Pervasive and Intelligent Decision Support in Intensive Medicine – The Complete Picture

Portela, Filipe; Santos, Manuel Filipe; Machado, José; Abelha, António; Silva, Álvaro; Rua, Fernando

doi:10.1007/978-3-319-10265-8_9

Cited by 47 publications

(20 citation statements)

References 18 publications

(17 reference statements)

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“…It is able to support physicians and nurses decision process in real-time [3]. INTCare take advantages of data streaming, intelligent agents and an autonomous Extract, Transformation and Loading (ETL) process.…”

Section: Intcarementioning

confidence: 99%

Pervasive Adaptive Data Acquisition Gateway for Critical Healthcare

Oliveira

Portela

Santos

et al. 2016

New Advances in Information Systems and Technologies

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Abstract. The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients' variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data.

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Section: Intcarementioning

confidence: 99%

Pervasive Adaptive Data Acquisition Gateway for Critical Healthcare

Oliveira

Portela

Santos

et al. 2016

New Advances in Information Systems and Technologies

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“…INTCare is an Intelligent Decision Support System (IDSS) [6] for Intensive Care which is in constantly developing and testing. This intelligent system was deployed in the Intensive Care Unit (ICU) of Centro Hospitalar do Porto (CHP).…”

Section: Intcarementioning

confidence: 99%

Clustering Barotrauma Patients in ICU–A Data Mining Based Approach Using Ventilator Variables

Oliveira

Portela

Santos

et al. 2015

Progress in Artificial Intelligence

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Abstract. Predicting barotrauma occurrence in intensive care patients is a difficult task. Data Mining modelling can contribute significantly to the identification of patients who will suffer barotrauma. This can be achieved by grouping patient data, considering a set of variables collected from ventilators directly related with barotrauma, and identifying similarities among them. For clustering have been considered k-means and k-medoids algortihms (Partitioning Around Medoids). The best model induced presented a Davies-Bouldin Index of 0.64. This model identifies the variables that have more similarity among the variables monitored by the ventilators and the occurrence of barotrauma.

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“…By this reason they need to be continuously monitored [4]. This type of patients is admitted to a specialized place called Intensive Care Unit (ICU) characterized by its critical environment [9]. Bersten and Soni [10] consider two types of patients that can be admitted to ICU: Patients who require continuous monitoring and treatment and patients with organ failure, with recovering hypotheses.…”

Section: Intensive Medicinementioning

confidence: 99%

Patients’ Admissions in Intensive Care Units: A Clustering Overview

et al. 2017

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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 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.

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Pervasive and Intelligent Decision Support in Intensive Medicine – The Complete Picture

Cited by 47 publications

References 18 publications

Pervasive Adaptive Data Acquisition Gateway for Critical Healthcare

Pervasive Adaptive Data Acquisition Gateway for Critical Healthcare

Clustering Barotrauma Patients in ICU–A Data Mining Based Approach Using Ventilator Variables

Patients’ Admissions in Intensive Care Units: A Clustering Overview

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