Smart Systems in Medical Informatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 787

Special Issue Editors


E-Mail Website
Guest Editor
Computer Science Department, Instituto Superior de Engenharia do Port, 4000 Porto, Portugal
Interests: user modeling; recommendation systems; medical informatics; adaptive systems

E-Mail Website
Guest Editor
Computer Science Department, Instituto Superior de Engenharia do Port, 4000 Porto, Portugal
Interests: medical informatics; artificial intelligence; natural language processing; machine learning

Special Issue Information

Dear Colleagues,

Since the early days of Artificial Intelligence (AI), many AI applications in the healthcare area have been developed. Initially, Knowledge-Based Systems, and in particular, Expert Systems, were the first AI approaches to develop applications in the field of Medical Informatics.

However, current advances in computational capacity, data acquisition and machine learning techniques have contributed to a growing interest in “Smart Systems in Medical Informatics”.

Smart Systems may contribute to providing more time for medical staff to interact with patients, improving the quality of health care. For example, Natural Language Processing (NLP) offers a set of techniques that allow for the implementation of applications that can be used to save time for psychiatrists, increase adherence to treatments, improve life quality and support a more independent style of living.

This Special Issue will provide an analysis of the actual trends regarding the main problems that can be dealt with as related to “Smart Systems in Medical Informatics”, and the corresponding main techniques used to deal with these problems. Additionally, some case studies will be presented and described.

Dr. António Constantino Lopes Martins
Dr. Luiz Felipe Rocha de Faria
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • smart system
  • medical informatics
  • artificial intelligence
  • health care
  • natural language processing
  • machine learning
  • personal healthcare assistant
  • recommendation systems

Published Papers (1 paper)

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Research

14 pages, 4019 KiB  
Article
Performance of API Design for Interoperability of Medical Information Systems
by Leticia Dávila Nicanor, Abraham Banda Madrid, Jesús E. Martínez Hernández and Irene Aguilar Juárez
Appl. Sci. 2024, 14(9), 3944; https://doi.org/10.3390/app14093944 - 6 May 2024
Viewed by 603
Abstract
After the experience of the COVID-19 pandemic, it has become evident that efficient and secure interoperability of medical information is crucial for effective diagnoses and medical treatments. However, a significant challenge arises concerning the heterogeneity of the systems storing patient information in medical [...] Read more.
After the experience of the COVID-19 pandemic, it has become evident that efficient and secure interoperability of medical information is crucial for effective diagnoses and medical treatments. However, a significant challenge arises concerning the heterogeneity of the systems storing patient information in medical centers or hospitals. Memory management becomes a pivotal element for the effective operation of the proposed API, as it must seamlessly execute across various devices, ranging from healthcare units, such as mobile phones, to servers in cloud computing. This proposal addresses these issues through techniques designed to enhance the performance of the software architecture in creating a medical interoperability API. This API has the capacity to be cloned and distributed to facilitate the exchange of data related to a patient’s medical history. To tackle heterogeneity, efficient memory management was implemented by utilizing an object-oriented approach and leveraging design patterns like abstract factory and wrapper. Regarding the evaluation of the proposal, this study showed an estimated performance of 94.5 percent, which was indirectly demonstrated through the assessment of operation sequences. This result suggests a satisfactory level based on complexity and coupling. Full article
(This article belongs to the Special Issue Smart Systems in Medical Informatics)
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