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

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance

Towards of a real-time Big Data architecture to intensive care

These days the exponential increase in the volume and variety of data stored by companies and organizations of various sectors of activity, has required to organizations the search for new solutions to improve their services and/or products, taking advantage of technological evolution. As a response to the inability of organizations to process large quantities and varieties of data, in the technological market, arise the Big Data. This emerging concept defined mainly by the volume, velocity and variety has evolved greatly in part by its ability to generate value for organizations in decision making. Currently, the health care sector is one of the five sectors of activity where the potential of Big Data growth most stands out. However, the way to go is still long and in fact there are few organizations, related to health care, that are taking advantage of the true potential of Big Data. The main target of this research is to produce a real-time Big Data architecture to the INTCare system, of the Centro Hospitalar do Porto, using the main open source big data solution, the Apache Hadoop. As a result of the first phase of this research we obtained a generic architecture who can be adopted by other Intensive Care Units."This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013." This work is also supported by the Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains - NORTE-01-0145-FEDER-00002

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The authors would like to thank FCT for the financial support through the contract PTDC/EEI - SII/1302/2012 (INTCare II

Pervasive Business Intelligence: A New Trend in Critical Healthcare

In the field of intensive medicine, presentation of medical information is identified as a major concern for the health professionals, since it can be a great aid when it is necessary to make decisions, of varying gravity, for the patient's state. The way in which this information is presented, and especially when it is presented, may make it difficult for the intensivists within intense healthcare units to understand a patient's state in a timely fashion. Should there be a need to cross various types of clinical data from various sources, the situation worsens considerably. To support the health professional's decision-making process, the Pervasive Business Intelligence (PBI) Systems are a forthcoming field. Based on this principle, the current study approaches the way to present information about the patients, after they are received in a BI system, making them available at any place and at any time for the intensivists that may need it for the decision-making. The patient's history will, therefore, be available, allowing examination of the vital signs data, what medicine that they might need, health checks performed, among others. Then, it is of vital importance, to make these conclusions available to the health professionals every time they might need, so as to aid them in the decision-making. This study aims to make a stance by approaching the theme of PBI in Critical Healthcare. The main objective is to understand the underlying concepts and the assets of BI solutions with Pervasive characteristics. Perhaps consider it a sort of guide or a path to follow for those who wish to insert Pervasive into Business Intelligence in Healthcare area.Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013info:eu-repo/semantics/publishedVersio

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.FCT -Fundação para a Ciência e a Tecnologia(PTDC/EEI-SII/1302/2012

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

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

Pervasive and intelligent decision support in Intensive Medicine – the complete picture

Series : Lecture notes in computer science (LNCS), vol. 8649In the Intensive Care Units (ICU) it is notorious the high number of data sources available. This situation brings more complexity to the way of how a professional makes a decision based on information provided by those data sources. Normally, the decisions are based on empirical knowledge and common sense. Often, they don’t make use of the information provided by the ICU data sources, due to the difficulty in understanding them. To overcome these constraints an integrated and pervasive system called INTCare has been deployed. This paper is focused in presenting the system architecture and the knowledge obtained by each one of the decision modules: Patient Vital Signs, Critical Events, ICU Medical Scores and Ensemble Data Mining. This system is able to make hourly predictions in terms of organ failure and outcome. High values of sensitivity where reached, e.g. 97.95% for the cardiovascular system, 99.77% for the outcome. In addition, the system is prepared for tracking patients’ critical events and for evaluating medical scores automatically and in real-time.(undefined

Step towards to improve the voluntary interruption of pregnancy by means of business intelligence

With the implementation of Information and Communication Technologies in the health sector, it became possible the existence of an electronic record of information for patients, enabling the storage and the availability of their information in databases. However, without the implementation of a Business Intelligence (BI) system, this information has no value. Thus, the major motivation of this paper is to create a decision support system that allows the transformation of information into knowledge, giving usability to the stored data. The particular case addressed in this chapter is the Centro Materno Infantil do Norte, in particular the Voluntary Interruption of Pregnancy unit. With the creation of a BI system for this module, it is possible to design an interoperable, pervasive and real-time platform to support the decision-making process of health professionals, based on cases that occurred. Furthermore, this platform enables the automation of the process for obtaining key performance indicators that are presented annually by this health institution. In this chapter, the BI system implemented in the VIP unity in CMIN, some of the KPIs evaluated as well as the benefits of this implementation are presented.FCT - Fundação para a Ciência e Tecnologia within the P roject Scope UID/CEC/00319/201