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Machine Learning Model Could Predict Outcomes Following Cardiac Arrest


A novel machine learning model could help predict mortality and neurological outcomes post-cardiac arrest, according to a new Johns Hopkins study. Presented at the Society of Critical Care Medicine's 49th Annual Critical Care Congress in Orlando, FL, study results indicate the new model demonstrated significantly improved prediction capabilities compared to the reference APACHE model. "The objectives of our study were to first predict the neurological outcome and mortality at discharge using data only from the first 24 hours of ICU admission and the second objective was to determine whether utilizing physiologic time series (PTS) data, specifically just features from the bedside monitoring data, are useful in terms of model performance," said lead investigator Hanbiehn Kim, MBE, of Johns Hopkins University, during his presentation. Using the Philips eICU database, which includes over 200,000 patients from 208 hospitals, Kim and colleagues from Johns Hopkins Hospital extracted data on cardiac arrest patients who were mechanically ventilated. Of note, this database includes PTS data from patient bedside bio-monitors that recorded heart rate, oxygen saturation, blood pressure, and respiratory rate at 5-minute intervals.

Predicting Mortality of Intensive Care Patients via Learning about Hazard

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Patients in intensive care units (ICU) are acutely ill and have the highest mortality rates for hospitalized patients. Predictive models and planning system could forecast and guide interventions to prevent the hazardous deterioration of patients’ physiologies, thereby giving the opportunity of employing machine learning and inference to assist with the care of ICU patients. We report on the construction of a prediction pipeline that estimates the probability of death by inferring rates of hazard over time, based on patients’ physiological measurements. The inferred model provided the contribution of each variable and information about the influence of sets of observations on the overall risks and expected trajectories of patients.

Developing an ICU scoring system with interaction terms using a genetic algorithm Machine Learning

ICU mortality scoring systems attempt to predict patient mortality using predictive models with various clinical predictors. Examples of such systems are APACHE, SAPS and MPM. However, most such scoring systems do not actively look for and include interaction terms, despite physicians intuitively taking such interactions into account when making a diagnosis. One barrier to including such terms in predictive models is the difficulty of using most variable selection methods in high-dimensional datasets. A genetic algorithm framework for variable selection with logistic regression models is used to search for two-way interaction terms in a clinical dataset of adult ICU patients, with separate models being built for each category of diagnosis upon admittance to the ICU. The models had good discrimination across all categories, with a weighted average AUC of 0.84 (>0.90 for several categories) and the genetic algorithm was able to find several significant interaction terms, which may be able to provide greater insight into mortality prediction for health practitioners. The GA selected models had improved performance against stepwise selection and random forest models, and provides greater flexibility in terms of variable selection by being able to optimize over any modeler-defined model performance metric instead of a specific variable importance metric.

A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome Prediction Machine Learning

Patients resuscitated from cardiac arrest (CA) face a high risk of neurological disability and death, however pragmatic methods are lacking for accurate and reliable prognostication. The aim of this study was to build computational models to predict post-CA outcome by leveraging high-dimensional patient data available early after admission to the intensive care unit (ICU). We hypothesized that model performance could be enhanced by integrating physiological time series (PTS) data and by training machine learning (ML) classifiers. We compared three models integrating features extracted from the electronic health records (EHR) alone, features derived from PTS collected in the first 24hrs after ICU admission (PTS24), and models integrating PTS24 and EHR. Outcomes of interest were survival and neurological outcome at ICU discharge. Combined EHR-PTS24 models had higher discrimination (area under the receiver operating characteristic curve [AUC]) than models which used either EHR or PTS24 alone, for the prediction of survival (AUC 0.85, 0.80 and 0.68 respectively) and neurological outcome (0.87, 0.83 and 0.78). The best ML classifier achieved higher discrimination than the reference logistic regression model (APACHE III) for survival (AUC 0.85 vs 0.70) and neurological outcome prediction (AUC 0.87 vs 0.75). Feature analysis revealed previously unknown factors to be associated with post-CA recovery. Results attest to the effectiveness of ML models for post-CA predictive modeling and suggest that PTS recorded in very early phase after resuscitation encode short-term outcome probabilities.

Early Hospital Mortality Prediction using Vital Signals Machine Learning

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.