Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Special Issues
Artificial Intelligence and Data Science for Health
share announcement

Artificial Intelligence and Data Science for Health

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 925

Special Issue Editor

Dr. Neil Vaughan

E-Mail Website
Guest Editor
Department of Clinical and Biomedical Science, University of Exeter, Exeter EX2 5DW, UK
Interests: evolutionary computation; genetic algorithms; health applications; artificial intelligence; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We hereby invite submissions to this Special Issue, entitled “Artificial Intelligence and Data Science for Health”, to be published in the journal Information, MDPI, ISSN 2078-2489.

Your contributions to this Special Issue will help deliver innovations to the forefront of healthcare's technological and data revolution, hence we invite researchers, practitioners, educators, and policymakers to contribute your innovative insights to this unique collection.

The goal of this Special Issue is to help shape the role of AI in deciphering complex biomedical data, improving clinical care, and delivering benefits to patients, health services and society more broadly.

Topics on which papers are welcome include, but are not limited to:

  • Biomedical data science;
  • Medical device engineering;
  • Medical AI for wearable devices;
  • Digital twin and cognitive AI;
  • Virtual reality (VR) medical applications;
  • AI-based clinical decision systems;
  • Biomedical natural language processing (NLP);
  • Large language models (LLM) in health informatics;
  • Behavioral and mental health informatics ;
  • Modeling in healthcare.

Dr. Neil Vaughan
Guest Editor

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

  • artificial intelligence
  • data science
  • virtual reality
  • health applications

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

16 pages, 3249 KiB  
Article
Explainable Artificial Intelligence and Deep Learning Methods for the Detection of Sickle Cell by Capturing the Digital Images of Blood Smears
by Neelankit Gautam Goswami, Niranjana Sampathila, Giliyar Muralidhar Bairy, Anushree Goswami, Dhruva Darshan Brp Siddarama and Sushma Belurkar
Information 2024, 15(7), 403; https://doi.org/10.3390/info15070403 - 12 Jul 2024
Viewed by 202
Abstract
A digital microscope plays a crucial role in the better and faster diagnosis of an abnormality using various techniques. There has been significant development in this domain of digital pathology. Sickle cell disease (SCD) is a genetic disorder that affects hemoglobin in red [...] Read more.
A digital microscope plays a crucial role in the better and faster diagnosis of an abnormality using various techniques. There has been significant development in this domain of digital pathology. Sickle cell disease (SCD) is a genetic disorder that affects hemoglobin in red blood cells. The traditional method for diagnosing sickle cell disease involves preparing a glass slide and viewing the slide using the eyepiece of a manual microscope. The entire process thus becomes very tedious and time consuming. This paper proposes a semi-automated system that can capture images based on a predefined program. It has an XY stage for moving the slide horizontally or vertically and a Z stage for focus adjustments. The case study taken here is of SCD. The proposed hardware captures SCD slides, which are further used to classify them with respect to normal. They are processed using deep learning models such as Darknet-19, ResNet50, ResNet18, ResNet101, and GoogleNet. The tested models demonstrated strong performance, with most achieving high metrics across different configurations varying with an average of around 97%. In the future, this semi-automated system will benefit pathologists and can be used in rural areas, where pathologists are in short supply. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)
►▼ Show Figures

Figure 1

15 pages, 465 KiB  
Article
Optimized Ensemble Learning Approach with Explainable AI for Improved Heart Disease Prediction
by Ibomoiye Domor Mienye and Nobert Jere
Information 2024, 15(7), 394; https://doi.org/10.3390/info15070394 - 8 Jul 2024
Viewed by 383
Abstract
Recent advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this research introduces an approach that [...] Read more.
Recent advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this research introduces an approach that integrates the robustness of ensemble learning algorithms with the precision of Bayesian optimization for hyperparameter tuning and the interpretability offered by Shapley additive explanations (SHAP). The ensemble classifiers considered include adaptive boosting (AdaBoost), random forest, and extreme gradient boosting (XGBoost). The experimental results on the Cleveland and Framingham datasets demonstrate that the optimized XGBoost model achieved the highest performance, with specificity and sensitivity values of 0.971 and 0.989 on the Cleveland dataset and 0.921 and 0.975 on the Framingham dataset, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)
►▼ Show Figures

Figure 1

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