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

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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 "Health Communication and Informatics".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 19639

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Special Issue Editors

Dr. Maqbool Hussain

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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

College of Science and Engineering, University of Derby, Derby, UK
Interests: artificial intelligence; semantic web; health informatics
Special Issues, Collections and Topics in MDPI journals

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 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

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

Published Papers (6 papers)

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Research

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14 pages, 1277 KiB

Open AccessArticle

NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security

by Zeinab Shahbazi and Yung-Cheol Byun

Int. J. Environ. Res. Public Health 2022, 19(12), 7027; https://doi.org/10.3390/ijerph19127027 - 08 Jun 2022

Cited by 13 | Viewed by 3338

Abstract

Social media evidence is the new topic in digital forensics. If social media information is correctly explored, there will be significant support for investigating various offenses. Exploring social media information to give the government potential proof of a crime is not an easy task. Digital forensic investigation is based on natural language processing (NLP) techniques and the blockchain framework proposed in this process. The main reason for using NLP in this process is for data collection analysis, representations of every phase, vectorization phase, feature selection, and classifier evaluation. Applying a blockchain technique in this system secures the data information to avoid hacking and any network attack. The system’s potential is demonstrated by using a real-world dataset. Full article

(This article belongs to the Special Issue AI and Big Data Revolution in Healthcare: Past, Current, and Future)

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18 pages, 2076 KiB

Open AccessArticle

Assessing REM Sleep Behaviour Disorder: From Machine Learning Classification to the Definition of a Continuous Dissociation Index

by Irene Rechichi, Antonella Iadarola, Maurizio Zibetti, Alessandro Cicolin and Gabriella Olmo

Int. J. Environ. Res. Public Health 2022, 19(1), 248; https://doi.org/10.3390/ijerph19010248 - 27 Dec 2021

Cited by 5 | Viewed by 3008

Abstract

Objectives: Rapid Eye Movement Sleep Behaviour Disorder (RBD) is regarded as a prodrome of neurodegeneration, with a high conversion rate to

α

–synucleinopathies such as Parkinson’s Disease (PD). The clinical diagnosis of RBD co–exists with evidence of REM Sleep Without Atonia (RSWA), a [...] Read more.

Objectives: Rapid Eye Movement Sleep Behaviour Disorder (RBD) is regarded as a prodrome of neurodegeneration, with a high conversion rate to

α

–synucleinopathies such as Parkinson’s Disease (PD). The clinical diagnosis of RBD co–exists with evidence of REM Sleep Without Atonia (RSWA), a parasomnia that features loss of physiological muscular atonia during REM sleep. The objectives of this study are to implement an automatic detection of RSWA from polysomnographic traces, and to propose a continuous index (the Dissociation Index) to assess the level of dissociation between REM sleep stage and atonia. This is performed using Euclidean distance in proper vector spaces. Each subject is assigned a dissociation degree based on their distance from a reference, encompassing healthy subjects and clinically diagnosed RBD patients at the two extremes. Methods: Machine Learning models were employed to perform automatic identification of patients with RSWA through clinical polysomnographic scores, together with variables derived from electromyography. Proper distance metrics are proposed and tested to achieve a dissociation measure. Results: The method proved efficient in classifying RSWA vs. not-RSWA subjects, achieving an overall accuracy, sensitivity and precision of 87%, 93% and 87.5%, respectively. On its part, the Dissociation Index proved to be promising in measuring the impairment level of patients. Conclusions: The proposed method moves a step forward in the direction of automatically identifying REM sleep disorders and evaluating the impairment degree. We believe that this index may be correlated with the patients’ neurodegeneration process; this assumption will undergo a robust clinical validation process involving healthy, RSWA, RBD and PD subjects. Full article

(This article belongs to the Special Issue AI and Big Data Revolution in Healthcare: Past, Current, and Future)

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28 pages, 50734 KiB

Open AccessArticle

Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment

by Syed Imran Ali, Su Woong Jung, Hafiz Syed Muhammad Bilal, Sang-Ho Lee, Jamil Hussain, Muhammad Afzal, Maqbool Hussain, Taqdir Ali, Taechoong Chung and Sungyoung Lee

Int. J. Environ. Res. Public Health 2022, 19(1), 226; https://doi.org/10.3390/ijerph19010226 - 26 Dec 2021

Cited by 4 | Viewed by 3360

Abstract

Clinical decision support systems (CDSSs) represent the latest technological transformation in healthcare for assisting clinicians in complex decision-making. Several CDSSs are proposed to deal with a range of clinical tasks such as disease diagnosis, prescription management, and medication ordering. Although a small number of CDSSs have focused on treatment selection, areas such as medication selection and dosing selection remained under-researched. In this regard, this study represents one of the first studies in which a CDSS is proposed for clinicians who manage patients with end-stage renal disease undergoing maintenance hemodialysis, almost all of whom have some manifestation of chronic kidney disease–mineral and bone disorder (CKD–MBD). The primary objective of the system is to aid clinicians in dosage prescription by levering medical domain knowledge as well existing practices. The proposed CDSS is evaluated with a real-world hemodialysis patient dataset acquired from Kyung Hee University Hospital, South Korea. Our evaluation demonstrates overall high compliance based on the concordance metric between the proposed CKD–MBD CDSS recommendations and the routine clinical practice. The concordance rate of overall medication dosing selection is 78.27%. Furthermore, the usability aspects of the system are also evaluated through the User Experience Questionnaire method to highlight the appealing aspects of the system for clinicians. The overall user experience dimension scores for pragmatic, hedonic, and attractiveness are 1.53, 1.48, and 1.41, respectively. A service reliability for the Cronbach’s alpha coefficient greater than 0.7 is achieved using the proposed system, whereas a dependability coefficient of the value 0.84 reveals a significant effect. Full article

(This article belongs to the Special Issue AI and Big Data Revolution in Healthcare: Past, Current, and Future)

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24 pages, 5419 KiB

Open AccessArticle

Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation

by Asim Abbas, Muhammad Afzal, Jamil Hussain, Taqdir Ali, Hafiz Syed Muhammad Bilal, Sungyoung Lee and Seokhee Jeon

Int. J. Environ. Res. Public Health 2021, 18(20), 10564; https://doi.org/10.3390/ijerph182010564 - 09 Oct 2021

Cited by 9 | Viewed by 2354

Abstract

Extracting clinical concepts, such as problems, diagnosis, and treatment, from unstructured clinical narrative documents enables data-driven approaches such as machine and deep learning to support advanced applications such as clinical decision-support systems, the assessment of disease progression, and the intelligent analysis of treatment efficacy. Various tools such as cTAKES, Sophia, MetaMap, and other rules-based approaches and algorithms have been used for automatic concept extraction. Recently, machine- and deep-learning approaches have been used to extract, classify, and accurately annotate terms and phrases. However, the requirement of an annotated dataset, which is labor-intensive, impedes the success of data-driven approaches. A rule-based mechanism could support the process of annotation, but existing rule-based approaches fail to adequately capture contextual, syntactic, and semantic patterns. This study intends to introduce a comprehensive rule-based system that automatically extracts clinical concepts from unstructured narratives with higher accuracy and transparency. The proposed system is a pipelined approach, capable of recognizing clinical concepts of three types, problem, treatment, and test, in the dataset collected from a published repository as a part of the I2b2 challenge 2010. The system’s performance is compared with that of three existing systems: Quick UMLS, BIO-CRF, and the Rules (i2b2) model. Compared to the baseline systems, the average F1-score of 72.94% was found to be 13% better than Quick UMLS, 3% better than BIO CRF, and 30.1% better than the Rules (i2b2) model. Individually, the system performance was noticeably higher for problem-related concepts, with an F1-score of 80.45%, followed by treatment-related concepts and test-related concepts, with F1-scores of 76.06% and 55.3%, respectively. The proposed methodology significantly improves the performance of concept extraction from unstructured clinical narratives by exploiting the linguistic and lexical semantic features. The approach can ease the automatic annotation process of clinical data, which ultimately improves the performance of supervised data-driven applications trained with these data. Full article

(This article belongs to the Special Issue AI and Big Data Revolution in Healthcare: Past, Current, and Future)

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11 pages, 969 KiB

Open AccessArticle

An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method

by Sohail M. Noman, Jehangir Arshad, Muhammad Zeeshan, Ateeq Ur Rehman, Amir Haider, Shahzada Khurram, Omar Cheikhrouhou, Habib Hamam and Muhammad Shafiq

Int. J. Environ. Res. Public Health 2021, 18(7), 3755; https://doi.org/10.3390/ijerph18073755 - 03 Apr 2021

Cited by 4 | Viewed by 3197

Abstract

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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Review

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17 pages, 3700 KiB

Open AccessReview

The Role of Virtual Reality as a Psychological Intervention for Mental Health Disturbances during the COVID-19 Pandemic: A Narrative Review

by Muhammad Hizri Hatta, Hatta Sidi, Shalisah Sharip, Srijit Das and Suriati Mohamed Saini

Int. J. Environ. Res. Public Health 2022, 19(4), 2390; https://doi.org/10.3390/ijerph19042390 - 18 Feb 2022

Cited by 11 | Viewed by 2944

Abstract

The COVID-19 pandemic spread throughout the world and created many problems. The COVID-19 pandemic caused an increase in mortality and morbidity, including mental health problems. Around the world, the movement control order (MCO) was strictly enforced, but the spread of the infection epidemic was still rampant. The magnitude of the increase in mental health illnesses has caused many individuals to suffer. Given that face-to-face interventions are challenging to carry out during an outbreak, we need to address this critical problem through an online approach, such as virtual reality (VR). This approach is vital to helping patients deal with their existing problems in more pragmatic, practical, and customer-friendly ways. Thus, in the present review, we proposed the development of a virtual digital device for this noble purpose. Various challenges, improvements, and expectations for VR applications were outlined and discussed in this narrative review. Full article

(This article belongs to the Special Issue AI and Big Data Revolution in Healthcare: Past, Current, and Future)

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