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

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

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

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

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

Using domain knowledge to improve intelligent decision support in intensive medicine - a study of bacteriological infections

Nowadays antibiotic prescription is object of study in many countries. The rate of prescription varies from country to country, without being found the reasons that justify those variations. In intensive care units the number of new infections rising each day is caused by multiple factors like inpatient length of stay, low defences of the body, chirurgical infections, among others. In order to complement the support of the decision process about which should be the most efficient antibiotic it was developed a heuristic based in domain knowledge extracted from biomedical experts. This algorithm is implemented by intelligent agents. When an alert appear on the presence of a new infection, an agent collects the microbiological results for cultures, it permits to identify the bacteria, then using the rules it searches for a role of antibiotics that can be administered to the patient, based on past results. At the end the agent presents to physicians the top-five sets and the success percentage of each antibiotic. This paper presents the approach proposed and a test with a particular bacterium using real data provided by an Intensive Care Unit.This work has been supported by FCT – Fundação para a Ciência e Tecnologia in the scope of the project: Pest-OE/EEI/UI0319/2014 and PEst-OE/EEI/UI0752/2014. The authors would like to thank FCT (Foundation of Science and Technology, Portugal) for the financial support through the contract PTDC/EEI-SII/1302/2012 (INTCare II)

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

INTCare: a knowledge discovery based intelligent decision support system for intensive care medicine

This paper introduces the INTCare system, an intelligent information system based on a completely automated Knowledge Discovery process and on the Agents paradigm. The system was designed to work in Hospital Intensive Care Units, supporting the physicians’ decisions by means of prognostic Data Mining models. In particular, these techniques were used to predict organ failure and mortality assessment. The main intention is to change the current reactive behaviour to a pro-active one, enhancing the quality of service. Current applications and experimentations, the functional and structural aspects, and technological options are presented

Critical events in mechanically ventilated patients

Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a pre-defined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.- Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013 . The authors would like to thank FCT (Foundation of Science and Technology, Portugal) for the financial support through the contract PTDC/EEI-SII/1302/2012 (INTCare II

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