Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

Chae, Minsu; Han, Sang-Wook; Gil, Hyo‐Wook; Cho, Namjun; Lee, HwaMin

doi:10.3390/diagnostics11071255

Cited by 16 publications

(10 citation statements)

References 35 publications

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“…We confirmed that the check of the measurement of laboratory data did not train as intended. We only used 8 h of past patient data in the previous study [23]. In this study, we excluded a patient whose laboratory data were not measured.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, we excluded patients whose albumin, bilirubin, creatinine, platelet, hemoglobin, or white blood cell rates were never measured. In our previous research [23], we used input parameters such as laboratory data and laboratory check variables, but it was not properly considered. Table 1 shows the characteristics of our study population.…”

Section: Methodsmentioning

confidence: 99%

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Machine Learning-Based Cardiac Arrest Prediction for Early Warning System

et al. 2022

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The early warning system detects early and responds quickly to emergencies in high-risk patients, such as cardiac arrest in hospitalized patients. However, traditional early warning systems have the problem of frequent false alarms due to low positive predictive value and sensitivity. We conducted early prediction research on cardiac arrest using time-series data such as biosignal and laboratory data. To derive the data attributes that affect the occurrence of cardiac arrest, we performed a correlation analysis between the occurrence of cardiac arrest and the biosignal data and laboratory data. To improve the positive predictive value and sensitivity of early cardiac arrest prediction, we evaluated the performance according to the length of the time series of measured biosignal data, laboratory data, and patient data range. We propose a machine learning and deep learning algorithm: the decision tree, random forest, logistic regression, long short-term memory (LSTM), gated recurrent unit (GRU) model, and the LSTM–GRU hybrid model. We evaluated cardiac arrest prediction models. In the case of our proposed LSTM model, the positive predictive value was 85.92% and the sensitivity was 89.70%.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Cardiac Arrest Prediction for Early Warning System

et al. 2022

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“…This shows that the features of the Cleveland dataset are more vital when compared to Framingham. Figure 3 shows the training and validation loss for best performing algorithm i.e., artificial neural network (ANN) [21][22][23][24]. Since the training loss and validation loss are deviating i.e., their incrementing nature is opposite to each other shows that the algorithm is not overfitting which is a good sign.…”

Section: Algorithmmentioning

confidence: 99%

A Deep Learning Framework for Prediction of Cardiopulmonary Arrest

Potluri,

Sahoo,

Satapathy

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The cardiopulmonary arrest is a major issue in any country. Gone are the days when it used to happen to those who are aged but now it is a major concern emerging among adolescents as well. According to the World Health Organization (WHO), cardiac arrest and stroke is still a major concern and remains a public health crisis. In past years India has witnessed many cases of heart related issues which used to occur predominantly among people having high cholesterol. But now the scenario has changed, and cases have been observed in people having normal cholesterol levels. There are several factors involved in heart stroke such as age, sex, blood pressure, etc. which are used by doctors to monitor and diagnose the same. OBJECTIVES: This paper focuses on different predictive models and ways to improve the accuracy of prediction by analyzing datasets on how they affect the accuracy of certain algorithms. METHODS: The factors contributing to heart issues can be used as a beacon to predict the stroke and help an individual to further consult a doctor beforehand. The idea is to target the datasets and the prediction algorithms of deep learning including advanced ones to improvise it and attain a better result. RESULTS: This paper brings out the comparative analysis among neural network techniques like ANN, Transfer Learning, MAML and LRP in which ANN showed the best result by giving the highest accuracy of 94%. CONCLUSION: Furthermore, it discusses a new attribute called “gamma prime fibrinogen” which could be used in the future to boost prediction performance.

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“…In this paper, we piloted the use of an implementation guide that combines IHCA with vital signs, which have been widely adopted in IHCA assessment [4,21] and play an important role in inpatient deterioration detection. Many health care institutions have developed early warning score systems to identify hospitalized patients that are at risk of deterioration, and in recent years, they have begun to incorporate machine learning-based models into this process.…”

Section: Principal Findingsmentioning

confidence: 99%

“…Vital signs have been an important indicator in many studies [1][2][3]. In recent years, researchers have used these data in studies of predictive models for in-hospital cardiac arrest (IHCA) [1,4]. In a real-world medical workflow, complete data may be obtained once every 4 to 8 hours.…”

Section: Introductionmentioning

confidence: 99%

Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study

Tseng¹,

Su²,

Lai³

2022

JMIR Med Inform

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Background Vital signs have been widely adopted in in-hospital cardiac arrest (IHCA) assessment, which plays an important role in inpatient deterioration detection. As the number of early warning systems and artificial intelligence applications increases, health care information exchange and interoperability are becoming more complex and difficult. Although Health Level 7 Fast Healthcare Interoperability Resources (FHIR) have already developed a vital signs profile, it is not sufficient to support IHCA applications or machine learning–based models. Objective In this paper, for IHCA instances with vital signs, we define a new implementation guide that includes data mapping, a system architecture, a workflow, and FHIR applications. Methods We interviewed 10 experts regarding health care system integration and defined an implementation guide. We then developed the FHIR Extract Transform Load to map data to FHIR resources. We also integrated an early warning system and machine learning pipeline. Results The study data set includes electronic health records of adult inpatients who visited the En-Chu-Kong hospital. Medical staff regularly measured these vital signs at least 2 to 3 times per day during the day, night, and early morning. We used pseudonymization to protect patient privacy. Then, we converted the vital signs to FHIR observations in the JSON format using the FHIR Extract Transform Load application. The measured vital signs include systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature. According to clinical requirements, we also extracted the electronic health record information to the FHIR server. Finally, we integrated an early warning system and machine learning pipeline using the FHIR RESTful application programming interface. Conclusions We successfully demonstrated a process that standardizes health care information for inpatient deterioration detection using vital signs. Based on the FHIR definition, we also provided an implementation guide that includes data mapping, an integration process, and IHCA assessment using vital signs. We also proposed a clarifying system architecture and possible workflows. Based on FHIR, we integrated the 3 different systems in 1 dashboard system, which can effectively solve the complexity of the system in the medical staff workflow.

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Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

Cited by 16 publications

References 35 publications

Machine Learning-Based Cardiac Arrest Prediction for Early Warning System

Machine Learning-Based Cardiac Arrest Prediction for Early Warning System

A Deep Learning Framework for Prediction of Cardiopulmonary Arrest

Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study

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