Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units

Hsieh, Meng Hsuen; Hsieh, Meng Ju; Chen, Chin‐Ming; Hsieh, Chih-Chang; Chao, Chien‐Ming; Lai, Chih‐Cheng

doi:10.1038/s41598-018-35582-2

Cited by 41 publications

(45 citation statements)

References 30 publications

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“…Various analytical tools are currently used in clinical practice to support decision-making on various clinical parameters, including MAP [2][3][4][5][6][7][8][9][10][11]. These clinical decisionmaking tools mostly employ machine learning techniques using algorithms such as LR, random forest ( RF), supported vector machine ( SVM), and deep learning.…”

Section: A Related Workmentioning

confidence: 99%

“…SVM has been proposed to monitor cardio-cerebrovascular hemorrhage in [9]. SVM, RF, LR, and ANN have been employed and their efficacy in predicting mortality rates has been compared in [10]. While multilayer perception (MLP), SVM, deep learning, and Naives Bayes were compared to predict ectopic pregnancy in [11].…”

Section: A Related Workmentioning

confidence: 99%

“…As a result, evolving research activity has begun to develop real-time decision support tools to predict MAP to enable preventive preparations and reduce complications [1][2][3][4][5][6]. The use of machine learning in clinical decision support systems has become a current research hotspot [2][3][4][5][6][7][8][9][10][11]. These systems are based on offline analysis to save the challenge of processing streams in real time [3][4][5][6][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…The use of machine learning in clinical decision support systems has become a current research hotspot [2][3][4][5][6][7][8][9][10][11]. These systems are based on offline analysis to save the challenge of processing streams in real time [3][4][5][6][9][10][11]. The latter requires machine learning algorithms that are capable of adapting to high-speed streams and their fast-changing characteristics [12].…”

Section: Introductionmentioning

confidence: 99%

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A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

et al. 2020

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Mean arterial pressure (MAP) is an important clinical parameter to evaluate the health of critically ill patients in intensive care units. Thus, the real time clinical decision support systems detecting anomalies and deviations in MAP enable early interventions and prevent serious complications. The state-ofthe-art decision support systems are based on a three-phase method that applies offline training, transfer learning, and retraining at the bedside. Their applicability in critical care units is challenging with delay and inaccuracy. In this paper, we propose a real time clinical decision support system forecasting the MAP status at the bedside using a new machine learning structure. The proposed system works in real time at the bedside without requiring the offline phase for training using large datasets. It thereby enables timely interventions and improved healthcare services. The proposed machine learning structure includes two stages. Stage I applies online learning using hierarchical temporal memory (HTM) to enable real time stream processing and provides unsupervised predictions. To the best of our knowledge, this is the first time it is applied to medical signals. Stage II is a long short-term memory (LSTM) classifier that forecasts the status of the patient's MAP ahead of time based on Stage I stream predictions. We perform a thorough performance evaluation of the proposed system and compare it with the state-of-the-art systems employing logistic regression (LR). The comparison shows the proposed system outperforms LR in terms of the classification accuracy, recall, precision, and area under the receiver operation curve (AUROC). INDEX TERMS Clinical decision support, classification, hierarchical temporal memory (HTM), long shortterm memory (LSTM), machine learning, real time prediction.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

et al. 2020

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“…The superior performance of machine learning has been well demonstrated in mortality prediction over SAPS II score, prediction of unplanned extubations and prediction of ICU readmissions. [3][4][5] However, the achievement of machine learning has been slow to be recognized in the medical community. Many would see machine learning as a 'black-box' model, and question how the model derives such results.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Model for Mortality Prediction in ICU: A Multicenter Study

Fong¹,

Ng³

et al. 2020

Preprint

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Background: Researchers have long been struggling to improve the disease severity score in mortality prediction in ICU. The digitalization of medical health records and advancement of computation power have promoted the use of machine learning in critical care. This study aimed to develop an interpretable machine learning model using datasets from multicenters, and to compare with the APACHE IV, in predicting hospital mortality of patients admitted to ICU.Method: The datasets were assembled from the eICU database including 136145 patients across 208 hospitals throughout the U.S. and 5 ICUs in Hong Kong, including 10909 patients. The two datasets were first combined into one large dataset before 80:20 stratified split into the training set and the test set. The XGBoost machine algorithm was chosen to predict the hospital mortality. The variables in the model were the same as those included in the APACHE IV score. The discrimination and calibration of the model were assessed. The model would be interpreted using the Shapley Additive explanations values.Results: Of the 147054 patients in the whole cohort, the hospital mortality was 9.3%. The area under the precision-recall curve for the XGBoost algorithm was 0.57, and 0.49 for APACHE IV. Similarly, the XGBoost reached an area under the receiving operating curve (AUROC) of 0.90, while APACHE IV had an AUROC of 0.87. Additionally, the XGBoost algorithm showed better calibration than the APACHE IV. The three most important variables were age, heart rate, and whether the patient was on ventilator.Conclusions: The severity score developed by machine learning model using mutlicenter datasets outperformed the APACHE IV in predicting hospital mortality for patients admitted to ICU.

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Scoring for Hemorrhage Severity in Traumatic Injury

Shickel

Balch

Aggas

et al. 2022

Biomarkers in Trauma, Injury and Critical Care

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Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units

Cited by 41 publications

References 30 publications

A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

Interpretable Machine Learning Model for Mortality Prediction in ICU: A Multicenter Study

Scoring for Hemorrhage Severity in Traumatic Injury

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