Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework.

BACKGROUND: One of the significant problems in the field of healthcare is the low survival rate of people who have experienced sudden cardiac arrest. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Traditional statistical methods have been used to predict cardiac arrest. They have often analyzed group-level differences using a limited number of variables. On the other hand, machine learning approach, which is part of a growing trend of predictive medical analysis, has provided personalized predictive analyses on more complex data and produced remarkable results. OBJECTIVE: This paper has two aims. First, it offers a systematic review to evaluate the capability and performance of machine learning techniques in predicting the risk of cardiac arrest. Second, it offers an integrative framework to synthesize the researches in this field. METHOD: A systematic review of cardiac arrest prediction studies was carried out through Pubmed, ScienceDirect, Google Scholar and SpringerLink databases. These studies used machine learning techniques and were conducted between the years 2000 and 2018. RESULTS: From a total of 1617 papers retrieved from the literature search, 75 studies were included in the final analysis. In order to explore how machine learning techniques were employed to predict cardiac arrest, a multi-layered framework was proposed. Each layer of the framework represents a classification of the current literature and contains taxonomies of relevant observed information. The framework integrates these classifications and illustrates the relative influence of a layer on other layers. The included papers were analyzed and synthesized through this framework. The used machine learning techniques were evaluated in terms of application and efficiency. The results illustrated the prediction capability of machine learning methods in predicting cardiac arrest. CONCLUSION: According to the results, machine learning techniques can improve the outcome of cardiac arrest prediction. However, future research should be carried out to evaluate the efficiency of rarely-used algorithms and to address the challenges of external validation, implementation and adoption of machine learning models in real clinical environments.

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

Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the applications of predictive analytics in medical environments. Various studies have used machine learning techniques for this purpose. However, there is no specific standard for choosing prediction models for different medical purposes. In this paper, the ISAF framework was proposed for choosing appropriate prediction models with regard to the properties of the classification methods. As one of the case study applications, a prognostic model for predicting cardiac arrests in sepsis patients was developed step by step through the ISAF framework. Finally, a new modified stacking model produced the best results. We predict 85 % of heart arrest cases one hour before the incidence (sensitivity> = 0.85) and 73 % of arrest cases 25 h before the occurrence (sensitivity> = 0.73). The results indicated that the proposed prognostic model has significantly improved the prediction results compared to the two standard systems of APACHE II and MEWS. Furthermore, compared to previous research, the proposed model has extended the prediction interval and improved the performance criteria.

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

BACKGROUND: Sepsis-associated cardiac arrest is a common issue with the low survival rate. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Several studies have been conducted to predict cardiac arrest using machine learning. However, no previous research has used machine learning for predicting cardiac arrest in adult sepsis patients. Moreover, the potential of some techniques, including ensemble algorithms, has not yet been addressed in improving the prediction outcomes. It is required to find methods for generating high-performance predictions with sufficient time lapse before the arrest. In this regard, various variables and parameters should also been examined. OBJECTIVE: The aim was to use machine learning in order to propose a cardiac arrest prediction model for adult patients with sepsis. It is required to predict the arrest several hours before the incidence with high efficiency. The other goal was to investigate the effect of the time series dynamics of vital signs on the prediction of cardiac arrest. METHOD: 30 h clinical data of every sepsis patients were extracted from Mimic III database (79 cases, 4532 controls). Three datasets (multivariate, time series and combined) were created. Various machine learning models for six time groups were trained on these datasets. The models included classical techniques (SVM, decision tree, logistic regression, KNN, GaussianNB) and ensemble methods (gradient Boosting, XGBoost, random forest, balanced bagging classifier and stacking). Proper solutions were proposed to address the challenges of missing values, imbalanced classes of data and irregularity of time series. RESULTS: The best results were obtained using a stacking algorithm and multivariate dataset (accuracy = 0.76, precision = 0.19, sensitivity = 0.77, f1-score = 0.31, AUC= 0.82). The proposed model predicts the arrest incidence of up to six hours earlier with the accuracy and sensitivity over 70%. CONCLUSION: We illustrated that machine learning techniques, especially ensemble algorithms have high potentials to be used in prognostic systems for sepsis patients. The proposed model, in comparison with the exiting warning systems including APACHE II and MEWS, significantly improved the evaluation criteria. According to the results, the time series dynamics of vital signs are of great importance in the prediction of cardiac arrest incidence in sepsis patients.