Decision tree model for predicting in‐hospital cardiac arrest among patients admitted with acute coronary syndrome

Li, Hong; Wu, Ting; Yang, Dongliang; Guo, Yuan-Lin; Liu, Pei Chang; Chen, Yuan; Xiao, Li Ping

doi:10.1002/clc.23255

Cited by 26 publications

(10 citation statements)

References 25 publications

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“…In a study on the prognosis of breast cancer patients, a decision tree model and a classical logistic regression model were constructed, respectively, with the predictive performance of the different models indicating that the decision tree model showed stronger predictive power when using real clinical data [ 38 ]. Similarly, the decision tree model has been applied to other areas of clinical medicine, including diagnosis of kidney stones [ 39 ], predicting the risk of sudden cardiac arrest [ 40 ], and exploration of the risk factors of type II diabetes [ 41 ]. A common feature of these studies is the use of a decision tree model to explore the interaction between variables and classify subjects into homogeneous categories based on their observed characteristics.…”

Section: Data-mining Algorithms For Clinical Big Datamentioning

confidence: 99%

Data mining in clinical big data: the frequently used databases, steps, and methodological models

et al. 2021

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Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

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Section: Data-mining Algorithms For Clinical Big Datamentioning

confidence: 99%

Data mining in clinical big data: the frequently used databases, steps, and methodological models

et al. 2021

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“…We constructed and conducted branch reduction on the decision tree model using the CHAID algorithm[ 16 ]. Inpatient hospitalization costs were taken as the dependent variable, and age, gender, operation times, LOS, payment method, wound position, wound type and operation type were used as classification nodes.…”

Section: Resultsmentioning

confidence: 99%

Analysis of hospitalization costs related to fall injuries in elderly patients

Su¹,

Fu²,

Zhao³

et al. 2021

WJCC

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BACKGROUND With the aging world population, the incidence of falls has intensified and fall-related hospitalization costs are increasing. Falls are one type of event studied in the health economics of patient safety, and many developed countries have conducted such research on fall-related hospitalization costs. However, China, a developing country, still lacks large-scale studies in this area. AIM To investigate the factors related to the hospitalization costs of fall-related injuries in elderly inpatients and establish factor-based, cost-related groupings. METHODS A retrospective study was conducted. Patient information and cost data for elderly inpatients (age ≥ 60 years, n = 3362) who were hospitalized between 2016 and 2019 due to falls was collected from the medical record systems of two grade-A tertiary hospitals in China. Quantile regression (QR) analysis was used to identify the factors related to fall-related hospitalization costs. A decision tree model based on the chi-squared automatic interaction detector algorithm for hospitalization cost grouping was built by setting the factors in the regression results as separation nodes. RESULTS The total hospitalization cost of fall-related injuries in the included elderly patients was 180479203.03 RMB, and the reimbursement rate of medical benefit funds was 51.0% (92039709.52 RMB/180479203.03 RMB). The medical material costs were the highest component of the total hospitalization cost, followed (in order) by drug costs, test costs, treatment costs, integrated medical service costs and blood transfusion costs The QR results showed that patient age, gender, length of hospital stay, payment method, wound position, wound type, operation times and operation type significantly influenced the inpatient cost ( P < 0.05). The cost grouping model was established based on the QR results, and age, length of stay, operation type, wound position and wound type were the most important influencing factors in the model. Furthermore, the cost of each combination varied significantly. CONCLUSION Our grouping model of hospitalization costs clearly reflected the key factors affecting hospitalization costs and can be used to strengthen the reasonable control of these costs.

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“…Therefore, the implementation of such tools can be complex and often require coordination at different levels. For instance, the risk of in-hospital cardiac arrest has been predicted using a decision tree [ 58 ], while other ML algorithms have been used for risk stratification of chest pain patients using coronary CTA data [ 52 ]. These tools, once externally validated and implemented to act in real-time in clinical settings, could help reduce the time for treatment and help save lives.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

Abedi

Razavi

Khan

et al. 2021

JCM

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The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions—from heart failure to stroke—has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.

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Decision tree model for predicting in‐hospital cardiac arrest among patients admitted with acute coronary syndrome

Cited by 26 publications

References 25 publications

Data mining in clinical big data: the frequently used databases, steps, and methodological models

Data mining in clinical big data: the frequently used databases, steps, and methodological models

Analysis of hospitalization costs related to fall injuries in elderly patients

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

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