An intelligent warning model for early prediction of cardiac arrest in sepsis patients

Javan, Samaneh Layeghian; Sepehri, Mohammad Mehdi; Javan, Malihe Layeghian; Khatibi, Toktam

doi:10.1016/j.cmpb.2019.06.010

Cited by 60 publications

(50 citation statements)

References 14 publications

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“…The limitation of the current ML-based studies is that most studies build prediction models for specific types of patients and use a single ML algorithm for their proposed system. The authors of [42], [44], [51] [52] and [53] developed mortality-prediction models for specific patients such as kidney-failure patients. Although accurate results were obtained in these studies, the application of their models is limited to specific domains.…”

Section: B Ml-based Systemsmentioning

confidence: 99%

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

et al. 2020

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The intensive care unit (ICU) admits the most seriously ill patients requiring extensive monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great risk of dying and for providing suitable interventions to save their lives. Accordingly, early prediction of patients at high mortality risk will enable their provision of appropriate and timely medical services. Although various severity scores and machine-learning models have recently been developed for early mortality prediction, such prediction remains challenging. This paper proposes a novel stacking ensemble approach to predict the mortality of ICU patients. Our approach is more accurate and medically intuitive compared to the literature work. Data were prepared and feature selection was processed under the supervision of the ICU domain expert. The data were split into six modalities based on the expert's decisions. For the prediction process, a separate classifier was selected for each modality based on the performance of the classifiers. We utilized the most popular and diverse classifiers in the literature, including linear discriminant analysis, decision tree (DT), multilayer perceptron, k-nearest neighbor, and logistic regression (LR). Then, a stacking ensemble classifier was constructed and optimized based on the fusion of these five classifier decisions. The framework was evaluated using 10,664 patients from the medical information mart for intensive care (MIMIC III) benchmark dataset. To predict patient mortality, extensive experiments were conducted using the patients' time series data of different lengths. For each patient, the first 6, 12, and 24 hours of the first stay were tested. The results indicate that our model outperformed the state-of-the-art approaches in terms of accuracy (94.4%), F1 score (93.7%), precision (96.4%), recall (91.1%), and area under the receiver operator characteristic (ROC) curve (93.3%). These results demonstrate the ability and efficiency of our approach to predict ICU mortality.

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Section: B Ml-based Systemsmentioning

confidence: 99%

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

et al. 2020

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“…With advances in algorithm technology, it is now possible to identify highly relevant features and discover new ways to utilize medical signals to improve the accuracy and functionality of prediction models to solve medical issues. Compared to prediction models of cardiac arrest generated using traditional methods such as regression method analysis or expert opinion, machine learning can achieve a better performance in many cases 9‐13 . In addition, current risk scores generated using traditional methods have limitations in clinical use due to their poor performance, low sensitivity, and/or a high false‐alarm rate 14 …”

Section: Introductionmentioning

confidence: 99%

“…However, many of these attributes were not valuable and insufficient for stratifying the onset of cardiac arrest 15 . Second, several studies have used machine learning to predict cardiac arrest in pediatric, 13 septic, 9 and ward patients, 12 however, no previous research has used machine learning to predict cardiac arrest in acute coronary syndrome (ACS) patients. Finally, although some models derived from machine learning algorithms can accurately predict cardiac arrest, most studies failed to generate a visualization risk score.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for early prediction of in‐hospital cardiac arrest in patients with acute coronary syndromes

Lin²,

et al. 2021

Clinical Cardiology

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Background Previous studies have used machine leaning to predict clinical deterioration to improve outcome prediction. However, no study has used machine learning to predict cardiac arrest in patients with acute coronary syndrome (ACS). Algorithms are required to generate high‐performance models for predicting cardiac arrest in ACS patients with multivariate features. Hypothesis Machine learning algorithms will significantly improve outcome prediction of cardiac arrest in ACS patients. Methods This retrospective cohort study reviewed 166 ACS patients who had in‐hospital cardiac arrest. Eight machine learning algorithms were trained using multivariate clinical features obtained 24 h prior to the onset of cardiac arrest. All machine learning models were compared to each other and to existing risk prediction scores (Global Registry of Acute Coronary Events, National Early Warning Score, and Modified Early Warning Score) using the area under the receiver operating characteristic curve (AUROC). Results The XGBoost model provided the best performance with regard to AUC (0.958 [95%CI: 0.938–0.978]), accuracy (88.9%), sensitivity (73%), negative predictive value (89%), and F1 score (80%) compared with other machine learning models. The K‐nearest neighbor model generated the best specificity (99.3%) and positive predictive value (93.8%) metrics, but had low and unacceptable values for sensitivity and AUC. Most, but not all, machine learning models outperformed the existing risk prediction scores. Conclusions The XGBoost model, which was generated based on a machine learning algorithm, has high potential to be used to predict cardiac arrest in ACS patients. This proposed model significantly improves outcome prediction compared to existing risk prediction scores.

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“…In recent years, various artificial intelligence (AI) applications have been gradually implemented in the medical field by using machine learning [5,6,7], resulting in more accurate results. There are two types of machine learning: unsupervised learning and supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

Perng

Kao

Kung

et al. 2019

JCM

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In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.

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An intelligent warning model for early prediction of cardiac arrest in sepsis patients

Cited by 60 publications

References 14 publications

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

Machine learning for early prediction of in‐hospital cardiac arrest in patients with acute coronary syndromes

Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

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