Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study

Pirracchio, Romain; Petersen, Maya; Carone, Marco; Resche-Rigon, Matthieu; Chevret, Sylvie; Laan, Mark J. van der

doi:10.1016/s2213-2600(14)70239-5

Cited by 298 publications

(226 citation statements)

References 48 publications

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“…Churpek et al confirmed that logistic regression and random forest outperformed a MEWS 32. Pirrachio et al developed the “Super ICU Learner Algorithm (SICULA)” using a combination of multiple machine learning methods for patients in intensive care units 44. Although machine learning outperformed the existing TTSs, they used more variables.…”

Section: Discussionmentioning

confidence: 99%

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Kwon

Lee²,

Lee³

et al. 2018

JAHA

201

193

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BackgroundIn‐hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false‐alarm rates. We propose a deep learning–based early warning system that shows higher performance than the existing track‐and‐trigger systems.Methods and ResultsThis retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning–based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning–based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity.ConclusionsAn algorithm based on deep learning had high sensitivity and a low false‐alarm rate for detection of patients with cardiac arrest in the multicenter study.

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Section: Discussionmentioning

confidence: 99%

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Kwon

Lee²,

Lee³

et al. 2018

JAHA

201

193

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“…Super learning has been successfully used in medical research [20] and spatial analyses [19] and improved the behaviour classification models from accelerometry, albeit marginally.…”

Section: Super Models: Is It Worth It?mentioning

confidence: 99%

“…The super learner model (SL) seeks to find the optimal combination candidate learners such that it will perform as well or better than any of the learner inputs [19]. Super learning has previously been applied to large medical data sets in order to make survival predictions with considerable success [20], but has until now not been evaluated for its ability to classify behaviour from accelerometry data.…”

Section: Introductionmentioning

confidence: 99%

Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry

Ladds

Thompson²,

Kadar

et al. 2017

Anim Biotelemetry

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Background: Semi-automating the analyses of accelerometry data makes it possible to synthesize large data sets. However, when constructing activity budgets from accelerometry data, there are many methods to extract, analyse and report data and results. For instance, machine learning is a robust approach to classifying data. We used a new method, super learning, that combines base learners (different machine learning methods) in an optimal manner to achieve overall improved accuracy. Other facets of super learning include the number of behavioural categories to predict, the number of epochs (sample window size) used to split data for training and testing and the parameters on which to train the models. Results:The super learner accurately classified behaviour categories with higher accuracy and lower variance than comparative models. For all models tested, using four behaviours, in comparison with six, achieved higher rates of accuracy. The number of epochs chosen also affected the accuracy with smaller epochs (7 and 13) performing better than longer epochs (25 and 75). Conclusions:Correct model selection, training and testing are imperative to creating reliable and valid classification models. To do so means model fitting must use a wide array of selection criteria. We evaluated a number of these including model, number of behaviours to classify and epoch length and then used a parameter grid search to implement the models. We found that all criteria tested contributed to the models' overall accuracies. Fewer behaviour categories and shorter epoch length improved the performance of all models tested. The super learner classified behaviours with higher accuracy and lower variance than other models tested. However, when using this model, users need to consider the additional human and computational time required for implementation. Machine learning is a powerful method for classifying the behaviour of animals from accelerometers. Care and consideration of the modelling parameters evaluated in this study are essential when using this type of statistical analysis.

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“…Complete results of this study have been published in 2015 in the Lancet Respiratory Medicine [25]. We also wished to develop an easily-accessible user-friendly web implementation of our scoring procedure, even despite the complexity of our approach (http://webapps.biostat.berkeley.edu:8080/sicula/).…”

Section: Introductionmentioning

confidence: 99%

Mortality Prediction in the ICU Based on MIMIC-II Results from the Super ICU Learner Algorithm (SICULA) Project

Pirracchio

2016

Secondary Analysis of Electronic Health Records

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Learning ObjectivesIn this chapter, we illustrate the use of MIMIC II clinical data, non-parametric prediction algorithm, ensemble machine learning, and the Super Learner algorithm. IntroductionPredicting mortality in patients hospitalized in intensive care units (ICU) is crucial for assessing severity of illness and adjudicating the value of novel treatments, interventions and health care policies. Several severity scores have been developed with the objective of predicting hospital mortality from baseline patient characteristics, defined as measurements obtained within the first 24 h after ICU admission. The first scores proposed, APACHE [1] (Acute Physiology and Chronic Health Evaluation), APACHE II [2], and SAPS [3] (Simplified Acute Physiology Score), relied upon subjective methods for variable importance measure, namely by prompting a panel of experts to select and assign weights to variables according to perceived relevance for mortality prediction. Further scores, such as the SAPS II [4] were subsequently developed using statistical modeling techniques [4][5][6][7]. To this day, the SAPS II [4] and APACHE II [2] scores remain the most widely used in clinical practice. However, since first being published, they have been modified several times in order to improve their predictive performance [6][7][8][9][10][11]. Despite these extensions of SAPS, predicted hospital mortality remains generally overestimated [8,9,[12][13][14]. As an illustration, Poole et al. [9] compared the SAPS II and the SAPS3 performance in a cohort of more than 28,000 admissions to 10 different Italian ICUs. They concluded that both scores provided unreliable predictions, but unexpectedly the newer SAPS 3 turned out to overpredict mortality more than the

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Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study

Cited by 298 publications

References 48 publications

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry

Mortality Prediction in the ICU Based on MIMIC-II Results from the Super ICU Learner Algorithm (SICULA) Project

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