Special Issue "Artificial Intelligence Algorithms for Medicine"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 1 May 2023 | Viewed by 985

Special Issue Editor

Special Issue Information

Dear Colleagues,

In recent decades, the Big Data phenomenon has driven the application of informatics in medicine to solve multiple problems in the field. In particular, the use of artificial intelligence algorithms , specifically machine learning algorithms, is turning out to be very useful in problems of disease prediction, the search for patterns of characteristics to identify populations at risk, the discovery of factors that influence the appearance of diseases, medical image processing and information extraction, and the classification of medical information. In this sense, a work area has been developed that specializes in the design and application of algorithms specifically aimed at solving problems in medicine. The objective of this Special Issue is to bring together works that show the latest advances in the application of artificial intelligence algorithms in the medical field, as well as specific experiences and applications to specific problems.

The objective of this Special Issue is to serve as a meeting point for all researchers who are working in these fields both theoretically and with an applied focus. The topics of interest include, but are not limited to:

  • Machine learning applied to medicine;
  • Artificial intelligence applied to medicine;
  • Big Data and health;
  • Application of artificial intelligence to information processing;
  • Data analysis applied to medicine;
  • Algorithms for medicine;
  • Massive data of medical processing;
  • Medical image processing;
  • e-Health.

Both review articles on the state of the art and experimental or theoretical articles are welcome.

Dr. Antonio Sarasa-Cabezuelo
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 1400 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

  • deep learning
  • machine learning
  • artificial intelligence
  • data analysis
  • algorithms
  • big data

Published Papers (1 paper)

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

Research

Article
Construction of Life-Cycle Simulation Framework of Chronic Diseases and Their Comorbidities Based on Population Cohort
Algorithms 2022, 15(5), 167; https://doi.org/10.3390/a15050167 - 16 May 2022
Viewed by 550
Abstract
Life-cycle population follow-up data collection is time-consuming and often takes decades. General cohort data studies collect short-to-medium-term data from populations of different age groups. The purpose of constructing a life-cycle simulation method is to find an efficient and reliable way to achieve the [...] Read more.
Life-cycle population follow-up data collection is time-consuming and often takes decades. General cohort data studies collect short-to-medium-term data from populations of different age groups. The purpose of constructing a life-cycle simulation method is to find an efficient and reliable way to achieve the way to characterize life-cycle disease metastasis from these short-to-medium-term data. In this paper, we have presented our effort at construction of a full lifetime population cohort simulation framework. The design aim is to generate a comprehensive understanding of the disease transition for full lifetime when we only have short-or-medium term population cohort data. We have conducted several groups of experiments to show the effectiveness of our method. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)
Show Figures

Figure 1

Back to TopTop