Special Issue "AI and Big Data Revolution in Healthcare: Past, Current, and Future"

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

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Dr. Maqbool Hussain
E-Mail Website
Guest Editor
1. Department of Software, Sejong University, Seoul 05006, Korea
2. Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA
Interests: healthcare AI; clinical decision support systems; knowledge graph; healthcare interopeability and standardization; precision medicine
Dr. Muhammad Afzal
E-Mail Website
Guest Editor
Department of Software, Sejong University, Seoul 05006, Korea
Interests: evidence base medicine; healthcare text mining; prcision medicine; healthcare information reterival
Dr. Wajahat Ali Khan
E-Mail Website
Guest Editor
Department of Computing, College of Engineering and Technology, University of Derby, Derby DE22 1GB, UK
Interests: semantic web and ontologies; healthcare interoperability; blockchain in healthcare; knowledge graphs
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Special Issue Information

Dear Colleagues,

With the rapid advancement in computer technologies, healthcare has also evolved. Smart and intelligent healthcare systems have been developed and are currently in practice. Some examples of these are electronic healthcare records (EHRs), electronic medical records (EMRs), personal healthcare records (PHRs), picture archiving and communication systems (PACs), and healthcare information management systems (HIMS). Along with these contemporary healthcare systems, the AI community has introduced integrated AI-based decision-making systems. These AI systems have been named clinical decision support systems (CDSS), and researchers from the University of Leeds have developed an early naïve Bayesian-based decision-making system for the diagnosis of acute abdominal pain.

The transition toward big data started in the late 1990s; however, with the introduction of sophisticated EHRs, EMRs, PHRs, PACs, and HIMS, hospitals have become hubs of giant data. Along the way, AI techniques have also evolved, and it has become possible to use some of the expensive computational techniques due to big data in healthcare and many other domains. Deep learning has become the streamlined AI technique for researchers in various domains, including healthcare, because of its decision-making capabilities.

To follow technology trends, most medical experts have also aligned their skills to become technology savvy. Simultaneously, biomedical techniques have also improved, and genomic data have become readily available at minimal cost. Having sophisticated technology to handle clinical and genomic big data, medical experts have demanded the use of both data types in conjunction to enable more accurate and targeted decisions that suit patients individually. This demand promotes a concept of “precision medicine” that tailors medical treatment to a patient’s cohort sharing similar characteristics.

At the edge of modern technologies, advanced AI techniques, and tech-savvy stakeholders, healthcare requirements are yet to align with AI. The adoption of CDSS technology and precision medicine with precise treatment and targeted therapy demands is still not stratified with current AI technologies. Nevertheless, there are AI-facilitated imaging technologies for healthcare, but the stakeholders need the support of more contextual and humanized decision-making.

Therefore, this Special Issue invites AI experts, data scientists, medical experts, researchers, and bioinformaticians to share their non-published experiences of the past, current state-of-the-art novel approaches, and future perspectives to contribute to AI in healthcare. The Special Issue is interested in relevant topics that include, but are not limited to:

AI Techniques, Knowledge Representation Schemes, and Management for Healthcare:

  • Machine learning for healthcare;
  • Rule-based learning;
  • Ontology-based knowledge representation and reasoning;
  • Case-based learning;
  • Text-based learning;
  • Clinical and biomedical text mining;
  • Explainable healthcare AI learning;
  • Clinical knowledge maintenance and evolution;
  • Deep reinforcement learning;
  • Active/self learning;
  • Embedding and transfer learning;
  • Knowledge graphs for clinical and genomic data association;
  • Interoperable knowledge;
  • Knowledge artifacts for blockchain in healthcare;
  • Contextual knowledge query construction;
  • IoT-enabled AI healthcare knowledge models;
  • Secured, accessible, and trustable knowledge-based recommendations.

Healthcare Applications and Case Studies:

  • Image-based diagnostic PACS;
  • Computerized physician order entry (CPOE);
  • CDSS diagnosis and treatment;
  • Evidence-based medicine (EBM);
  • Precision medicine, such as precision oncology;
  • AI-assisted chatbots for healthcare;
  • Medical education;
  • AI-driven eHealth and mHealth applications;
  • COVID-19 case studies—role of machine learning and big data;
  • Case studies—big data in health informatics;
  • Case studies—precision medicine.

Dr. Wajahat Ali Khan
Dr. Maqbool Hussain
Dr. Muhammad Afzal
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.


  • healthcare AI
  • clinical decision support systems
  • big data in healthcare
  • machine learning in healthcare
  • clinical knowledge management
  • clinical and genomic association

Published Papers (1 paper)

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An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method
Int. J. Environ. Res. Public Health 2021, 18(7), 3755; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073755 - 03 Apr 2021
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Diabetes distress is an alternative disorder that is often associated with depression syndromes. Psychosocial distress is an alternative disorder that acts as a resistance to diabetes self-care management and compromises diabetes control. Yet, in Nigeria, the focus of healthcare centers is largely inclined [...] Read more.
Diabetes distress is an alternative disorder that is often associated with depression syndromes. Psychosocial distress is an alternative disorder that acts as a resistance to diabetes self-care management and compromises diabetes control. Yet, in Nigeria, the focus of healthcare centers is largely inclined toward the medical aspect of diabetes that neglects psychosocial care. In this retrospective study, specific distress was measured by the Diabetes Distress Screening (DDS) scale, and depression was analyzed by the Beck Depression Inventory (BDI) and Diagnosis Statistics Manual (DSM) criteria in type 2 diabetes mellitus (T2DM) patients of Northwestern Nigeria. Additionally, we applied the Chi-square test and linear regression to measure the forecast prevalence ratio and evaluate the link between the respective factors that further determine the odd ratios and coefficient correlations in five nonintrusive variables, namely age, gender, physical exercise, diabetes history, and smoking. In total, 712 sample patients were taken, with 51.68% male and 47.31% female patients. The mean age and body mass index (BMI) was 48.6 years ± 12.8 and 45.6 years ± 8.3. Based on the BDI prediction, 90.15% of patients were found depressed according to the DSM parameters, and depression prevalence was recorded around 22.06%. Overall, 88.20% of patients had DDS-dependent diabetes-specific distress with a prevalence ratio of 24.08%, of whom 45.86% were moderate and 54.14% serious. In sharp contrast, emotion-related distress of 28.96% was found compared to interpersonal (23.61%), followed by physician (16.42%) and regimen (13.21%) distress. The BDI-based matching of depression signs was also statistically significant with p < 0.001 in severe distress patients. However, 10.11% of patients were considered not to be depressed by DSM guidelines. The statistical evidence indicates that depression and distress are closely correlated with age, sex, diabetes history, physical exercise, and smoking influences. The facts and findings in this work show that emotional distress was found more prevalent. This study is significant because it considered several sociocultural and religious differences between Nigeria and large, undeveloped, populated countries with low socioeconomic status and excessive epidemiological risk. Finally, it is important for the clinical implications of T2DM patients on their initial screenings. Full article
(This article belongs to the Special Issue AI and Big Data Revolution in Healthcare: Past, Current, and Future)
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