Data mining models to predict patient's readmission in intensive care units

Decision making is one of the most critical activities in Intensive Care Units (ICU). Moreover, it is extremely difficult for health professionals to interpret in real time all the available data. In order to improve the decision process, classification models have been developed to predict patient’s readmission in ICU. Knowing the probability of readmission in advance will allow for a more efficient planning of discharge. Consequently, the use of these models results in a lower rates of readmission and a cost reduction, usually associated with premature discharges and unplanned readmissions. In this work was followed a numerical index, called Stability and Workload Index for Transfer (SWIFT). The data used to induce the classification models are from ICU of Centro Hospitalar do Porto, Portugal. The results obtained so far, in terms of accuracy, were very satisfactory (98.91%). Those results were achieved through the use of Naïve Bayes technique. The models will allow health professionals to have a better perception on patient’s future condition in the moment of the hospital discharge. Therefore it will be possible to know the probability of a patient being readmitted into the ICU.(undefined

Estudio numérico de los efectos de las propiedades termofísicas y de transporte en el proceso de inyección de combustibles alternativos para motores de encendido por compresión

La disminución de emisiones producto de la combustión de los motores de encendido por compresión, así como el interés internacional por disminuir la dependencia directa de los productos derivados del petróleo, han impulsado proyectos de investigación que permitan establecer los beneficios generados por el uso de combustibles alternativos, ya sean de carácter renovable o provenientes de otras tecnologías de producción. Uno de los procesos más importantes del desarrollo de la combustión en este tipo de motores es la fase de inyección de combustible, puesto que todo el proceso de combustión depende de su óptima ejecución. Por esta razón, es de vital importancia conocer como los nuevos combustibles llevan a cabo la formación del chorro inyectado y el proceso de mezcla. El análisis del proceso de mezcla para un nuevo combustible puede ser realizado de forma experimental, pero la compleja infraestructura necesaria para ejecutar tipo de pruebas impide que puedan llevarse a cabo de manera amplia en toda la comunidad científica. Este trabajo pretende aprovechar las ventajas de la modelación computacional para evaluar el comportamiento de tres combustibles alternativos y de esta forma abrir el camino a la exploración de ´estos desde un punto de vista numérico. De igual forma la metodología empleada en este trabajo de investigación podrá ser empleada para analizar cualquier otro tipo de combustible que sea desarrollado

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

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

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

Assessment of technology acceptance in intensive care units

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 condit ions and there aren’t 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

Real-time data mining models for predicting length of stay in intensive care units

Nowadays the efficiency of costs and resources planning in hospitals embody a critical role in the management of these units. Length Of Stay (LOS) is a good metric when the goal is to decrease costs and to optimize resources. In Intensive Care Units (ICU) optimization assumes even a greater importance derived from the high costs associated to inpatients. This study presents two data mining approaches to predict LOS in an ICU. The first approach considered the admission variables and some other physiologic variables collected during the first 24 hours of inpatient. The second approach considered admission data and supplementary clinical data of the patient (vital signs and laboratory results) collected in real-time. The results achieved in the first approach are very poor (accuracy of 73 %). However, when the prediction is made using the data collected in real-time, the results are very interesting (sensitivity of 96.104%). The models induced in second experiment are sensitive to the patient clinical situation and can predict LOS according to the monitored variables. Models for predicting LOS at admission are not suited to the ICU particularities. Alternatively, they should be induced in real-time, using online-learning and considering the most recent patient condition when the model is induced.(undefined

Predicting plateau pressure in intensive medicine for ventilated patients

Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm2O) in a real environment and using real data. The present study explored and assessed the possibility of predicting the Plateau pressure class with high accuracies. The dataset used only contained data provided by the ventilators. The best models are able to predict the Plateau Pressure with an accuracy ranging from 95.52% to 98.71%.This work has been supported by FCT - 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)

A clustering approach for predicting readmissions in intensive medicine

Decision making assumes a critical role in the Intensive Medicine. Data Mining is emerging in the clinical area to provide processes and technologies for transforming data into useful knowledge to support clinical decision makers. Appling clustering techniques to the data available on the patients admitted into Intensive Care Units and knowing which ones correspond to readmissions, it is possible to create meaningful clusters that will represent the base characteristics of readmitted patients. Thus, exploring common characteristics it is possible to prevent discharges that will result into readmissions and then improve the patient outcome and reduce costs. Moreover, readmitted patients present greater difficulty to be recovered. In this work it was followed the Stability and Workload Index for Transfer (SWIFT). A subset of variables from SWIFT was combined with the results from laboratory exams, namely the Lactic Acid and the Leucocytes values, in order to create clusters to identify, in the moment of discharge, patients that probably will be readmitted