide precision interventions. Full article

(This article belongs to the Special Issue The State of the Art of Health Data Science: Precision Medicine, Predictive Models and Clinical Decision Support Systems)

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Open AccessArticle

Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data—An Interpretable Machine Learning Approach

Adrian Richter

Julia Truthmann

Jean-François Chenot

and

Carsten Oliver Schmidt

Int. J. Environ. Res. Public Health 2021, 18(22), 12013; https://doi.org/10.3390/ijerph182212013 - 16 Nov 2021

Viewed by 1247

Abstract

(1) Background: Predicting chronic low back pain (LBP) is of clinical and economic interest as LBP leads to disabilities and health service utilization. This study aims to build a competitive and interpretable prediction model; (2) Methods: We used clinical and claims data of 3837 participants of a population-based cohort study to predict future LBP consultations (ICD-10: M40.XX-M54.XX). Best subset selection (BSS) was applied in repeated random samples of training data (75% of data); scoring rules were used to identify the best subset of predictors. The rediction accuracy of BSS was compared to randomforest and support vector machines (SVM) in the validation data (25% of data); (3) Results: The best subset comprised 16 out of 32 predictors. Previous occurrence of LBP increased the odds for future LBP consultations (odds ratio (OR) 6.91 [5.05; 9.45]), while concomitant diseases reduced the odds (1 vs. 0, OR: 0.74 [0.57; 0.98], >1 vs. 0: 0.37 [0.21; 0.67]). The area-under-curve (AUC) of BSS was acceptable (0.78 [0.74; 0.82]) and comparable with SVM (0.78 [0.74; 0.82]) and randomforest (0.79 [0.75; 0.83]); (4) Conclusions: Regarding prediction accuracy, BSS has been considered competitive with established machine-learning approaches. Nonetheless, considerable misclassification is inherent and further refinements are required to improve predictions. Full article

(This article belongs to the Special Issue The State of the Art of Health Data Science: Precision Medicine, Predictive Models and Clinical Decision Support Systems)

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Open AccessArticle

Diagnostic Agreement between Physicians and a Consultation–Liaison Psychiatry Team at a General Hospital: An Exploratory Study across 20 Years of Referrals

Mattia Marchi

Federica Maria Magarini

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Int. J. Environ. Res. Public Health 2021, 18(2), 749; https://doi.org/10.3390/ijerph18020749 - 17 Jan 2021

Cited by 6 | Viewed by 1863

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) 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

(This article belongs to the Special Issue The State of the Art of Health Data Science: Precision Medicine, Predictive Models and Clinical Decision Support Systems)

Review

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Open AccessReview

A Systematic Approach in Developing Management Workforce Readiness for Digital Health Transformation in Healthcare

Mark Brommeyer

and

Zhanming Liang

Int. J. Environ. Res. Public Health 2022, 19(21), 13843; https://doi.org/10.3390/ijerph192113843 - 25 Oct 2022

Cited by 1 | Viewed by 1922

Abstract

Background: The COVID-19 pandemic has sped up digital health transformation across the health sectors to enable innovative health service delivery. Such transformation relies on competent managers with the capacity to lead and manage. However, the health system has not adopted a holistic approach in addressing the health management workforce development needs, with many hurdles to overcome. The objectives of this paper are to present the findings of a three-step approach in understanding the current hurdles in developing a health management workforce that can enable and maximize the benefits of digital health transformation, and to explore ways of overcoming such hurdles. Methods: A three-step, systematic approach was undertaken, including an Australian digital health policy documentary analysis, an Australian health service management postgraduate program analysis, and a scoping review of international literatures. Results: The main findings of the three-step approach confirmed the strategies required in developing a digitally enabled health management workforce and efforts in enabling managers in leading and managing in the digital health space. Conclusions: With the ever-changing landscape of digital health, leading and managing in times of system transformation requires a holistic approach to develop the necessary health management workforce capabilities and system-wide capacity. The proposed framework, for overall health management workforce development in the digital health era, suggests that national collaboration is necessary to articulate a more coordinated, consistent, and coherent set of policy guidelines and the system, policy, educational, and professional organizational enablers that drive a digital health focused approach across all the healthcare sectors, in a coordinated and contextual manner. Full article

(This article belongs to the Special Issue The State of the Art of Health Data Science: Precision Medicine, Predictive Models and Clinical Decision Support Systems)

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Open AccessReview

How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic

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Int. J. Environ. Res. Public Health 2021, 18(14), 7648; https://doi.org/10.3390/ijerph18147648 - 19 Jul 2021

Cited by 4 | Viewed by 2758

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, 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

(This article belongs to the Special Issue The State of the Art of Health Data Science: Precision Medicine, Predictive Models and Clinical Decision Support Systems)

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