Luciana Cardoso

The next generation of interoperability agents in healthcare

Interoperability in health information systems is increasingly a requirement rather than an option. Standards and technologies, such as multi-agent systems, have proven to be powerful tools in interoperability issues. In the last few years, the authors have worked on developing the Agency for Integration, Diffusion and Archive of Medical Information (AIDA), which is an intelligent, agent-based platform to ensure interoperability in healthcare units. It is increasingly important to ensure the high availability and reliability of systems. The functions provided by the systems that treat interoperability cannot fail. This paper shows the importance of monitoring and controlling intelligent agents as a tool to anticipate problems in health information systems. The interaction between humans and agents through an interface that allows the user to create new agents easily and to monitor their activities in real time is also an important feature, as health systems evolve by adopting more features and solving new problems. A module was installed in Centro Hospitalar do Porto, increasing the functionality and the overall usability of AIDA.

Improving High Availability and Reliability of Health Interoperability Systems

The accessibility and availability of patient clinical information are a constant need. The Agency for Interoperation, Diffusion and Archive of Medical Information (AIDA) was then developed to ensure the interoperability among healthcare information systems successfully. AIDA has demonstrated over time the need for greater control over its agents and their activities as the need for monitoring and preventing its machines and agents.

Abstract Computation in Schizophrenia Detection through Artificial Neural Network Based Systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Predicting nosocomial infection by using data mining technologies

The existence of nosocomial infection prevision systems in healthcare environments can contribute to improve the quality of the healthcare institution and also to reduce the costs with the treatment of the patients that acquire these infections. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify their eventual occurrence. This paper presents the results of the application of predictive models to real clinical data. Good models, induced by the Data Mining (DM) classification techniques Support Vector Machines and Naive Bayes, were achieved (sensitivities higher than 91.90%). Therefore, with these models that be able to predict these infections may allow the prevention and, consequently, the reduction of nosocomial infection incidence. They should act as a Clinical Decision Support System (CDSS) capable of reducing nosocomial infections and the associated costs, improving the healthcare and, increasing patients’ safety and well-being.

Intelligent Information System to Tracking Patients in Intensive Care Units

With the increasing expansion of health information systems, there is a need to create an interface: human, machine and the surrounding environment. This interface is called Ambient Intelligence and it has been increasing in the healthcare area. In this paper it is presented the Ambient Intelligence system implemented in the Intensive Care Unit of Centro Hospitalar do Porto, a hospital in the north of Portugal. This Ambient Intelligence is consisted by INTCare system, which the main goal is monitoring the patients’ vital signs, PaLMS system, responsible for the patient’s localisation and identification and AIDA, the platform that guarantees the interoperability from all information systems in the hospital. Furthermore, an usability evaluation was performed, described in this article, to find out what can be improved.

An Agent-Based RFID Monitoring System for Healthcare

In the last years, with the progressive expansion of Healthcare Information Systems (HISs), the healthcare platforms for interoperability and monitoring systems have become increasingly more vital sources of clinical information. In this context, in Centro Hospitalar do Porto (CHP), the INTCare system was developed with the purpose to create new useful knowledge for decision support in real-time. It is an unquestionable potential area to develop effective systems for the prediction of clinical events, including Decision Support Systems (DSSs), for organ failure and death in Intensive Care. The INTCare uses multiple data sources that are collected at the bedside. However, this system fails on the recognition of the patient absence in bed. Thereby, this problem led to the development of the Patient Localization and Management System (PaLMS), i.e., a RFID localization and monitoring system. Thus, this paper describes the PaLMS Intelligent Multi-Agent System for the integration of PaLMS into the hospital platform for Interoperability, Diffusion and Archive – Agency for Integration, Diffusion and Archive of Medical Information (AIDA) platform. On the other hand, a failure prevention system that actuates in the PaLMS agents, improving their availability, is also presented and thoroughly discussed.

A Multi-agent Platform for Hospital Interoperability

The interoperability among the Health Information Systems is a natural demand nowadays. The Agency for Integration, Diffusion and Archive of Medical Information (AIDA) is a Multi-Agent System (MAS) specifically developed to guarantee interoperability in health organizations.

Intelligent Systems for Monitoring and Prevention in Healthcare Information Systems

Nowadays the interoperability in Healthcare Information Systems (HIS) is a fundamental requirement. The Agency for Integration, Diffusion and Archive of Medical Information (AIDA) is an interoperability healthcare platform that ensures these demands and it is implemented in Centro Hospitalar do Porto (CHP), a major healthcare unit in Portugal. Therefore, the overall performance of CHP HIS depends on the success of AIDA functioning. This paper presents monitoring and prevention systems implemented in the CHP, which aim to improve the system integrity and high availability. These systems allow the monitoring and the detection of situations conducive to failure in the AIDA main components: database, machines and intelligent agents. Through the monitoring systems, it was found that the database most critical period is between 11:00 and 12:00 and the resources are well balanced. The prevention systems detected abnormal situations that were reported to the administrators that took preventive actions, avoiding damage to AIDA workflow.

Tracking people and equipment simulation inside healthcare units

Simulating the trajectory of a patient, health professional or medical equipment can have diverse advantages in a healthcare environment. Many hospitals choose and to rely on RFID tracking systems to avoid the theft or loss of equipment, reduce the time spent looking for equipment, finding missing patients or staff, and issuing warnings about personnel access to unauthorized areas. The ability to successfully simulate the trajectory of an entity is very important to replicate what happens in RFID embedded systems. Testing and optimizing in a simulated environment, which replicates actual conditions, prevent accidents that may occur in a real environment. Trajectory prediction is a software approach which provides, in real time, the set of sensors that can be deactivated to reduce power consumption and thereby increase the system’s lifetime. Hence, the system proposed here aims to integrate the aforementioned strategies - simulation and prediction. It constitutes an intelligent tracking simulation system able to simulate and predict an entity’s trajectory in an area fitted with RFID sensors. The system uses a Data Mining algorithm, designated SK-Means, to discover object movement patterns through historical trajectory data.