Data mining predictive models for pervasive intelligent decision support in intensive care medicine

The introduction of an Intelligent Decision Support System (IDSS) in a critical area like the Intensive Medicine is a complex and difficult process. In this area, their professionals don’t have much time to document the cases, because the patient direct care is always first. With the objective to reduce significantly the manual records and, enabling, at the same time, the possibility of developing an IDSS which can help in the decision making process, all data acquisition process and knowledge discovery in database phases were automated. From the data acquisition to the knowledge discovering, the entire process is autonomous and executed in real-time. On-line induced data mining models were used to predict organ failure and outcome. Preliminary results obtained with a limited population of patients showed that this approach can be applied successfully.Fundação para a Ciência e a Tecnologia (FCT

Grid data mining for outcome prediction in intensive care medicine

This paper introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Specific Classifier and Majority Voting methods for Distributed Data Mining (DDM) are explored and compared with the Centralized Data Mining (CDM) approach. Experimental tests were conducted considering a real world data set from the intensive care medicine in order to predict the outcome of the patients. The results demonstrate that the performance of the DDM methods are better than the CDM method.Fundação para a Ciência e a Tecnologia (FCT

Pervasive business intelligence platform to improve the quality of decision process in primary and secondary education – A Portuguese case study

Business Intelligence (BI) can be seen as a method that gathers information and data from information systems in order to help companies to be more accurate in their decision-making process. Traditionally BI systems were associated with the use of Data Warehouses (DW). The prime purpose of DW is to serve as a repository that stores all the relevant information required for making the correct decision. The necessity to integrate streaming data became crucial with the need to improve the efficiency and effectiveness of the decision process. In primary and secondary education, there is a lack of BI solutions. Due to the schools reality the main purpose of this study is to provide a Pervasive BI solution able to monitoring the schools and student data anywhere and anytime in real-time as well as disseminating the information through ubiquitous devices. The first task consisted in gathering data regarding the different choices made by the student since his enrolment in a certain school year until the end of it. Thereafter a dimensional model was developed in order to be possible building a BI platform. This paper presents the dimensional model, a set of pre-defined indicators, the Pervasive Business Intelligence characteristics and the prototype designed. The main contribution of this study was to offer to the schools a tool that could help them to make accurate decisions in real-time. Data dissemination was achieved through a localized application that can be accessed anywhere and anytime.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013

Enabling ubiquitous data mining in intensive care: Features selection and data pre-processing

Ubiquitous Data Mining and Intelligent Decision Support Systems are gaining interest by both computer science researchers and intensive care doctors. Previous work contributed with Data Mining models to predict organ failure and outcome of patients in order to support and guide the clinical decision based on the notion of critical events and the data collected from monitors in real-time. This paper addresses the study of the impact of the Modified Early Warning Score, a simple physiological score that may allow improvements in the quality and safety of management provided to surgical ward patients, in the prediction sensibility. The feature selection and data pre-processing are also detailed. Results show that for some variables associated to this score the impact is minimal.Fundação para a Ciência e a Tecnologia (FCT

Towards PWA in Healthcare

Nowadays there is a very large number of mobile applications that use the network to offer some functionality to users and because of this, applications are limited by the network conditions, such as network latency. These mobile applications usually are developed in a traditional approach, designated as a native approach and its goal is to develop the application to a specific operating system (iOS, Android). Applications used in a working environment are known to improve its process, but the network has the potential to decrease application performance and traditional mobile development is inefficient. Healthcare is a field with huge opportunities for application development because applications have the potential to improve work efficiency and quality of patient care. This paper consists of introducing the Progressive Web Application mobile development approach in the healthcare industry as an m-Health solution. It highlights successful cases of such an approach and key features, that allow establishing a reliable and resilient mobile application, that deals with most challenges involving the network nowadays and is a valid opportunity in the healthcare business. This document also presents a mobile health application for dietary evaluation, compares the PWA approach and other traditional approaches with a SWOT Analysis, PWA success cases, the INTCare system (an intelligent decision support system available in the Centro Ho spitalar do Porto) and the opportunity to use Progressive Web App in the INTCare's Electronic Nursing Record (ENR), which is a web interface that represents clinical patient information, integrated in a new proposed INTCare system architecture design. (C) 2019 The Authors. Published by Elsevier B.V.This work has been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains -NORTE-01-0145-FEDER-000026

INTCARE: multi-agent approach for real-time intelligent decision support in intensive medicine

For an Intelligent Decision Support System to work in real-time, it is of great value the use of intelligent agents that cooperate with each other to accomplish their tasks. In a critical environment like an Intensive Care Unit, doctors should have the right information, at the right time, to better assist their patients. In this paper we present an architecture for a Multi-Agents System that will support doctors’ decision by in real-time, guaranteeing that all required clinical data is available and capable of predicting the patients’ condition for the next hour.Fundação para a Ciência e a Tecnologia (FCT

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented

KDD, SEMMA and CRISP-DM: a parallel overview

In the last years there has been a huge growth and consolidation of the Data Mining field. Some efforts are being done that seek the establishment of standards in the area. Included on these efforts there can be enumerated SEMMA and CRISP-DM. Both grow as industrial standards and define a set of sequential steps that pretends to guide the implementation of data mining applications. The question of the existence of substantial differences between them and the traditional KDD process arose. In this paper, is pretended to establish a parallel between these and the KDD process as well as an understanding of the similarities between them

Future challenges in intelligent tutoring systems: a framework

Intelligent Tutoring Systems (ITS) provide the benefits of one-on-one instruction in an automatic way and cost effectively, keeping in mind their multidisciplinary nature. The challenge remains on transporting to com-puters the expertise, skills and mode of action of the human tutor, overcoming space, time, socio-economical and environmental restrictions. ITS appear as a form of deployment of this issue and have been object of an increasing research. This paper aims to establish some characteristics, properties and functions that an ITS should provide, and the possible contributions that the different fields of research can make, proposing a multi-domain and multidisciplinary framework to address the research in this field. The framework incorpo-rates a knowledge base where data and knowledge related to the problem are maintained and a model base re-lated to student, teaching and environmental issues together with pedagogical perspectives

Multichannel interaction for healthcare intelligent decision support

Hospital 4.0 enables the paradigm of personalized healthcare services to be increasingly easy and more effective by using emerging technologies. Multichannel interaction services aim precisely to take advantage of this trend by introducing a multichannel interaction model that enables interaction between different health service actors (patients, nurses, doctors, administrative staff, pharmaceutics, technicians) across multiple channels and in different contexts without losing information. In this article, a model is idealized and proposed in which all main the actors that belong to health service are identified. The model aims to present what would be the multichannel interaction in a health context to improve the services provided to patients as well as their relationship with a health organization.The work has been supported by FCT - Fundacao para a Cienciae Tecnologia within the Project Scope: UID/CEC/00319/2020