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Information
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Machine Learning and Health Informatics: Techniques, Applications, and Advancements
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Machine Learning and Health Informatics: Techniques, Applications, and Advancements

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 10031

Special Issue Editors

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
Dr. Asad Masood Khattak

E-Mail Website
Guest Editor
College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates
Interests: semantic web; data mining; context-aware computing; secure computing; smart cities
Special Issues, Collections and Topics in MDPI journals
Dr. Zeeshan Pervez

E-Mail Website
Guest Editor
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High Street, Paisley PA1 2BE, UK
Interests: Internet of Things; cybersecurity; data security; privacy; applied ML/AI

Special Issue Information

Dear Colleagues,

Machine Learning (ML) is widely used in the fields of computer sciences, healthcare, bioinformatics, businesses, social media analysis, and smart cities to facilitate accurate analysis, monitoring, and decision-making. These ML applications have revolutionized the Health Informatics (HI) domain; a number of advanced applications can be found in various fields of healthcare ranging from e-healthcare to personalized healthcare. These applications assist physicians in complex diagnosis, patient-context-based selection of treatment regimens, patient monitoring in controlled and uncontrolled environments, evidence-based personalized decision-making, personalized medicine and monitoring, and drug development, to name a few. ML pertaining to diagnosis deals with identifying patterns of certain diseases within Electronic Medical Record (EMR) data. Such applications are useful for identifying anomalies in patients’ health records which can be flagged for further investigation by caregivers. Prognosis for a disease, such as cancer, is considered to be a highly complicated process. In this regard, ML applications assist the physician in modeling the prognosis through a number of clinical variables, such as gene expression profiles, histological parameters, and other relevant factors. Likewise, drug discovery processes are long and complex in nature and are affected by environmental factors. ML techniques can assist in decision-making in all stages of the discovery process, such as identification of biomarkers, digital pathology in clinical trials, target validation, and others. This Special Issue is focused on how to identify, select, develop, test, and implement an appropriate ML method/algorithm/application in the presence of multiple health issues, geological factors, and patient attributes, considering the availability of relevant facilities.
This Special Issue aims to cover novel and state-of-the-art technologies and methods in the area of Machine Learning combined with Health Informatics, with the purpose of improving quality and accuracy of the eHealth, uHealth, and mHealth applications. The editors welcome high-quality and original research articles and reviews.
Relevant topics for this Special Issue include but are not limited to the following:

  • Knowledge Graph and its utilization in health informatics;
  • Health informatics initiatives and their design, implementation, evaluation, and adoption;
  • COVID-19 and machine learning—the challenges and lessons learned;
  • Precision medicine;
  • Internet of Medical Things;
  • Deep learning in healthcare;
  • Security and privacy in healthcare;
  • Blockchain in healthcare;
  • Knowledge maintenance and evolution;
  • Context-aware systems and their applications in healthcare;
  • Big medical data and analytics;
  • Evidence-based decision-making;
  • Case studies of machine learning and health informatics;
  • Machine learning, healthcare, and smart cities.

Dr. Wajahat Ali Khan
Dr. Asad Masood Khattak
Dr. Zeeshan Pervez
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. Information 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 1600 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
  • Precision medicine
  • Big medical data
  • Machine learning
  • Knowledge management
  • Internet of Things
  • Security and privacy

Published Papers (3 papers)

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Research

11 pages, 279 KiB  
Article
A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature
by Tanmay Basu, Simon Goldsworthy and Georgios V. Gkoutos
Information 2021, 12(4), 139; https://doi.org/10.3390/info12040139 - 24 Mar 2021
Cited by 5 | Viewed by 2108
Abstract
The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction [...] Read more.
The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors. Full article
(This article belongs to the Special Issue Machine Learning and Health Informatics: Techniques, Applications, and Advancements)
21 pages, 5695 KiB  
Article
Monitoring People’s Emotions and Symptoms from Arabic Tweets during the COVID-19 Pandemic
by Ali Al-Laith and Mamdouh Alenezi
Information 2021, 12(2), 86; https://doi.org/10.3390/info12020086 - 19 Feb 2021
Cited by 27 | Viewed by 3949
Abstract
Coronavirus-19 (COVID-19) started from Wuhan, China, in late December 2019. It swept most of the world’s countries with confirmed cases and deaths. The World Health Organization (WHO) declared the virus a pandemic on 11 March 2020 due to its widespread transmission. A public [...] Read more.
Coronavirus-19 (COVID-19) started from Wuhan, China, in late December 2019. It swept most of the world’s countries with confirmed cases and deaths. The World Health Organization (WHO) declared the virus a pandemic on 11 March 2020 due to its widespread transmission. A public health crisis was declared in specific regions and nation-wide by governments all around the world. Citizens have gone through a wide range of emotions, such as fear of shortage of food, anger at the performance of governments and health authorities in facing the virus, sadness over the deaths of friends or relatives, etc. We present a monitoring system of citizens’ concerns using emotion detection in Twitter data. We also track public emotions and link these emotions with COVID-19 symptoms. We aim to show the effect of emotion monitoring on improving people’s daily health behavior and reduce the spread of negative emotions that affect the mental health of citizens. We collected and annotated 5.5 million tweets in the period from January to August 2020. A hybrid approach combined rule-based and neural network techniques to annotate the collected tweets. The rule-based technique was used to classify 300,000 tweets relying on Arabic emotion and COVID-19 symptom lexicons while the neural network was used to expand the sample tweets that were annotated using the rule-based technique. We used long short-term memory (LSTM) deep learning to classify all of the tweets into six emotion classes and two types (symptom and non-symptom tweets). The monitoring system shows that most of the tweets were posted in March 2020. The anger and fear emotions have the highest number of tweets and user interactions after the joy emotion. The results of user interaction monitoring show that people use likes and replies to interact with non-symptom tweets while they use re-tweets to propagate tweets that mention any of COVID-19 symptoms. Our study should help governments and decision-makers to dispel people’s fears and discover new symptoms associated with the symptoms that were declared by the WHO. It can also help in the understanding of people’s mental and emotional issues to address them before the impact of disease anxiety becomes harmful in itself. Full article
(This article belongs to the Special Issue Machine Learning and Health Informatics: Techniques, Applications, and Advancements)
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11 pages, 2550 KiB  
Article
Lightweight End-to-End Neural Network Model for Automatic Heart Sound Classification
by Tao Li, Yibo Yin, Kainan Ma, Sitao Zhang and Ming Liu
Information 2021, 12(2), 54; https://doi.org/10.3390/info12020054 - 26 Jan 2021
Cited by 19 | Viewed by 2758
Abstract
Heart sounds play an important role in the initial screening of heart diseases. However, the accurate diagnosis with heart sound signals requires doctors to have many years of clinical experience and relevant professional knowledge. In this study, we proposed an end-to-end lightweight neural [...] Read more.
Heart sounds play an important role in the initial screening of heart diseases. However, the accurate diagnosis with heart sound signals requires doctors to have many years of clinical experience and relevant professional knowledge. In this study, we proposed an end-to-end lightweight neural network model that does not require heart sound segmentation and has very few parameters. We segmented the original heart sound signal and performed a short-time Fourier transform (STFT) to obtain the frequency domain features. These features were sent to the improved two-dimensional convolutional neural network (CNN) model for features learning and classification. Considering the imbalance of positive and negative samples, we introduced FocalLoss as the loss function, verified our network model with multiple random verifications, and, hence, obtained a better classification result. Our main purpose is to design a lightweight network structure that is easy for hardware implementation. Compared with the results of the latest literature, our model only uses 4.29 K parameters, which is 1/10 of the size of the state-of-the-art work. Full article
(This article belongs to the Special Issue Machine Learning and Health Informatics: Techniques, Applications, and Advancements)
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