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Machine Learning and Analytics for Medical Care and Health Service
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Machine Learning and Analytics for Medical Care and Health Service

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

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 20901

Special Issue Editors

Dr. Fadi Thabtah

E-Mail Website
Guest Editor
Digital Technologies, Manukau Institute of Technology, Auckland, New Zealand & School of Human and Health Sciences, University of Huddersfield, UK
Interests: machine learning, autism, data analytics; feature selection, medical informatics
Dr. David Peebles

E-Mail Website
Guest Editor
School of Human and Health Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: computational modelling; machine learning; decision making; autism; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data science and analytics are growing in importance for science and business as data continues to grow exponentially. Vast quantities of data are being collected by various stakeholders from major social medial platforms (Twitter, Facebook, Snapchat, etc.), online customers, healthcare, and mobile applications, among others, leading to the coining of a new term, “big data”, and the development of new powerful artificial intelligence (AI) and machine learning methods. These methods are capable of exploring data and seeking useful patterns and interpreting them for decision making.

Medical care and health systems are constantly striving for better, more accurate services and methods, but there are multiple challenges associated with diagnostic efficiency and accuracy, health costs, screening processes, medical resource management, medical accessibility, data security, and data quality, among others. The volume of data in medical care and health services is growing, and so AI and machine learning methods will be crucial to dealing with these challenges as they provide valuable insights that can help solve medical problems directly related to human welfare, thereby improving people’s lives.  Interest in big data processing within medical care and health services is increasing rapidly, reflecting the growing excitement in this rapidly expanding field and the potential of significant improvements in cost reduction and patient outcomes that will emerge from the application of these new AI and machine learning methods.

The purpose of this Special Issue on medical care and health services is to showcase recent advances in machine learning, AI, and big data, in medical related applications. These include but are not limited to the following:

  • Health informatics;
  • Medical decision making;
  • Intelligent health services;
  • Mobile health;
  • Machine learning methods in medical care;
  • Deep learning methods for medical care;
  • Intelligent medical diagnosis;
  • Applications of AI in healthcare;
  • Medical information systems;
  • Smart healthcare systems;
  • Social care informatics;
  • Medical and clinical data analysis case studies.

Dr. Fadi Thabtah
Dr. David Peebles 
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

  • Health informatics
  • Machine learning
  • Big data
  • Data analytics
  • Healthcare management
  • Healthcare and clinical decision making
  • Electronic and mobile health services
  • Medical diagnosis
  • Healthcare information systems
  • Data quality and accessibility.

Published Papers (3 papers)

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Research

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11 pages, 1772 KiB  
Article
Medical Fraud and Abuse Detection System Based on Machine Learning
by Conghai Zhang, Xinyao Xiao and Chao Wu
Int. J. Environ. Res. Public Health 2020, 17(19), 7265; https://doi.org/10.3390/ijerph17197265 - 5 Oct 2020
Cited by 18 | Viewed by 4312
Abstract
It is estimated that approximately 10% of healthcare system expenditures are wasted due to medical fraud and abuse. In the medical area, the combination of thousands of drugs and diseases make the supervision of health care more difficult. To quantify the disease–drug relationship [...] Read more.
It is estimated that approximately 10% of healthcare system expenditures are wasted due to medical fraud and abuse. In the medical area, the combination of thousands of drugs and diseases make the supervision of health care more difficult. To quantify the disease–drug relationship into relationship score and do anomaly detection based on this relationship score and other features, we proposed a neural network with fully connected layers and sparse convolution. We introduced a focal-loss function to adapt to the data imbalance and a relative probability score to measure the model’s performance. As our model performs much better than previous ones, it can well alleviate analysts’ work. Full article
(This article belongs to the Special Issue Machine Learning and Analytics for Medical Care and Health Service)
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12 pages, 1740 KiB  
Article
A Euclidean Group Assessment on Semi-Supervised Clustering for Healthcare Clinical Implications Based on Real-Life Data
by Muhammad Noman Sohail, Jiadong Ren and Musa Uba Muhammad
Int. J. Environ. Res. Public Health 2019, 16(9), 1581; https://doi.org/10.3390/ijerph16091581 - 6 May 2019
Cited by 17 | Viewed by 3208
Abstract
The grouping of clusters is an important task to perform for the initial stage of clinical implication and diagnosis of a disease. The researchers performed evaluation work on instance distributions and cluster groups for epidemic classification, based on manual data extracted from various [...] Read more.
The grouping of clusters is an important task to perform for the initial stage of clinical implication and diagnosis of a disease. The researchers performed evaluation work on instance distributions and cluster groups for epidemic classification, based on manual data extracted from various repositories, in order to evaluate Euclidean points. This study was carried out on Weka (3.9.2) using 281 real-life health records of diabetes mellitus patients including males and females of ages>20 and <87, who were simultaneously suffering from other chronic disease symptoms, in Nigeria from 2017 to 2018. Updated plugins of K-mean and self-organizing map(SOM) machine learning algorithms were used to cluster the data class of mellitus type for initial clinical implications. The results of the K-mean assessment were built in 0.21 seconds with nine iterations for “type” and eight for “class” attributes. Out of 281 instances, 87 (30.97%) were classified as negative and 194 (69.03%) as positive in the testing on the Euclidean space plot. By assessment for Euclidean points, SOM discovered the search space in a more effective way, but K-mean positioning potencies are impulsive in convergence. This study is important for epidemiological disease diagnosis in countries with a high epidemic risk and low socioeconomic status. Full article
(This article belongs to the Special Issue Machine Learning and Analytics for Medical Care and Health Service)
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Review

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28 pages, 371 KiB  
Review
Early Autism Screening: A Comprehensive Review
by Fadi Thabtah and David Peebles
Int. J. Environ. Res. Public Health 2019, 16(18), 3502; https://doi.org/10.3390/ijerph16183502 - 19 Sep 2019
Cited by 67 | Viewed by 12774
Abstract
Autistic spectrum disorder (ASD) refers to a neurodevelopmental condition associated with verbal and nonverbal communication, social interactions, and behavioural complications that is becoming increasingly common in many parts of the globe. Identifying individuals on the spectrum has remained a lengthy process for the [...] Read more.
Autistic spectrum disorder (ASD) refers to a neurodevelopmental condition associated with verbal and nonverbal communication, social interactions, and behavioural complications that is becoming increasingly common in many parts of the globe. Identifying individuals on the spectrum has remained a lengthy process for the past few decades due to the fact that some individuals diagnosed with ASD exhibit exceptional skills in areas such as mathematics, arts, and music among others. To improve the accuracy and reliability of autism diagnoses, many scholars have developed pre-diagnosis screening methods to help identify autistic behaviours at an early stage, speed up the clinical diagnosis referral process, and improve the understanding of ASD for the different stakeholders involved, such as parents, caregivers, teachers, and family members. However, the functionality and reliability of those screening tools vary according to different research studies and some have remained questionable. This study evaluates and critically analyses 37 different ASD screening tools in order to identify possible areas that need to be addressed through further development and innovation. More importantly, different criteria associated with existing screening tools, such as accessibility, the fulfilment of Diagnostic and Statistical Manual of Mental Disorders (DSM-5) specifications, comprehensibility among the target audience, performance (specifically sensitivity, specificity, and accuracy), web and mobile availability, and popularity have been investigated. Full article
(This article belongs to the Special Issue Machine Learning and Analytics for Medical Care and Health Service)
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