Machine Learning Based Early Detection System of Cardiac Arrest

Liu, Ji-Han; Chang, Hsiao-Ko; Wu, Cheng-Tse; Lim, Wee Shin; Wang, Hui-Chih; Jang, Jyh-Shing Roger

doi:10.1109/taai48200.2019.8959922

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…In 16 studies [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62], the focus of AI technologies was to develop an early warning system alerting health care professionals when patients were at risk of going into cardiac arrest in the future. To develop a warning model, most studies used ML model algorithms [49][50][51]53,54,59,61], whereas 5 only used DL-based algorithms [47,48,56,60,62].…”

Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

“…To develop a warning model, most studies used ML model algorithms [49][50][51]53,54,59,61], whereas 5 only used DL-based algorithms [47,48,56,60,62]. Four studies used both ML-and DL-based algorithms [52,55,57,58], comparing them with each other to observe which yielded the best outcome. The ML algorithms used in these studies included LR [50,52,55,58], SVM [50][51][52]58], DT [52,53,57,59], RF [55,57,58], Naive Bayes [57,58], gradient boosting [58], Bayesian networks [49], AdaBoost [57], transfer learning [54], and multichannel Hidden Markov Model [61].…”

Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

“…Four studies used both ML-and DL-based algorithms [52,55,57,58], comparing them with each other to observe which yielded the best outcome. The ML algorithms used in these studies included LR [50,52,55,58], SVM [50][51][52]58], DT [52,53,57,59], RF [55,57,58], Naive Bayes [57,58], gradient boosting [58], Bayesian networks [49], AdaBoost [57], transfer learning [54], and multichannel Hidden Markov Model [61]. The KNN [52,58], artificial neural network [48,58,61], long short-term memory [47,56,59], and recurrent neural network [47,55,56,62] algorithms were used in the studies to constitute a DL-based early warning system.…”

Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

“…The KNN [52,58], artificial neural network [48,58,61], long short-term memory [47,56,59], and recurrent neural network [47,55,56,62] algorithms were used in the studies to constitute a DL-based early warning system. A total of 10 of the studies compared their outcomes to existing or traditional early warning systems [47,48,50,52,53,[56][57][58]60,62]. The studies compared their models to scoring systems such as the Modified Early Warning Score [48,50,52,53,56,58,60,62], Early Warning Score [57], National Early Warning Score [60], and Pediatric Early Warning Score [47].…”

Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

“…A total of 10 of the studies compared their outcomes to existing or traditional early warning systems [47,48,50,52,53,[56][57][58]60,62]. The studies compared their models to scoring systems such as the Modified Early Warning Score [48,50,52,53,56,58,60,62], Early Warning Score [57], National Early Warning Score [60], and Pediatric Early Warning Score [47]. Only 1 study showed similar outcomes when using an AI model compared with a traditional warning system [53], whereas, in other studies, the AI-based model outperformed the system it was compared with.…”

Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

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Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Alamgir¹,

Mousa²,

Shah³

2021

JMIR Med Inform

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Background Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. Objective This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. Methods A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). Conclusions AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.

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Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

Section: Development Of An Early Warning System Using Aimentioning

confidence: 99%

See 3 more Smart Citations

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Alamgir¹,

Mousa²,

Shah³

2021

JMIR Med Inform

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A predictive framework in healthcare: Case study on cardiac arrest prediction

Javan

Sepehri

2021

Artificial Intelligence in Medicine

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Development of A Pharmaceutical Early Warning Scoring Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

Chang¹,

Wang²,

Huang³

et al. 2020

Preprint

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BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a pharmaceutical early warning model to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose a new early warning score model for detecting cardiac arrest via pharmaceutical classification and by using a sliding window; we apply learning-based algorithms to time-series data for a Pharmaceutical Early Warning Scoring Model (PEWSM). By treating pharmaceutical features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits, and replenishers and regulators of water and electrolytes. The best AUROC of bits is 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, LSTM yields better performance with time-series data. The proposed PEWSM, which offers 4-hour predictions, is better than the National Early Warning Score (NEWS) in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

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Machine Learning Based Early Detection System of Cardiac Arrest

Cited by 6 publications

References 12 publications

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

A predictive framework in healthcare: Case study on cardiac arrest prediction

Development of A Pharmaceutical Early Warning Scoring Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

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