Selected Papers from the Workshop on Intelligent Systems and Applications in Healthcare (ISA’Health 2016)

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: closed (31 October 2016) | Viewed by 10412

Special Issue Editors


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Guest Editor
Computer Science and Technology, ALGORITMI RESEARCH Centre, University of Minho, Portugal
Interests: electronic health records; interoperability; databases; applied artificial intelligence

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Guest Editor
Information Systems and Technologies, Algoritmi Research Centre, University of Minho, 4710-057 Braga, Portugal
Interests: intelligent decision support systems; artificial intelligence; business intelligence; data mining; intelligent data systems

Special Issue Information

Dear Colleagues,

The main business goal in healthcare is to increase the quality of services in healthcare while reducing costs. This objective contributes to improving the quality of life of patients, and, in some cases, reducing mortality and morbidity. To achieve this goal, the use of intelligent systems in the decision process becomes essential. The Workshop on Intelligent Systems and Applications in Healthcare (ISA’Health 2016) is focused on demonstrating how to take advantages of using Computer Intelligence, ensuring interoperability and security, overcoming local and temporal barriers in order to support the needs of society, and the development of innovative systems and applications to support healthcare decision support (http://idsist.wix.com/isahealth).

This workshop will bring together researchers who are working in the area of Intelligent System and Business Informatics. The cross-domain integration and appraisal of different fields, related to Intelligent Systems and Applications, provides an atmosphere to foster a variety of perspectives and opinions.

The main goal of this Special Issue is to extend the best workshop papers, presenting innovative and exciting works and promoting a discussion about how Intelligent Systems can contribute to improve the quality of the decision process in healthcare.

Prof. José Machado
Prof. António Abelha
Prof. Manuel Filipe Santos
Prof. Carlos Filipe Portela
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 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

  • Intelligent Systems
  • Biomedical Engineering
  • Data Engineering
  • Applications
  • Artificial Intelligence
  • Information Systems
  • Intelligent Decision Support
  • Health Care

Published Papers (2 papers)

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Research

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Article
Automated Detection of Liver Histopathological Findings Based on Biopsy Image Processing
by Maria Tsiplakidou, Markos G. Tsipouras, Nikolaos Giannakeas, Alexandros T. Tzallas and Pinelopi Manousou
Information 2017, 8(1), 36; https://doi.org/10.3390/info8010036 - 20 Mar 2017
Cited by 11 | Viewed by 4473
Abstract
Hepatic steatosis is the accumulation of fat in the hepatic cells and the liver. Triglycerides and other kinds of molecules are included in the lipids. When there is some defect in the process, hepatic steatosis arise, during which the free fatty acids are [...] Read more.
Hepatic steatosis is the accumulation of fat in the hepatic cells and the liver. Triglycerides and other kinds of molecules are included in the lipids. When there is some defect in the process, hepatic steatosis arise, during which the free fatty acids are taken by the liver and exuded as lipoproteins. Alcohol is the main cause of steatosis when excessive amounts are consumed for a long period of time. In many cases, steatosis can lead to inflammation that is mentioned as steatohepatitis or non-alcoholic steatohepatitis (NASH), which can later lead to fibrosis and finally cirrhosis. For automated detection and quantification of hepatic steatosis, a novel two-stage methodology is developed in this study. Initially, the image is processed in order to become more suitable for the detection of fat regions and steatosis quantification. In the second stage, initial candidate image regions are detected, and then they are either validated or discarded based on a series of criteria. The methodology is based on liver biopsy image analysis, and has been tested using 40 liver biopsy images obtained from patients who suffer from hepatitis C. The obtained results indicate that the proposed methodology can accurately assess liver steatosis. Full article
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507 KiB  
Article
Patients’ Admissions in Intensive Care Units: A Clustering Overview
by Ana Ribeiro, Filipe Portela, Manuel Santos, António Abelha, José Machado and Fernando Rua
Information 2017, 8(1), 23; https://doi.org/10.3390/info8010023 - 17 Feb 2017
Cited by 6 | Viewed by 5168
Abstract
Intensive care is a critical area of medicine having a multidisciplinary nature requiring all types of healthcare professionals. Given the critical environment of intensive care units (ICUs), the need to use information technologies, like decision support systems, to improve healthcare services and ICU [...] Read more.
Intensive care is a critical area of medicine having a multidisciplinary nature requiring all types of healthcare professionals. Given the critical environment of intensive care units (ICUs), the need to use information technologies, like decision support systems, to improve healthcare services and ICU management is evident. It is proven that unplanned and prolonged admission to the ICU is not only prejudicial to a patient's health, but also such a situation implies a readjustment of ICU resources, including beds, doctors, nurses, financial resources, among others. By discovering the common characteristics of the admitted patients, it is possible to improve these outcomes. In this study clustering techniques were applied to data collected from admitted patients in an intensive care unit. The best results presented a silhouette of 1, with a distance to centroids of 6.2 × 10−17 and a Davies–Bouldin index of −0.652. Full article
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