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Computers
Special Issues
Applications of Machine Learning and Artificial Intelligence for Healthcare
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Applications of Machine Learning and Artificial Intelligence for Healthcare

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 392

Special Issue Editor

Dr. Elias Dritsas

E-Mail Website
Guest Editor
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece
Interests: artificial intelligence; big data; data analysis; databases; data mining; data structures; machine learning; privacy; security; trust
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Substantial advances have been made in artificial intelligence (AI) and machine learning (ML) in recent years, and these advancements have great potential for healthcare applications.

The domain of eHealth is emerging alongside the advancement of information and telecommunication technologies and the need for improved healthcare services. At the same time, healthcare applications also face many challenges, such as the difficulties associated with obtaining various types of health-related information, lack of large-sized training data, or even privacy concerns. More specialized research efforts and developments are still needed to address these issues.

This Special Issue aims to provide original, high-quality, innovative ideas and research solutions (for both theoretical and practical challenges) for data analysis and modelling with the aid of artificial intelligence and machine learning in the domain of healthcare.

The key topics of interest include (but are not limited to):

  1. Artificial intelligence in healthcare;
  2. Machine learning in healthcare;
  3. Statistics;
  4. Predictive modeling;
  5. Monitoring;
  6. Data analytics;
  7. Personal health advisor;
  8. Early diagnosis.

Dr. Elias Dritsas
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. Computers 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 1800 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 data
  • e-Health
  • prediction

Published Papers (1 paper)

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Research

21 pages, 3742 KiB  
Article
A Framework for Cleaning Streaming Data in Healthcare: A Context and User-Supported Approach
by Obaid Alotaibi, Sarath Tomy and Eric Pardede
Computers 2024, 13(7), 175; https://doi.org/10.3390/computers13070175 - 16 Jul 2024
Viewed by 227
Abstract
Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, [...] Read more.
Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, and data errors, which can significantly affect data analysis results and lead to inaccurate decision making. Enhancing the quality of real-time data streams has become a challenging task as it is crucial for better decisions. In this paper, we propose a framework to improve the quality of a real-time data stream by considering different aspects, including context-awareness. The proposed framework tackles several issues in the data stream, including duplicated data, missing values, and outliers to improve data quality. The proposed framework also provides recommendations on appropriate data cleaning techniques to the user to help improve data quality in real time. Also, the data quality assessment is included in the proposed framework to provide insight to the user about the data stream quality for better decisions. We present a prototype to examine the concept of the proposed framework. We use a dataset that is collected in healthcare and process these data using a case study. The effectiveness of the proposed framework is verified by the ability to detect and repair stream data quality issues in selected context and to provide a recommended context and data cleaning techniques to the expert for better decision making in providing healthcare advice to the patient. We evaluate our proposed framework by comparing the proposed framework against previous works. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)
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