Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach

Li, Lihua; Ji, Jiayi; Sanderson, Mark

doi:10.1186/s12913-020-05936-6

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…Shah et al [ 42 ] utilized SVMs, random forests, and K-nearest neighbors to monitor the physical activities of patients with epileptic seizures by treating wireless devices as sensors in medical cyber-physical systems. Hu et al [ 43 ] utilized a flexible tree-driven principled variable selection approach to facilitate the identification and ranking of the significance of determinants of high medical expenses and their effects among patients with breast cancers.…”

Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

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Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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“…We applied the XGBoost machine learning approach to determine each determinant's importance in predicting the 4 outcomes. The approach has been used in studies about cardiovascular health outcomes and health care costs for multiple diseases, 22 , 23 , 24 , 25 , 26 but rarely in CVD health care costs. The machine learning approach has several advantages over traditional statistical methods.…”

Section: Methodsmentioning

confidence: 99%

“…This approach has also been applied to understand the determinants of health care costs, such as end‐of‐life care costs and costs for breast cancer. 25 , 26 , 27 , 28 …”

mentioning

confidence: 99%

Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning

Sun

Yao

et al. 2023

JAHA

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Background Existing studies on cardiovascular diseases (CVDs) often focus on individual‐level behavioral risk factors, but research examining social determinants is limited. This study applies a novel machine learning approach to identify the key predictors of county‐level care costs and prevalence of CVDs (including atrial fibrillation, acute myocardial infarction, congestive heart failure, and ischemic heart disease). Methods and Results We applied the extreme gradient boosting machine learning approach to a total of 3137 counties. Data are from the Interactive Atlas of Heart Disease and Stroke and a variety of national data sets. We found that although demographic composition (eg, percentages of Black people and older adults) and risk factors (eg, smoking and physical inactivity) are among the most important predictors for inpatient care costs and CVD prevalence, contextual factors such as social vulnerability and racial and ethnic segregation are particularly important for the total and outpatient care costs. Poverty and income inequality are the major contributors to the total care costs for counties that are in nonmetro areas or have high segregation or social vulnerability levels. Racial and ethnic segregation is particularly important in shaping the total care costs for counties with low poverty rates or social vulnerability level. Demographic composition, education, and social vulnerability are consistently important across different scenarios. Conclusions The findings highlight the differences in predictors for different types of CVD cost outcomes and the importance of social determinants. Interventions directed toward areas that have been economically and socially marginalized may aid in reducing the impact of CVDs.

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“…Modelling uses machine-learning methods, in which the machine learns from the data and uses it to forecast new data [ 1 , 2 ]. The most commonly predictive analytic model used is regression [ 3 – 6 ]. The proposed model for accurate prediction of future outputs has applications in banking, economics, e-commerce, sports, business, entertainment, etc.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning

Taloba

El-Aziz

Alshanbari

et al. 2022

Journal of Healthcare Engineering

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Medical costs are one of the most common recurring expenses in a person’s life. Based on different research studies, BMI, ageing, smoking, and other factors are all related to greater personal medical care costs. The estimates of the expenditures of health care related to obesity are needed to help create cost-effective obesity prevention strategies. Obesity prevention at a young age is a top concern in global health, clinical practice, and public health. To avoid these restrictions, genetic variants are employed as instrumental variables in this research. Using statistics from public huge datasets, the impact of body mass index (BMI) on overall healthcare expenses is predicted. A multiview learning architecture can be used to leverage BMI information in records, including diagnostic texts, diagnostic IDs, and patient traits. A hierarchy perception structure was suggested to choose significant words, health checks, and diagnoses for training phase informative data representations, because various words, diagnoses, and previous health care have varying significance for expense calculation. In this system model, linear regression analysis, naive Bayes classifier, and random forest algorithms were compared using a business analytic method that applied statistical and machine-learning approaches. According to the results of our forecasting method, linear regression has the maximum accuracy of 97.89 percent in forecasting overall healthcare costs. In terms of financial statistics, our methodology provides a predictive method.

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Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach

Cited by 14 publications

References 30 publications

Machine and cognitive intelligence for human health: systematic review

Machine and cognitive intelligence for human health: systematic review

Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning

Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning

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