36,533 research outputs found
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
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
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
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
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
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
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
Pervasive intelligent decision support system: technology acceptance in intensive care units
Intensive Care Units are considered a critical environment where the
decision needs to be carefully taken. The real-time recognition of the condition
of the patient is important to drive the decision process efficiently. In order to
help the decision process, a Pervasive Intelligent Decision Support System
(PIDSS) was developed. To provide a better comprehension of the acceptance
of the PIDSS it is very important to assess how the users accept the system at
level of usability and their importance in the Decision Making Process. This
assessment was made using the four constructs proposed by the Technology
Acceptance Methodology and a questionnaire-based approach guided by the
Delphi Methodology. The results obtained so far show that although the users
are satisfied with the offered information recognizing its importance, they
demand for a faster system.Fundação para a Ciência e a Tecnologia (FCT
- …