Intelligent Data Acquisition and Scoring System for Intensive Medicine

Portela, Filipe; Santos, Manuel Filipe; Machado, José; Silva, Álvaro; Rua, Fernando; Abelha, António

doi:10.1007/978-3-642-32395-9_1

Cited by 11 publications

(10 citation statements)

References 16 publications

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“…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%

Critical Events in Mechanically Ventilated Patients

Portela

Santos

Machado

et al. 2016

New Advances in Information Systems and Technologies

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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.

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Section: Intcarementioning

confidence: 99%

Critical Events in Mechanically Ventilated Patients

Portela

Santos

Machado

et al. 2016

New Advances in Information Systems and Technologies

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“…The objective of PIDSS component is to behave as an Intelligent Scoring System (ISS). The ISS (Portela, et al, 2012) is incorporated into the Electronic Nursing Record (ENR). Nowadays, the ICU professionals can record and consult the scores in real-time.…”

Section: Icu Medical Scoresmentioning

confidence: 99%

“… ICU Medical Scores (Portela et al, 2012);  ICU Critical Events (P. G. Filipe Portela, Manuel Filipe Santos, Álvaro Silva, Fernando Rua, 2012);  Probability of occur an organ failure probability and patient die (F. P. . In order to assess the technology developed, the Technology Acceptance Model (TAM) was adopted.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Technology Acceptance in Intensive Care Units

Portela

Aguiar

Santos

et al. 2014

International Journal of Systems and Service-Oriented Engineering

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The process of deploy a technology in critical services need to be very careful planned and processed. As an example it is the Intensive Care Unit (ICU). In the ICU the patients are in critically ill conditions and there are not available time to make experiences or to develop incomplete systems. With the objective to improve the implementation process, the same should be accompanied in order to understand the environment and user behaviour. In this case and with the goal to evaluate the implementation process, an assessment model was applied to a real system called INTCare. INTCare is a Pervasive Intelligent Decision Support System (PIDSS). It was deployed in the ICU of Centro Hospitalar do Porto and was evaluated using the Technology Acceptance Model 3 (TAM). This assessment was made using the four constructs proposed by the TAM 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 this importance, they demand for a faster system. This work present the main results achieved and suggest one way to follow when some technology is deployed in an environment like is ICU.

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“…The agents are integrated in decision making process and allow having data with high level of quality for automatically and in real-time attain: a) Intensive Care Scores [14] ; b) Patient Critical Events [15]; c) Predict organ failure and patient outcome [16].…”

Section: Oracle Data Miningmentioning

confidence: 99%

Real-Time Decision Support in Intensive Medicine: An intelligent approach for monitoring Data Quality

Portela¹,

Santos²,

Machado³

et al. 2013

JOMB

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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.  Index Terms-intensive care; data quality, real-time; intelligent decision support; intcare.

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Intelligent Data Acquisition and Scoring System for Intensive Medicine

Cited by 11 publications

References 16 publications

Critical Events in Mechanically Ventilated Patients

Critical Events in Mechanically Ventilated Patients

Assessment of Technology Acceptance in Intensive Care Units

Real-Time Decision Support in Intensive Medicine: An intelligent approach for monitoring Data Quality

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