A new architecture for intelligent clinical decision support for intensive medicine

Real-time and intelligent decision support systems are of most importance to supply intensive care professionals with important information in useful time. The work presented hereby shows an architectural overview of the communication system with bedside devices such as vital sign monitors. Intelligent Decision Support System for Intensive Medicine (ICDS4IM) goal is to ensure information quality and availability to Intensive Medicine professionals to take supported decisions in a mutable environment where complex and unpredictable events are a common state. Therefore, this work focus on Health Information Systems, Interoperability and Information Diffusion and Archive. Moreover, communication standards and the usage of a new technology such as containerization are discussed. (C) 2020 The Authors. Published by Elsevier B.V.The work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the Projects Scope: UID/CEC/00319/2020 and DSAIPA/DS/0084/2018

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

A pervasive approach to a real-time intelligent decision support system in intensive medicine

The decision on the most appropriate procedure to provide to the patients the best healthcare possible is a critical and complex task in Intensive Care Units (ICU). Clinical Decision Support Systems (CDSS) should deal with huge amounts of data and online monitoring, analyzing numerous parameters and providing outputs in a short real-time. Although the advances attained in this area of knowledge new challenges should be taken into account in future CDSS developments, principally in ICUs environments. The next generation of CDSS will be pervasive and ubiquitous providing the doctors with the appropriate services and information in order to support decisions regardless the time or the local where they are. Consequently new requirements arise namely the privacy of data and the security in data access. This paper will present a pervasive perspective of the decision making process in the context of INTCare system, an intelligent decision support system for intensive medicine. Three scenarios are explored using data mining models continuously assessed and optimized. Some preliminary results are depicted and discussed.Fundação para a Ciência e a Tecnologia (FCT

Real-time decision support in intensive medicine: an intelligent approach for monitoring data quality

Intensive Medicine is an area where big amounts of data are generated every day. The process to obtain knowledge from these data is extremely difficult and sometimes dangerous. The main obstacles of this process are the number of data collected manually and the quality of the data collected automatically. Information quality is a major constrain to the success of Intelligent Decision Support Systems (IDSS). This is the case of INTCare an IDSS which operates in real-time. Data quality needs to be ensured in a continuous way. The quality must be assured essentially in the data acquisition process and in the evaluation of the results obtained from data mining models. To automate this process a set of intelligent agents have been developed to perform a set of data quality tasks. This paper explores the data quality issues in IDSS and presents an intelligent approach for monitoring the data quality in INTCare system.Fundação para a Ciência e a Tecnologia (FCT

Knowledge discovery for pervasive and real-time intelligent decision support in intensive care medicine

Pervasiveness, real-time and online processing are important requirements included in the researchers’ agenda for the development of future generation of Intelligent Decision Support Systems (IDSS). In particular, knowledge discovery based IDSS operating in critical environments such of intensive care, should be adapted to those new requests. This paper introduces the way how INTCare, an IDSS developed in the intensive care unit of the Centro Hospitalar do Porto, will accommodate the new functionalities. Solutions are proposed for the most important constraints, e.g., paper based data, missing values, values out- of-range, data integration, data quality. The benefits and limitations of the approach are discussed.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA/72819/ 2006, SFRH/BD/70156/201

Architecture for intensive care data processing and visualization in real-time

Clinical data is growing every day. Ergo, to treat, store and publish such data is an emergent task. Furthermore, analysing data in real-time using streaming and processing technologies and methods, in order to obtain quality data, prepared to support decision making is of extreme value. Big Data emerged with the introduction of real-time processing, thus revolutionizing traditional technologies and techniques through the ability to deal with the volume, speed and variety of data. Countless studies have been proposed in the healthcare domain in search of solutions that allow the flow of data in real-time. However, the work presented hereby is distinguished by allowing the collection, processing, storage and analysis of Intensive Care Units (ICU) data, both collected in real-time from bedside monitors but also stored in a historical repository. The architecture proposed makes use of current technologies, like Nextgen Connector as message supplier and integrator, Elasticsearch as a search index, Kibana for viewing stored data and Grafana for real-time streaming. This article is part of the ICDS4IM project - Intelligent Clinical Decision Support in Intensive Care Medicine to support the experimentation of data processing techniques and technologies, based in HL7 format and collected in real-time so that it can be made available through Health Information Systems across the healthcare institutions.The work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: DSAIPA/DS/0084/2018

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

Intelligent decision support in Intensive Care : towards technology acceptance

Decision support technology acceptance is a critical factor in the success of the adoption this type of systems by the users. INTCARE is an intelligent decision support system for intensive care medicine. The main purpose of this system is to help the doctors and nurses making decisions more proactively based on the prediction of the organ failure and the outcome of the patients. To assure the adoption of INTCARE by the doctors and by the nurses, several requirements had taken into account: process dematerialization (information is now in electronic format); interoperability among the systems (the AIDA platform was used to interoperate with other information systems); on-line data acquisition and real-time processing (a set of software agents has been developed to accomplish these tasks). A technology acceptance methodology has been followed in the Intensive Care Unit (ICU) of Centro Hospitalar do Porto in order to assure the most perfect alignment between the functional and technical characteristics of INTCARE and the user expectations. Results showed that the ICU staff is permeable to the system. In general more than 90 % of the answers are scored with 4 or 5 points which gives a good motivation to continue the work.Fundação para a Ciência e a Tecnologia (FCT

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care