Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.2
4.6
Journals
Bioengineering
Special Issues
Machine Learning in Healthcare: Behavioral and Cognitive Insights from Medical...
share announcement

Machine Learning in Healthcare: Behavioral and Cognitive Insights from Medical Data

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2194

Special Issue Editors

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
Dr. Neda Abdelhamid

E-Mail Website
Guest Editor
Business Analytics Department, Abu Dhabi School of Management, Abu Dhabi 6844, United Arab Emirates
Interests: data analytics; machine learning

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) and Machine Learning (ML) are key technologies that are having an increasingly significant impact across a wide variety of domains due to their powerful capabilities in providing innovative and cost-effective solutions to complex problems. AI systems are able to mimic human intelligence and can learn and adapt to their environments. Machine learning systems use algorithms to find structures and patterns in either structured, semi-structured, or unstructured data to allow people to make predictive and planning decisions. With the development of natural language processing systems, deep learning, and other learning techniques, together with sophisticated visualization methods, AI systems can now offer high quality, complex problem solutions which allow users to make decisions in real time.

The two main forms of learning that AI and machine learning systems perform are supervised and unsupervised learning. The former involves training on labelled data to develop models for predictive analysis on unseen data for tasks such as classification and regression analysis, while the latter analyses unlabelled data to derive hidden patterns for tasks related to association, feature engineering, and cluster analysis. The digitization of medical and healthcare systems around the world has created a wealth of both forms of data that machine learning and AI practitioners are exploiting to develop diagnostic and clinical applications that are becoming increasingly indispensable for healthcare professionals.

This Special Issue on “Machine Learning in Healthcare: Behavioral and Cognitive Insights from Medical Data” aims to illustrate the breadth and impact of recent innovations by inviting scholars, clinicians, diagnosticians, healthcare professionals, and computer scientists creating or employing AI and ML methods in the various medical and healthcare fields to present their research. This Special Issue focuses in particular on cognitive, neurological, and behavioural applications in healthcare such as cognitive impairment, dementia, autism spectrum disorder, and other related medical conditions to provide the healthcare community with effective solutions based on AI and machine learning. The papers we seek will cover a range of AI and machine learning techniques in cognitive and behavioural medical conditions, with techniques including, but not limited to, classification, feature engineering, cluster analysis, regression analysis, association rules, correlation analysis, data visualization, and time series analysis. We call for papers covering a wide range of applications, including medical screening, disease diagnosis, medical therapy, analysis of neuropsychological features, biomarker analysis, medical records storage and collection, medical data management, clinical data analysis, medical data privacy, cognitive therapy, behavioural data analysis, medical imaging, and disease management and intervention.

Dr. David Peebles
Dr. Neda Abdelhamid
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. Bioengineering 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 2700 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

  • machine learning
  • artificial intelligence
  • healthcare
  • medical data analysis
  • cognitive and behavioural medical conditions

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 3378 KiB  
Article
ACNN-BiLSTM: A Deep Learning Approach for Continuous Noninvasive Blood Pressure Measurement Using Multi-Wavelength PPG Fusion
by Mou Cui, Xuhao Dong, Yan Zhuang, Shiyong Li, Shimin Yin, Zhencheng Chen and Yongbo Liang
Bioengineering 2024, 11(4), 306; https://doi.org/10.3390/bioengineering11040306 - 25 Mar 2024
Viewed by 764
Abstract
As an essential physiological indicator within the human body, noninvasive continuous blood pressure (BP) measurement is critical in the prevention and treatment of cardiovascular disease. However, traditional methods of blood pressure prediction using a single-wavelength Photoplethysmographic (PPG) have bottlenecks in further improving BP [...] Read more.
As an essential physiological indicator within the human body, noninvasive continuous blood pressure (BP) measurement is critical in the prevention and treatment of cardiovascular disease. However, traditional methods of blood pressure prediction using a single-wavelength Photoplethysmographic (PPG) have bottlenecks in further improving BP prediction accuracy, which limits their development in clinical application and dissemination. To this end, this study proposed a method to fuse a four-wavelength PPG and a BP prediction model based on the attention mechanism of a convolutional neural network and bidirectional long- and short-term memory (ACNN-BiLSTM). The effectiveness of a multi-wavelength PPG fusion method for blood pressure prediction was evaluated by processing PPG signals from 162 volunteers. The study compared the performance of the PPG signals with different individual wavelengths and using a multi-wavelength PPG fusion method in blood pressure prediction, assessed using mean absolute error (MAE), root mean squared error (RMSE) and AAMI-related criteria. The experimental results showed that the ACNN-BiLSTM model achieved a better MAE ± RMSE for a systolic BP and diastolic BP of 1.67 ± 5.28 and 1.15 ± 2.53 mmHg, respectively, when using the multi-wavelength PPG fusion method. As a result, the ACNN-BiLSTM blood pressure model based on multi-wavelength PPG fusion could be considered a promising method for noninvasive continuous BP measurement. Full article
(This article belongs to the Special Issue Machine Learning in Healthcare: Behavioral and Cognitive Insights from Medical Data)
►▼ Show Figures

Graphical abstract

16 pages, 1300 KiB  
Article
Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping
by Neda Abdelhamid, Rajdeep Thind, Heba Mohammad and Fadi Thabtah
Bioengineering 2023, 10(10), 1131; https://doi.org/10.3390/bioengineering10101131 - 27 Sep 2023
Cited by 3 | Viewed by 1036
Abstract
Autistic spectrum disorder (ASD) is a neurodevelopmental condition that characterises a range of people, from individuals who are not able to speak to others who have good verbal communications. The disorder affects the way people see, think, and behave, including their communications and [...] Read more.
Autistic spectrum disorder (ASD) is a neurodevelopmental condition that characterises a range of people, from individuals who are not able to speak to others who have good verbal communications. The disorder affects the way people see, think, and behave, including their communications and social interactions. Identifying autistic traits, preferably in the early stages, is fundamental for clinicians in expediting referrals, and hence enabling patients to access to required healthcare services. This article investigates various ASD behavioral features in toddlers and proposes a data process using machine-learning techniques. The aims of this study were to identify early behavioral features that can help detect ASD in toddlers and to map these features to the neurodevelopment behavioral areas of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). To achieve these aims, the proposed data process assesses several behavioral features using feature selection techniques, then constructs a classification model based on the chosen features. The empirical results show that during the screening process of toddlers, cognitive features related to communications, social interactions, and repetitive behaviors were most relevant to ASD. For the machine-learning algorithms, the predictive accuracy of Bayesian network (Bayes Net) and logistic regression (LR) models derived from ASD behavioral data subsets were consistent pinpointing to the suitability of ML techniques in predicting ASD. Full article
(This article belongs to the Special Issue Machine Learning in Healthcare: Behavioral and Cognitive Insights from Medical Data)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop