2,546 research outputs found
Early hospital mortality prediction using vital signals
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.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE
2018 and published in Smart Health journa
Benchmarking machine learning models on multi-centre eICU critical care dataset
Progress of machine learning in critical care has been difficult to track, in
part due to absence of public benchmarks. Other fields of research (such as
computer vision and natural language processing) have established various
competitions and public benchmarks. Recent availability of large clinical
datasets has enabled the possibility of establishing public benchmarks. Taking
advantage of this opportunity, we propose a public benchmark suite to address
four areas of critical care, namely mortality prediction, estimation of length
of stay, patient phenotyping and risk of decompensation. We define each task
and compare the performance of both clinical models as well as baseline and
deep learning models using eICU critical care dataset of around 73,000
patients. This is the first public benchmark on a multi-centre critical care
dataset, comparing the performance of clinical gold standard with our
predictive model. We also investigate the impact of numerical variables as well
as handling of categorical variables on each of the defined tasks. The source
code, detailing our methods and experiments is publicly available such that
anyone can replicate our results and build upon our work.Comment: Source code to replicate the results
https://github.com/mostafaalishahi/eICU_Benchmar
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