Healthcare analytics (New York, N.Y.)最新文献_第3页

An artificial intelligence-based approach for human parasite egg segmentation and classification 基于人工智能的人类寄生虫卵分割与分类方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-11-10 DOI: 10.1016/j.health.2025.100432

Sohag Kumar Mondal , Monira Islam , Md. Salah Uddin Yusuf

{"title":"An artificial intelligence-based approach for human parasite egg segmentation and classification","authors":"Sohag Kumar Mondal , Monira Islam , Md. Salah Uddin Yusuf","doi":"10.1016/j.health.2025.100432","DOIUrl":"10.1016/j.health.2025.100432","url":null,"abstract":"<div><div>Intestinal parasitic infections are a significant global health issue, recognized by the World Health Organization (WHO) as a major cause of disease. Current diagnostic methods rely on labor-intensive manual examination of fecal samples under a microscope. This study aims to overcome these challenges by leveraging artificial intelligence (AI) to automate the identification of parasitic eggs in laboratory settings. To enhance image clarity and remove noise from microscopic fecal images, we employed the Block-Matching and 3D Filtering (BM3D) technique, which effectively addresses Gaussian, Salt and Pepper, Speckle, and Fog Noise. Contrast enhancement between subjects and the background was achieved using Contrast-Limited Adaptive Histogram Equalization (CLAHE). A U-Net model was utilized for image segmentation, followed by a watershed algorithm to extract Regions of Interest (ROI) from the segmented images. Finally, a Convolutional Neural Network (CNN) was developed for classification through automatic feature learning in the spatial domain. The U-Net model, optimized using the Adam optimizer, demonstrated excellent performance with 96.47% accuracy, 97.85% precision, and 98.05% sensitivity at the pixel level. At the object level, it achieved 96% Intersection over Union (IoU) and a 94% Dice Coefficient. The CNN classifier achieved 97.38% accuracy, with macro average F1 scores of 97.67%. This study presents an innovative AI-based approach for diagnosing intestinal parasitic infections. By integrating advanced image filtering, segmentation, and classification techniques, the proposed method shows promise in improving diagnostic efficiency and reducing reliance on manual processes.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100432"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A constrained optimization approach for ultrasound shear wave speed estimation with time-lateral plane cleaning in medical imaging 医学成像中带时间横向平面清洗的超声剪切波速估计约束优化方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-09-27 DOI: 10.1016/j.health.2025.100423

MD Jahin Alam, Md. Kamrul Hasan

{"title":"A constrained optimization approach for ultrasound shear wave speed estimation with time-lateral plane cleaning in medical imaging","authors":"MD Jahin Alam, Md. Kamrul Hasan","doi":"10.1016/j.health.2025.100423","DOIUrl":"10.1016/j.health.2025.100423","url":null,"abstract":"<div><div>Ultrasound shear wave elastography (SWE) is a noninvasive tissue stiffness measurement technique for medical diagnosis. In SWE, an acoustic radiation force creates shear waves (SW) throughout a medium where the shear wave speed (SWS) is related to the medium stiffness. Traditional SWS estimation techniques are not noise-resilient in handling jitter and reflection artifacts. This paper proposes new techniques to estimate SWS in both time and frequency domains. These new methods utilize loss functions which are: (1) optimized by lateral signal shift between known locations, and (2) constrained by neighborhood displacement group shift determined from the time-lateral plane-denoised SW propagation. The proposed constrained optimization is formed by coupling neighboring particles’ losses with a Gaussian kernel, giving an optimum arrival time for the center particle to enforce local stiffness homogeneity and enable noise resilience. The explicit denoising scheme involves isolating SW profiles from time-lateral planes, creating parameterized masks. Additionally, lateral interpolation is performed to enhance reconstruction resolution and thereby improve the reliability of optimization. The proposed scheme is evaluated on a simulation (US-SWS-Digital-Phantoms) and three experimental phantom datasets: (i) Mayo Clinic CIRS049 model, (ii) RSNA-QIBA-US-SWS, (iii) Private data. The constrained optimization performance is compared with three time-of-flight (ToF) and two frequency-domain methods. The evaluations produced visually and quantitatively superior and noise-robust reconstructions compared to classical methods. Due to the quality and minimal error of SWS map formation, the proposed technique can find its application in tissue health inspection and cancer diagnosis.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100423"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms 一种可解释的分析方法来检测心脏病发作使用生物标志物和自然启发算法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-07-11 DOI: 10.1016/j.health.2025.100407

Maithri Bairy , Krishnaraj Chadaga , Niranjana Sampathila , R. Vijaya Arjunan , G. Muralidhar Bairy

{"title":"An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms","authors":"Maithri Bairy , Krishnaraj Chadaga , Niranjana Sampathila , R. Vijaya Arjunan , G. Muralidhar Bairy","doi":"10.1016/j.health.2025.100407","DOIUrl":"10.1016/j.health.2025.100407","url":null,"abstract":"<div><div>Heart attacks are among the leading causes of death globally, and the earliest possible identification of at-risk patients is critical to lowering deaths. Advanced machine learning and deep learning algorithms have been effectively used to predict the presence of heart attack based on clinical and laboratory markers. This study used five explainable artificial intelligence techniques (XAI) to ensure that predictions made by the model are understandable and interpretable to facilitate clinical decisions. Fourteen nature-inspired feature selection algorithms were applied to identify the most informative markers while optimizing the predictive models for greater accuracy and reliability. Mutual information achieved a maximum testing accuracy of 90 % and highest precision of 94 %. The Whale Optimization Algorithm, Jaya Algorithm, Grey Wolf Optimizer and Sine Cosine Algorithm were the next best performing algorithms. The XAI results showed that the most important markers were ST slope, Oldpeak, exercise-induced angina, chest pain type, and fasting blood sugar. These models can be implemented in healthcare institutions to predict heart attack risks early, allowing timely interventions to reduce the likelihood of severe cardiovascular diseases. By supporting healthcare professionals with computer-aided diagnostic tools, these systems can enhance patient-specific decision-making while alleviating strain on healthcare resources.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100407"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A bio-inspired approach to feature optimization for ischemic heart disease detection 缺血性心脏病检测特征优化的生物启发方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-10-30 DOI: 10.1016/j.health.2025.100427

D. Cenitta , N. Arul , T. Praveen Pai , R. Vijaya Arjunan , Tanuja Shailesh

{"title":"A bio-inspired approach to feature optimization for ischemic heart disease detection","authors":"D. Cenitta , N. Arul , T. Praveen Pai , R. Vijaya Arjunan , Tanuja Shailesh","doi":"10.1016/j.health.2025.100427","DOIUrl":"10.1016/j.health.2025.100427","url":null,"abstract":"<div><div>Ischemic Heart Disease (IHD) stands as one of the primary contributors to worldwide deaths, therefore requiring precise and efficient predictive models. Standard machine learning techniques encounter hurdles, including excessive feature dimensions and unbalanced data distribution together with inappropriate feature group choice that negatively affect model effectiveness. The research introduces an optimized feature selection method by employing an Improved Squirrel Search Algorithm (ISSA) to raise the predictive capacity for IHD classification. The ISSA implements adaptive search features to automatically optimize feature selection, through which it maintains important attributes while eliminating redundant information. The selected features are evaluated using a Random Forest classifier, known for its robustness and interpretability in medical prediction tasks. Experimental results on the University of California Irvine (UCI) Heart Disease dataset show that the Improved Squirrel Search Algorithm–Random Forest (ISSA-RF) model achieves a classification accuracy of 98.12 %, outperforming existing feature selection techniques while reducing computational overhead. Bio-inspired optimization proves effective in medical diagnostics through recent research findings that lead to more efficient predictive healthcare models with interpretable properties.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100427"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytical framework for enhancing brain signal classification through hybrid filtering and dimensionality reduction 一种通过混合滤波和降维增强脑信号分类的分析框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-11-09 DOI: 10.1016/j.health.2025.100435

Rajani Rai B , Karunakara Rai B , Mamatha A S , Nikshitha

{"title":"An analytical framework for enhancing brain signal classification through hybrid filtering and dimensionality reduction","authors":"Rajani Rai B , Karunakara Rai B , Mamatha A S , Nikshitha","doi":"10.1016/j.health.2025.100435","DOIUrl":"10.1016/j.health.2025.100435","url":null,"abstract":"<div><div>Accurate classification of focal and non-focal epilepsy is a critical healthcare analytics challenge that requires robust data preprocessing and feature optimization. This work develops an integrated analytics framework that combines hybrid filtering with hybrid dimensionality reduction to improve both signal quality and predictive performance. A multi-criteria ranking strategy based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed, incorporating conventional signal measures alongside distance and divergence metrics to identify optimal preprocessing pipelines. Statistical validation is performed using the Friedman test with Nemenyi post-hoc analysis to establish the significance of competing filter–dimensionality reduction combinations. The validated framework is benchmarked across conventional, hybrid, and deep learning classifiers, with the most effective configuration—Butterworth.</div><div>Wavelet Packet Decomposition (BW + WPD) filtering followed by Principal Component Analysis–Linear Discriminant Analysis (PCA + LDA)—achieving 95.63% accuracy using an Adaboost classifier on the Bern–Barcelona dataset. Evaluation on the independent Bonn dataset confirms robustness and cross-subject generalizability. These findings demonstrate the value of a multi-metric, statistically validated analytics strategy for reliable epilepsy detection, with potential applicability to broader healthcare signal classification tasks.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100435"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative analysis of generalized additive models for obesity risk prediction 肥胖风险预测的广义加性模型的比较分析

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-08-27 DOI: 10.1016/j.health.2025.100410

Olushina Olawale Awe , Olawale Abiodun Olaniyan , Ayorinde Emmanuel Olatunde , Ronel SewPaul , Natisha Dukhi

{"title":"A comparative analysis of generalized additive models for obesity risk prediction","authors":"Olushina Olawale Awe , Olawale Abiodun Olaniyan , Ayorinde Emmanuel Olatunde , Ronel SewPaul , Natisha Dukhi","doi":"10.1016/j.health.2025.100410","DOIUrl":"10.1016/j.health.2025.100410","url":null,"abstract":"<div><div>Obesity is a growing global health crisis, and traditional regression models often fail to capture the complex relationships between risk factors, limiting predictive accuracy and hindering effective public health interventions. Conventional methods overlook non-linear associations and interaction effects across demographic, socioeconomic, and behavioral predictors, which are particularly important in diverse populations with varying obesity determinants. To address these limitations, we applied Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to analyze obesity predictors in a nationally representative adolescent sample (N <span><math><mo>=</mo></math></span> 671). Our framework included comprehensive variable selection across demographic, socioeconomic, behavioral, and clinical domains, comparison with three alternative regression models, and validation using the Generalized Akaike Information Criterion (GAIC). The binomial stepwise GAMLSS model demonstrated superior performance (GAIC <span><math><mo>=</mo></math></span> 624.98). Key findings included strong geographic variation, significant gender disparity, a socioeconomic gradient, and important behavioral predictors such as weight gain attempts. The GAMLSS framework improves obesity risk prediction by modeling complex relationships often missed by traditional methods, offering targeted intervention strategies based on geographic, gender, and socioeconomic factors, and challenging assumptions about dietary influences.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100410"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytical review of biosensor-based chronic pain quantification in healthcare 医疗保健中基于生物传感器的慢性疼痛量化分析综述

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-09-15 DOI: 10.1016/j.health.2025.100419

Aarthi Kannan , Daniel West , Dinesh Kumbhare , Wei-Ting Ting , Md. Younus Ali , Hameem I. Kawsar , Gurmit Singh , Harsha Shanthanna , Eleni Hapidou , Matiar M.R. Howlader

{"title":"An analytical review of biosensor-based chronic pain quantification in healthcare","authors":"Aarthi Kannan , Daniel West , Dinesh Kumbhare , Wei-Ting Ting , Md. Younus Ali , Hameem I. Kawsar , Gurmit Singh , Harsha Shanthanna , Eleni Hapidou , Matiar M.R. Howlader","doi":"10.1016/j.health.2025.100419","DOIUrl":"10.1016/j.health.2025.100419","url":null,"abstract":"<div><div>Current clinical methods for chronic pain assessment lack objective, quantitative measures, creating a critical gap in diagnostic accuracy. This review investigates the relationship between chronic pain and key biomarkers detectable in body fluids, such as glutamate, interleukin-6, nitric oxide, and quinolinic acid. We first discuss the biological mechanisms underlying chronic pain and evaluate the relevance of these biomarkers. The review then focuses on recent advancements in non-enzymatic electrochemical biosensors used to monitor these biomarkers. For each sensor, we summarize performance metrics including sensitivity, detection limits, and linear range, while highlighting the analytical methodologies used to establish correlations between biomarker levels and pain intensity. Our findings demonstrate that quantitative analysis of biomarker fluctuations can enhance chronic pain monitoring. The integration of sensor-based biomarker analytics with clinical workflows may offer a path toward personalized treatment plans and improved decision-making in healthcare supply chains. This review emphasizes the need for continued development of high-precision biosensors as analytical tools for translating physiological signals into clinically actionable pain metrics.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100419"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytics-driven review of U-Net for medical image segmentation U-Net用于医学图像分割的分析驱动综述

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-09-20 DOI: 10.1016/j.health.2025.100416

Fnu Neha , Deepshikha Bhati , Deepak Kumar Shukla , Sonavi Makarand Dalvi , Nikolaos Mantzou , Safa Shubbar

{"title":"An analytics-driven review of U-Net for medical image segmentation","authors":"Fnu Neha , Deepshikha Bhati , Deepak Kumar Shukla , Sonavi Makarand Dalvi , Nikolaos Mantzou , Safa Shubbar","doi":"10.1016/j.health.2025.100416","DOIUrl":"10.1016/j.health.2025.100416","url":null,"abstract":"<div><div>Medical imaging (MI) plays a vital role in healthcare by providing detailed insights into anatomical structures and pathological conditions, supporting accurate diagnosis and treatment planning. Noninvasive modalities, such as X-ray, magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US), produce high-resolution images of internal organs and tissues. The effective interpretation of these images relies on the precise segmentation of the regions of interest (ROI), including organs and lesions. Traditional methods based on manual feature extraction are time-consuming, inconsistent, and not scalable. This review explores recent advances in artificial intelligence (AI)-driven segmentation, focusing on Convolutional Neural Network (CNN) architectures, particularly the U-Net family and its variants—U-Net++, and U-Net 3+. These models enable automated, pixel-wise classification across modalities and have improved segmentation accuracy and efficiency. The review outlines the evolution of U-Net architectures, their clinical integration, and offers a modality-wise comparison. It also addresses challenges such as data heterogeneity, limited generalizability, and model interpretability, proposing solutions including attention mechanisms and Transformer-based designs. Emphasizing clinical applicability, this work bridges the gap between algorithmic development and real-world implementation.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100416"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A data-driven multicriteria decision model for healthcare workforce retention strategies 医疗保健人力保留策略的数据驱动多标准决策模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-06-13 DOI: 10.1016/j.health.2025.100403

Debora Di Caprio , Sofia Sironi , Fan-Yun Lan , Ramin Rostamkhani

{"title":"A data-driven multicriteria decision model for healthcare workforce retention strategies","authors":"Debora Di Caprio , Sofia Sironi , Fan-Yun Lan , Ramin Rostamkhani","doi":"10.1016/j.health.2025.100403","DOIUrl":"10.1016/j.health.2025.100403","url":null,"abstract":"<div><div>The retention of nurses and physicians in Hospitals is a global problem affecting the healthcare system worldwide. This study focuses on the healthcare workforce retention problem considering the current situation in Taiwan. Healthcare staff in Taiwan are undergoing a critical phase, with an increasing number of experienced workers leaving their job to go to work for private organizations or as freelancers. We develop a data-driven four-phase methodology based on the design of a satisfaction index that allows to rank different groups of employees against a given set of criteria. First, criteria are identified and clustered to describe different job dimensions (phase 1). Hence, subjective evaluations of the criteria are collected from healthcare workers while experts provide pairwise comparisons among them (phase 2). An adjusted analytic hierarchy process (AHP) is used to weight the job dimensions and the criteria within each job dimension (phase 3). Finally, the satisfaction index is formalized and computed for different groups of employees (phase 4). The methodology has been implemented with data collected from healthcare workers employed in three healthcare institutions in Northern Taiwan. The proposed index represents a novel decision support tool for managers and policy makers in designing intervention strategies able to address different needs of different groups of employees. Besides, it allows for innovative applications to quality management (QM) by extending the standard QM approach to hospitals and healthcare centers far beyond the common focus on patients' satisfaction. Finally, the mathematical formulation of the index is very flexible and allows for applications to any employment sector through a variety of analyses based on different categorizations of the workers.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100403"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytics framework for healthcare expenditure forecasting with machine learning 基于机器学习的医疗保健支出预测分析框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-10-28 DOI: 10.1016/j.health.2025.100428

John Wang , Shubin Xu , Yawei Wang , Houda EL Bouhissi

{"title":"An analytics framework for healthcare expenditure forecasting with machine learning","authors":"John Wang , Shubin Xu , Yawei Wang , Houda EL Bouhissi","doi":"10.1016/j.health.2025.100428","DOIUrl":"10.1016/j.health.2025.100428","url":null,"abstract":"<div><div>The United States healthcare system relies heavily on Medicaid, which serves nearly 80 million people and accounts for a substantial share of both state and federal budgets. This study employs a range of forecasting methods, including ARIMA, Holt's linear trend, polynomial regressions (degree 2 and 4), Prophet, and piecewise linear regression, as well as machine learning models such as random forest, gradient boosting, and support vector regression, to analyze the growth of Medicaid expenditures. Using data from 1966 to 2024, the analysis identifies historical patterns and evaluates model performance with Root Mean Squared Error (RMSE) and related metrics to project costs through 2035. The results show that the autoregressive model with integrated moving average and Prophet generate the most accurate baseline forecasts, suggesting that Medicaid expenditures are likely to exceed one trillion dollars within the next 15 years. Although the machine learning models produced somewhat lower estimates, they revealed complex relationships between policy variables and expenditure behavior, making them useful for building alternative forecasting scenarios. The discussion emphasizes the policy relevance of these findings, particularly in relation to budget sustainability and healthcare equity, and highlights the importance of employing multiple forecasting approaches. Overall, the study demonstrates the value of decision analytics in healthcare forecasting by highlighting the need for accurate predictions, flexible models, and interpretable outcomes. It provides evidence-based tools to anticipate Medicaid's financial challenges and support the development of sustainable healthcare strategies for the years ahead.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100428"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0