Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain

Abstract

Background A big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in the emergency department (ED). Therefore, we conducted the present study to clarify it. Methods In total, 85,254 ED patients with chest pain in three hospitals between 2009 and 2018 were identified. We randomized the patients into a 70%/30% split for ML model training and testing. We used 14 clinical variables from their electronic health records to construct a random forest model with the synthetic minority oversampling technique preprocessing algorithm to predict acute myocardial infarction (AMI) < 1 month and all-cause mortality < 1 month. Comparisons of the predictive accuracies among random forest, logistic regression, support-vector clustering (SVC), and K-nearest neighbor (KNN) models were also performed. Results Predicting MACE using the random forest model produced areas under the curves (AUC) of 0.915 for AMI < 1 month and 0.999 for all-cause mortality < 1 month. The random forest model had better predictive accuracy than logistic regression, SVC, and KNN. We further integrated the AI prediction model with the HIS to assist physicians with decision-making in real time. Validation of the AI prediction model by new patients showed AUCs of 0.907 for AMI < 1 month and 0.888 for all-cause mortality < 1 month. Conclusions An AI real-time prediction model is a promising method for assisting physicians in predicting MACE in ED patients with chest pain. Further studies to evaluate the impact on clinical practice are warranted.

An App Developed for Predicting Nurse Intention to Quit the Job (NIQJ) Using the Artificial Neural Networks(ANN) in Microsoft Excel: Population-Based Questionnaire Study

“…There are different types of algorithms for classi cation in Machine Learning [34,35], such as Logistic Regression, Support Vector Machines(SVM) [36], Naïve Bayes, Random Forest Classi cation, ANN, CNN [18,20,22], and k-nearest neighbors algorithm (KNN) [37]. ANN was shown superior with 93.2% classi cation accuracy in the previous study [36], similar to our results, although only two models(CNN and ANN) were compared.…”

Feasibility of electronic patient-reported outcome monitoring with self-management advice and warning system in aplastic anemia and paroxysmal nocturnal hemoglobinuria – a pilot study (ePRO-AA-PNH)

“…For inexperienced physicians the tool might be especially helpful to estimate the severity and monitor the disease course, as it is di cult to gain extensive clinical experience in these rare diseases. The multidimensional recording of symptoms, blood values and therapies could also form the basis for an automatic prediction of adverse events and disease course as has been attempted in other elds (34,35), in particular when considering the future bene ts of arti cial intelligence and machine learning.…”

The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center

“…Due to the success of the AI systems implementation in the ED, the AI center revised the systems depending on the needs of the other departments and promoted their use (e.g., outcome prediction of burns for surgical treatment, anesthesia risk assessment, mortality prediction and timing prediction for weaning mechanical ventilation in ICU, and fall detection in elderly wards). Figure 3 shows a screenshot of the outcome prediction system in ED patients with chest pain [8]. The system has been integrated with the existing emergency computerized order entry system.…”

“…So far, all of the hospital's branches have installed the AI systems in several departments, including the Emergency, Surgery, Anesthesiology, Intensive Care Unit, and Nursing departments. In addition, as the AI Center is gradually developing and improving the AI systems, it was able to study and observe the use of AI in the healthcare setting and publish them in international journals [8,9]. Based on the experience of Chi Mei Hospital in the implementation of its AI systems, this paper reported the current situation and the challenges being faced by healthcare AI from the perspectives of the government, hospitals, users, and manufacturers.…”