Artificial Intelligence Algorithms in Healthcare

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1629

Special Issue Editor


E-Mail Website
Guest Editor
Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, 20133 Milano, Italy
Interests: bioinformatics; computational biology; artificial intelligence in medicine; electronics

Special Issue Information

Dear Colleagues,

We invite you to submit your research to this Special Issue, “Artificial Intelligence Algorithms in Healthcare”, which will focus on the area of artificial intelligence methods and algorithms applied to the field of healthcare. Artificial intelligence in healthcare aims to support decision-based medical tasks through knowledge- and/or data-intensive computer-based solutions that ultimately support and improve the performance of a human care provider. The articles have to present new methods related to artificial intelligence and computer science algorithms with a high impact on the medical or healthcare domain. Potential topics include, but are not limited to: AI-based clinical decision making; natural language processing in medicine; data analytics and mining for biomedical decision support; new computational platforms and models for biomedicine; intelligent exploitation of heterogeneous data sources aimed at supporting decision-based and data-intensive clinical tasks; machine learning and deep learning in medicine and healthcare; artificial intelligence methods in computer-aided diagnostic tools and decision support analytics for clinical informatics; and deep learning in precision medicine.

Dr. Gabriella Trucco
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. Algorithms 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

  • AI-based clinical decision making
  • computational intelligence in bio- and clinical medicine
  • intelligent information systems in healthcare and medicine
  • natural language processing in medicine
  • data analytics and mining for biomedical decision support
  • new computational platforms and models for biomedicine
  • intelligent exploitation of heterogeneous data sources aimed at supporting decision-based and data-intensive clinical tasks
  • intelligent devices and instruments
  • automated reasoning and meta-reasoning in medicine
  • machine learning in medicine, medically oriented human biology, and healthcare
  • AI and data science in medicine, medically oriented human biology, and healthcare
  • AI-based modeling and management of healthcare pathways and clinical guidelines
  • models and systems for AI-based population health
  • AI in medical and healthcare education
  • artificial intelligence methods and algorithms in computer-aided diagnostic tools and decision support analytics for clinical informatics
  • deep learning in precision medicine
  • artificial intelligence algorithms in precision health
  • rule-based expert systems
  • robotic process automation

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 5690 KiB  
Article
Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
by Xiaonan Si, Lei Wang, Wenchang Xu, Biao Wang and Wenbo Cheng
Algorithms 2024, 17(3), 122; https://doi.org/10.3390/a17030122 - 15 Mar 2024
Viewed by 774
Abstract
Gout is one of the most painful diseases in the world. Accurate classification of gout is crucial for diagnosis and treatment which can potentially save lives. However, the current methods for classifying gout periods have demonstrated poor performance and have received little attention. [...] Read more.
Gout is one of the most painful diseases in the world. Accurate classification of gout is crucial for diagnosis and treatment which can potentially save lives. However, the current methods for classifying gout periods have demonstrated poor performance and have received little attention. This is due to a significant data imbalance problem that affects the learning attention for the majority and minority classes. To overcome this problem, a resampling method called ENaNSMOTE-Tomek link is proposed. It uses extended natural neighbors to generate samples that fall within the minority class and then applies the Tomek link technique to eliminate instances that contribute to noise. The model combines the ensemble ’bagging’ technique with the proposed resampling technique to improve the quality of generated samples. The performance of individual classifiers and hybrid models on an imbalanced gout dataset taken from the electronic medical records of a hospital is evaluated. The results of the classification demonstrate that the proposed strategy is more accurate than some imbalanced gout diagnosis techniques, with an accuracy of 80.87% and an AUC of 87.10%. This indicates that the proposed algorithm can alleviate the problems caused by imbalanced gout data and help experts better diagnose their patients. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms in Healthcare)
Show Figures

Figure 1

Back to TopTop