Healthcare analytics (New York, N.Y.)最新文献_第7页

An efficient blood supply chain network model with multiple echelons for managing outdated products 一种高效的多梯队血液供应链网络模型，用于过期产品的管理

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2024-12-18 DOI: 10.1016/j.health.2024.100377

Agus Mansur , Ivan Darma Wangsa , Novrianty Rizky , Iwan Vanany

引用次数: 0

An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data 基于特征选择和基因表达数据的肝癌准确诊断的增强机器学习方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2024-12-12 DOI: 10.1016/j.health.2024.100373

Amena Mahmoud , Eiko Takaoka

{"title":"An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data","authors":"Amena Mahmoud , Eiko Takaoka","doi":"10.1016/j.health.2024.100373","DOIUrl":"10.1016/j.health.2024.100373","url":null,"abstract":"<div><div>Liver cancer is a significant global health concern, necessitating accurate and timely diagnosis for effective treatment. Machine learning approaches have emerged as promising tools for improving liver cancer classification using gene expression data in recent years. This study presents an advanced machine learning approach for liver cancer diagnosis using gene expression data, combining feature selection techniques with a stacking ensemble learning model. Our method addresses the challenges of high dimensionality and complex patterns in genomic data to improve diagnostic accuracy and interpretability. We employed a feature selection process to identify the most relevant gene expressions associated with liver cancer. This approach reduced the dimensionality of the data while preserving crucial biological information. The selected features were then used to train a stacking ensemble model, which combined multiple base learners, including Multi-Layer Perceptron (MLP), Random Forest (RF) model, K-nearest neighbor (KNN) model, and Support vector machine (SVM), with a meta-learner Extreme Gradient Boosting (Xgboost) model to make final predictions. The stacking ensemble achieved an accuracy of (97%), outperforming individual machine learning algorithms and traditional diagnostic methods. Furthermore, the model demonstrated high sensitivity (96.8%) and specificity (98.1%), crucial for early detection and minimizing false positives.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100373"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deterministic compartmental model for optimal control strategies of Giardiasis infection with saturating incidence and environmental dynamics 具有饱和发病率和环境动态的贾第虫病感染最优控制策略的确定性室室模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-01-07 DOI: 10.1016/j.health.2025.100383

Stephen Edward , Nyimvua Shaban

{"title":"Deterministic compartmental model for optimal control strategies of Giardiasis infection with saturating incidence and environmental dynamics","authors":"Stephen Edward , Nyimvua Shaban","doi":"10.1016/j.health.2025.100383","DOIUrl":"10.1016/j.health.2025.100383","url":null,"abstract":"<div><div>This study develops a deterministic compartmental model that tracks Giardiasis’s direct and indirect transmission dynamics. The study begins by constructing a model incorporating four constant controls: health education, screening, hospitalization, and sanitation. The analytical results of the model are investigated and presented. The positivity of the solutions and the existence of invariant regions were established. The model exhibits a unique disease-free equilibrium and multiple endemic equilibria. The effective reproduction number was derived using the Next-Generation Matrix (NGM) approach, and its implications for the stability of the equilibria were explored. Local stability of the disease-free equilibrium was confirmed using the Routh–Hurwitz criteria, while global stability results were also presented. Sensitivity analysis was conducted based on the effective reproduction number, identifying the most influential parameters. We introduce an optimal control problem to curb the spread of Giardiasis. We rigorously establish the existence of optimal control solutions and analytically characterize these solutions using Pontryagin’s Maximum Principle. We conduct numerical simulations to evaluate the effectiveness of various control strategies. The results are promising, showing that the simultaneous implementation of all four control measures, education, screening, treatment, and sanitation, can lead to a significant reduction in disease cases, thereby offering a reassuring solution to the spread of Giardiasis.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100383"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytical transmission model for evaluating pneumonia vaccination and control strategies 评估肺炎疫苗接种和控制策略的分析传播模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-04-27 DOI: 10.1016/j.health.2025.100394

Dipo Aldila , Abdullah Hasan Hassan , Mohamad Hifzhudin Noor Aziz , Putri Zahra Kamalia

{"title":"An analytical transmission model for evaluating pneumonia vaccination and control strategies","authors":"Dipo Aldila , Abdullah Hasan Hassan , Mohamad Hifzhudin Noor Aziz , Putri Zahra Kamalia","doi":"10.1016/j.health.2025.100394","DOIUrl":"10.1016/j.health.2025.100394","url":null,"abstract":"<div><div>Pneumonia is an infectious disease caused by various agents, such as viruses, bacteria, or fungi. This study proposes an analytical pneumonia model to assess the impact of vaccine interventions. The proposed mathematical model reveals that pneumonia will be eradicated from the population if the basic reproduction number is less than one. Furthermore, our bifurcation analysis indicates the absence of a backward bifurcation, meaning that the basic reproduction number is the sole threshold for determining the endemicity of a disease. In other words, pneumonia will be extinct if the basic reproduction number is less than one and will exist if it is larger than one. We estimate our model parameter values using incidence data from five districts in Jakarta, Indonesia. The dataset consists of weekly incidence data from 2023 until mid-2024. Our analysis shows North Jakarta has the highest case incidence per 100,000 individuals compared to the other districts. A global sensitivity analysis, using the partial rank correlation coefficient and Latin hypercube sampling, was conducted to identify the most impactful parameters on the basic reproduction number for each district in Jakarta. An optimal control problem was formulated to determine the most effective strategies for controlling pneumonia in the field. We found that adult vaccination has a greater impact on reducing the spread of pneumonia than a newborn vaccination strategy. However, combining both newborn and adult vaccinations is essential to ensure long-lasting immunity in children.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100394"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879067","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 hybrid deep learning framework for early detection of Mpox using image data 基于图像数据的Mpox早期检测混合深度学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-05-14 DOI: 10.1016/j.health.2025.100396

Sajal Chakroborty

引用次数: 0

A machine learning and neural network approach for classifying multidrug-resistant bacterial infections 多药耐药细菌感染分类的机器学习和神经网络方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-02-22 DOI: 10.1016/j.health.2025.100388

Preeda Mengsiri , Ratchadaporn Ungcharoen , Sethavidh Gertphol

{"title":"A machine learning and neural network approach for classifying multidrug-resistant bacterial infections","authors":"Preeda Mengsiri , Ratchadaporn Ungcharoen , Sethavidh Gertphol","doi":"10.1016/j.health.2025.100388","DOIUrl":"10.1016/j.health.2025.100388","url":null,"abstract":"<div><div>Antimicrobial resistance (AMR) represents a major public health challenge, significantly complicating infection prevention and treatment. This study employs machine learning and neural network techniques to classify multidrug-resistant Gram-negative bacterial (MDR-GNB) infections using electronic health records from 624 patients at Thatphanom Crown Prince Hospital in Thailand. We compared several algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Light Gradient Boosting Machine (LightGBM), with the MLP model exhibiting the highest accuracy and specificity. Performance was further enhanced by integrating feature selection methods such as Sequential Forward Selection (SFS), Recursive Feature Elimination with Cross-Validation (RFE-CV), and SelectKBest with data augmentation techniques, including ADASYN and SMOTE variants. Utilizing SHapley Additive exPlanations (SHAP) provided valuable insights into the most influential predictors for MDR-GNB. Notably, the MLP model achieved an AUC of 0.70, surpassing prior studies and highlighting its potential to advance clinical decision-making in managing MDR-GNB infections.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100388"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A predictive healthcare model using machine learning and psychological factors for medication adherence 使用机器学习和药物依从性心理因素的预测性医疗保健模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-05-03 DOI: 10.1016/j.health.2025.100397

Junwu Dong , Minyi Chu , Yirou Xu

{"title":"A predictive healthcare model using machine learning and psychological factors for medication adherence","authors":"Junwu Dong , Minyi Chu , Yirou Xu","doi":"10.1016/j.health.2025.100397","DOIUrl":"10.1016/j.health.2025.100397","url":null,"abstract":"<div><div>Ensuring effective medication adherence is vital for managing chronic diseases, yet global patient adherence remains suboptimal. This study aims to develop a predictive model for medication adherence behaviour (MAB) employing machine learning techniques, addressing the limitations of traditional correlation-based approaches. Based on the Meta-Theoretic Model of Motivation and Personality (3M Model), data from 428 chronic disease patients, included dark triad traits (narcissism, Machiavellianism, psychopathy), general self-efficacy, doctor-patient trust, and demographic variables. Five machine learning algorithms – multiple logistic regression, decision tree, adaptive boosting, random forest and support vector machine (SVM) – were utilized to identify MAB levels and assess feature importance. Among these, the random forest model achieved the highest performance, with an accuracy of 0.637, recall of 0.538, precision of 0.556, and an F1 score of 0.544. Feature ranking revealed that narcissism, Machiavellianism, doctor-patient trust, psychopathy, and general self-efficacy were the most influential predictors. These findings demonstrate that integrating psychological and demographic factors into machine learning models can enhance the prediction of medication adherence. This study presents a novel interdisciplinary framework that integrates behavioural health analytics and data science to inform clinical decision-making. It provides valuable insights into the severity and temporal progression of medication adherence behaviours, offering clinicians a practical reference for developing more effective intervention strategies.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100397"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922291","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 attention-based loss function and synthetic minority oversampling technique for alleviating class imbalance in predicting diabetes 基于注意力的损失函数和合成少数派过采样技术在糖尿病预测中的应用

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-05-29 DOI: 10.1016/j.health.2025.100399

Santanu Roy , Reshma Rachel Cherish , Gifty Roy

{"title":"An attention-based loss function and synthetic minority oversampling technique for alleviating class imbalance in predicting diabetes","authors":"Santanu Roy , Reshma Rachel Cherish , Gifty Roy","doi":"10.1016/j.health.2025.100399","DOIUrl":"10.1016/j.health.2025.100399","url":null,"abstract":"<div><div>Diabetes is a chronic disease due to higher blood sugar (or Glucose) levels in the blood. This study proposes a novel attention-based loss function and a lightweight artificial neural network (ANN) called Diabetic Lite (DB-Lite) for diabetes prediction in the Pima Indian Diabetes Dataset (PIDD). We show that the Pima dataset has many challenges. It is a small and imbalanced dataset; moreover, many features are non-linearly correlated in this dataset. The novelties of this research work are as follows: (i) A novel loss function of attention-based binary cross entropy (ABCE) is proposed for the first time to alleviate the statistical imbalance present within the Pima dataset. This ABCE loss function is incorporated in the DB-Lite model, which is trained from scratch. (ii) A Swish activation function is deployed in the hidden layer of DB-Lite instead of Rectified Linear Unit (ReLU) to deal with the non-linear dependency of features with the final outcome. (iii) The synthetic minority oversampling technique (SMOTE) is used as a pre-processing technique to mitigate the class imbalance problem from the Pima dataset. (iv) An adaptive learning rate is utilized while training the model to speed up the convergence of the DB-Lite model. Our final proposed framework has achieved 99.7% accuracy, 99.4% precision, 99.8% recall, and 99.6% F1 score in testing, which is the best result on this Pima dataset. The Welch t-testing (as a statistical hypothesis testing) and 10-fold cross-validation are utilized to prove the validity of the proposed loss function.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100399"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185724","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 large-scale risk assessment and classification model for pneumococcus using Finnish national health data 基于芬兰国家卫生数据的肺炎球菌大规模风险评估和分类模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-01-03 DOI: 10.1016/j.health.2025.100382

Viljami Männikkö , Juha Turunen , Heidi Åhman , Esa Harju

{"title":"A large-scale risk assessment and classification model for pneumococcus using Finnish national health data","authors":"Viljami Männikkö , Juha Turunen , Heidi Åhman , Esa Harju","doi":"10.1016/j.health.2025.100382","DOIUrl":"10.1016/j.health.2025.100382","url":null,"abstract":"<div><div><em>Streptococcus pneumoniae</em>, or pneumococcus, poses a significant health risk, particularly to infants, the elderly, and individuals with underlying medical conditions. In Finland, pneumococcal vaccination is part of the national immunization program, with vaccination provided to young children and only selected at-risk adult populations included. This study aims to leverage the Finnish national electronic health record system, Kanta, to analyze treatment histories and identify individuals at increased risk for disease to improve vaccination strategies. Kanta provides a comprehensive, nationwide database of patient treatment histories, which can be utilized to track individual risk factors and disease episodes. We analyzed health data from 96,200 Finnish residents with risk factors for pneumococcal disease following guidelines from the Finnish Institute for Health and Welfare and the World Health Organization. We prioritize vaccination for those at the greatest risk by categorizing individuals based on their identified risk factors. This study demonstrates the potential for using national health record data to conduct large-scale risk analyses, allowing for more targeted and efficient vaccination strategies. The novelty of our approach lies in the automatic identification of high-risk individuals, which can inform public health initiatives and enhance the monitoring of pneumococcal disease risk at a population level.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100382"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rate 基于logistic招募率的COVID-19传播最优控制模型及敏感性分析

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-01-08 DOI: 10.1016/j.health.2024.100375

Jonner Nainggolan , Moch. Fandi Ansori , Hengki Tasman

{"title":"An optimal control model with sensitivity analysis for COVID-19 transmission using logistic recruitment rate","authors":"Jonner Nainggolan , Moch. Fandi Ansori , Hengki Tasman","doi":"10.1016/j.health.2024.100375","DOIUrl":"10.1016/j.health.2024.100375","url":null,"abstract":"<div><div>This study proposes an optimal control model for COVID-19 spread, incorporating a logistic recruitment rate. The observations show the disease-free equilibrium exists when the population-existing threshold exceeds 1. The stability of equilibrium is determined by the basic reproduction number <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>. This implies that equilibrium is stable when <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> is less than or equal to 1, but it is unstable when the value is greater than 1. Furthermore, an endemic equilibrium and stability is recorded when <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> exceeds 1. To identify influential factors in COVID-19 spread, sensitivity index and sensitivity analyses of <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> are conducted. The model perfectly integrates both prevention and therapy controls. As a result, numerical simulations show that the prevention control is more effective than the treatment control in reducing COVID-19 spread. Moreover, the simultaneous implementation of prevention and treatment controls outperforms individual control methods in mitigating COVID-19 spread. Finally, sensitivity analysis conducted with constant controls shows the contributions of the controls to disease dynamics.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100375"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0