Mortality assessment in intensive care units via adverse events using artificial neural networks

This work presents a novel approach for the prediction of mortality in intensive care units (ICUs) based on the use of adverse events, which are defined from four bedside alarms, and artificial neural networks (ANNs). This approach is compared with two logistic regression (LR) models: the prognostic model used in most of the European ICUs, based on the simplified acute physiology score (SAPS II), and a LR that uses the same input variables of the ANN model. Materials and Methods: A large dataset was considered, encompassing forty two ICUs of nine European countries. The recorded features of each patient include the final outcome, the case mix (e.g. age) and the intermediate outcomes, defined as the daily averages of the out of range values of four biometrics (e.g. heart rate). The SAPS II score requires seventeen static variables (e.g. serum sodium), which are collected within the first day of the patient's admission. A nonlinear least squares method was used to calibrate the LR models while the ANNs are made up of multilayer perceptrons trained by the RPROP algorithm. A total of 13164 adult patients were randomly divided into training (66%) and test (33%) sets. The two methods were evaluated in terms of receiver operator characteristic (ROC) curves. Results: The event based models predicted the outcome more accurately than the currently used SAPS II model (P<0.05), with ROC areas within the ranges 83.9-87.1% (ANN) and 82.6-85.2% (LR) vs 80% (LR SAPS II). When using the same inputs, the ANNs outperform the LR (improvement of 1.3-2%). Conclusion: Better prognostic models can be achieved by adopting low cost and real-time intermediate outcomes rather than static data.BIOMED Project BMH4-CT96-0817 for the provision of part of the EURICUS II data.FRICE

A Neural Network Model for Mortality Prediction in ICU

Abstrac

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%

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

Computer modeling and signal analysis of cardiovascular physiology

This dissertation aims to study cardiovascular physiology from the cellular level to the whole heart level to the body level using numerical approaches. A mathematical model was developed to describe electromechanical interaction in the heart. The model integrates cardio-electrophysiology and cardiac mechanics through excitation-induced contraction and deformation-induced currents. A finite element based parallel simulation scheme was developed to investigate coupled electrical and mechanical functions. The developed model and numerical scheme were utilized to study cardiovascular dynamics at cellular, tissue and organ levels. The influence of ion channel blockade on cardiac alternans was investigated. It was found that the channel blocker may significantly change the critical pacing period corresponding to the onset of alternans as well as the alternans’ amplitude. The influence of electro-mechanical coupling on cardiac alternans was also investigated. The study supported the earlier assumptions that discordant alternans is induced by the interaction of conduction velocity and action potential duration restitution at high pacing rates. However, mechanical contraction may influence the spatial pattern and onset of discordant alternans. Computer algorithms were developed for analysis of human physiology. The 12-lead electrocardiography (ECG) is the gold standard for diagnosis of various cardiac abnormalities. However, disturbances and mistakes may modify physiological waves in ECG and lead to wrong diagnoses. This dissertation developed advanced signal analysis techniques and computer software to detect and suppress artifacts and errors in ECG. These algorithms can help to improve the quality of health care when integrated into medical devices or services. Moreover, computer algorithms were developed to predict patient mortality in intensive care units using various physiological measures. Models and analysis techniques developed here may help to improve the quality of health care

Detecting Hazardous Intensive Care Patient Episodes Using Real-time Mortality Models

PhD thesisThe modern intensive care unit (ICU) has become a complex, expensive, data-intensive environment. Caregivers maintain an overall assessment of their patients based on important observations and trends. If an advanced monitoring system could also reliably provide a systemic interpretation of a patient's observations it could help caregivers interpret these data more rapidly and perhaps more accurately. In this thesis I use retrospective analysis of mixed medical/surgical intensive care patients to develop predictive models. Logistic regression is applied to 7048 development patients with several hundred candidate variables. These candidate variables range from simple vitals to long term trends and baseline deviations. Final models are selected by backward elimination on top cross-validated variables and validated on 3018 additional patients. The real-time acuity score (RAS) that I develop demonstrates strong discrimination ability for patient mortality, with an ROC area (AUC) of 0.880. The final model includes a number of variables known to be associated with mortality, but also computationally intensive variables absent in other severity scores. In addition to RAS, I also develop secondary outcome models that perform well at predicting pressor weaning (AUC=0.825), intraaortic balloon pump removal (AUC=0.816), the onset of septic shock (AUC=0.843), and acute kidney injury (AUC=0.742). Real-time mortality prediction is a feasible way to provide continuous risk assessment for ICU patients. RAS offers similar discrimination ability when compared to models computed once per day, based on aggregate data over that day. Moreover, RAS mortality predictions are better at discrimination than a customized SAPS II score (Day 3 AUC=0.878 vs AUC=0.849, p < 0.05). The secondary outcome models also provide interesting insights into patient responses to care and patient risk profiles. While models trained for specifically recognizing secondary outcomes consistently outperform the RAS model at their specific tasks, RAS provides useful baseline risk estimates throughout these events and in some cases offers a notable level of predictive utility.Ph.D. in Computer Scienc