Implementing a Pervasive Real-Time Intelligent System for Tracking Critical Events with Intensive Care Patients

2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)

Silva

et al. 2014

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Cardiac Arrhythmia (CA) is very dangerous and can significantly undermine patient condition. New tools are fundamental to forecast and to prevent possible critical situations. In order to help clinicians acting proactively, predictive data mining real-time models were induced using online-learning. As input variables were considered those acquired at the patient admission and complementary variables (vital signs, laboratory results, therapeutics) hourly collected. The results are very motivating; sensitivity near to 95% was obtained when using Support Vector Machines. The approach explored in this work reveals to be an interesting contribution to the healthcare in terms of predicting CA and a good direction to be further explored.

Section: A Cardiac Arrhythmiamentioning

confidence: 99%

Preventing patient Cardiac Arrhythmias by using data mining techniques

2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)

Silva

et al. 2014

Self Cite

“…These events can occur several times a day. The possibility of having the ability to identify events automatically and in real-time represents an important help to the decision process [10]. Patient condition can modify several times for hour.…”

Section: Ambient Intelligence Intensive Care and Critic Eventsmentioning

confidence: 99%

“…The identification of a critic event is recognized when an attribute values is out of the normal range for a certain period of time [13]. An earlier study was made in order to track vital signs critic events based in the value collected and the time event [10]. The results achieved in the past were the first step to develop a universal and most complete system by giving the possibility to the user configure it.…”

Section: Ambient Intelligence Intensive Care and Critic Eventsmentioning

confidence: 99%

A Real-Time Intelligent System for Tracking Patient Condition

Oliveira

Lecture Notes in Computer Science

et al. 2015

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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.

“…organ failure, patient outcome, length of stay, barotrauma among others). The use of critical events [1,13] improves the sensitivity of the data mining models [1,[14][15][16]. Now this project is focused in the respiratory system and in predicting the occurrence of barotrauma [17].…”

Section: Intcarementioning

confidence: 99%

“…However and although the high number of data available, the physicians do not have time to read and understand all the data available in the right moment. In order to support and facilitate this analysis the Critical Events (CE) concept [1] was adopted and it was associated to ventilation variables. The concept was initially applied to vital signs variables [2].…”

Section: Introductionmentioning

confidence: 99%

Critical Events in Mechanically Ventilated Patients

New Advances in Information Systems and Technologies

Machado

et al. 2016

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Abstract. 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 predefined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.