Healthcare analytics (New York, N.Y.)最新文献

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 : 2025-06-13 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":"2025-06-13","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 analytical approach to modeling conjunctival viral disease using fuzzy logic and time-delay dynamics 基于模糊逻辑和时滞动力学的结膜病毒病建模分析方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-06 DOI: 10.1016/j.health.2025.100404

Muhammad Tashfeen , Hothefa Shaker Jassim , Fazal Dayan , Muhammad Azizur Rehman , Alwahab Dhulfiqar Zoltán , Husam A. Neamah

{"title":"An analytical approach to modeling conjunctival viral disease using fuzzy logic and time-delay dynamics","authors":"Muhammad Tashfeen , Hothefa Shaker Jassim , Fazal Dayan , Muhammad Azizur Rehman , Alwahab Dhulfiqar Zoltán , Husam A. Neamah","doi":"10.1016/j.health.2025.100404","DOIUrl":"10.1016/j.health.2025.100404","url":null,"abstract":"<div><div>Conjunctivitis, commonly known as pink eye, is the inflammation of the conjunctiva, often accompanied by redness, itchiness, and the discharge of thick white or greyish pus. Highly contagious in settings involving close contact, it poses significant public health and economic concerns. This study proposes a fuzzy mathematical modeling framework to investigate Conjunctival Viral Disease (CVD) transmission dynamics, with particular attention to the roles of asymptomatic carriers and environmental influences. Unlike conventional models that rely solely on deterministic parameters, the incorporation of fuzzy theory allows for representing uncertainties and variabilities inherent in real-world disease transmission. The model further incorporates time-delay terms to account for incubation periods and other latent effects, enhancing the accuracy of system dynamics. This fuzzy framework performs key analyses, including identifying equilibrium points, computation of the basic reproduction number, sensitivity analysis, and assessment of local and global stability. Numerical solutions are obtained using the Forward Euler and Nonstandard Finite Difference (NSFD) methods. The NSFD scheme is rigorously examined for convergence, non-negativity, boundedness, and consistency properties. Simulation results confirm that the NSFD approach maintains the qualitative features of the model even under larger time steps. Overall, the study underscores the importance of integrating fuzzy logic and time delays in epidemic modeling and presents a robust methodological approach for understanding and managing the spread of infectious diseases in uncertain and dynamic environments.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100404"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298568","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 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

An analytical approach to assessing the spatial equity and allocation of healthcare resources in Shanghai 上海市卫生资源空间公平与配置分析方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 DOI: 10.1016/j.health.2025.100400

Hong-Yan Li , Jing Guo, Chuang-Hao Yang

{"title":"An analytical approach to assessing the spatial equity and allocation of healthcare resources in Shanghai","authors":"Hong-Yan Li , Jing Guo, Chuang-Hao Yang","doi":"10.1016/j.health.2025.100400","DOIUrl":"10.1016/j.health.2025.100400","url":null,"abstract":"<div><div>The rational allocation of healthcare resources is vital for establishing a healthcare system that aligns with the levels of economic and social development. As a comprehensive discipline integrating geography, cartography, remote sensing, and computer science, Geographic Information System (GIS) can visualize and analyze spatial information through mapping. By utilizing GIS's statistical analysis and data visualization functions, this study provides a more efficient and intuitive analysis of Shanghai's spatial healthcare resource allocation and a more comprehensive assessment of its current allocation status. To examine the spatial correlation and spatial proximity, we apply the Global Moran Index (Moran's I), the Local Indicators of Spatial Association (LISA) test, and Hot Spot Analysis (Getis-Ord Gi∗) for assessment. Furthermore, by utilizing the Lorenz curve and Gini coefficient, this study provides a new perspective by expanding the measurement dimensions for assessing healthcare resource allocation in Shanghai. The results show that: From the global spatial correlation perspective, the allocation of healthcare resources in Shanghai exhibits spatial clustering. From the local spatial correlation perspective, healthcare resources in Shanghai show significant regional disparities, with resources concentrated in central urban areas. And from a multidimensional perspective, the equity of allocation of healthcare resources in Shanghai in 2022 was higher when measured by population (0.298 ± 0.063) and economy (0.292 ± 0.027) than by geographic area (0.612 ± 0.100) and green spaces (0.590 ± 0.110) of the Gini coefficient. These findings offer valuable insights for promoting the structural optimization and spatial distribution of healthcare resources in Shanghai.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100400"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185725","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 equity-based spatial analytics framework for evaluating pharmacy accessibility using geographical information systems 利用地理信息系统评价药房可及性的基于公平的空间分析框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-05-21 DOI: 10.1016/j.health.2025.100401

Sara Al-Naabi , Noura Al Nasiri , Talal Al-Awadhi , Meshal Abdullah , Ammar Abulibdeh

{"title":"An equity-based spatial analytics framework for evaluating pharmacy accessibility using geographical information systems","authors":"Sara Al-Naabi , Noura Al Nasiri , Talal Al-Awadhi , Meshal Abdullah , Ammar Abulibdeh","doi":"10.1016/j.health.2025.100401","DOIUrl":"10.1016/j.health.2025.100401","url":null,"abstract":"<div><div>Healthcare services have a significant impact on socioeconomic and health development globally. In Oman, rapid development since the 1970s has led to a focus on the equitable distribution of public services. This research aims to evaluate the spatial accessibility and distribution of pharmacies in Muscat Governorate, Oman, using Geographical Information Systems (GIS) and spatial analysis techniques. The primary objective is to measure the equity in the spatial distribution of pharmacies within Muscat Governorate. The study utilizes spatial datasets, including administrative areas, pharmacy locations, settlement locations, transportation networks, and non-spatial datasets such as demographic data. The methodology involves spatial distribution analysis using Average Nearest Neighbor (ANN), Moran's I for spatial autocorrelation, Kernel Density Analysis (KDA), Thiessen polygons for catchment areas, and Network analysis for determining service areas and accessibility by walking and driving distances. Findings indicate a clustered distribution of pharmacies, with higher concentrations in densely populated northern Wilayats like Muttrah, AS Seeb, and Bawshar. Muttrah exhibits the highest accessibility, with 99 % coverage within a 2.5 km radius, whereas Muscat Wilaya lacks pharmacy services entirely. These findings highlight significant disparities in the spatial distribution of pharmacies, underscoring the need for policy interventions to ensure equitable access. Policymakers should consider geographic and demographic factors in health service planning to ensure fair distribution and accessibility across the governorate. Implementing these recommendations can help improve healthcare access and equity in Muscat, contributing to overall social and health development.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100401"},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147169","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 Deep Learning Framework for Chronic Kidney Disease stage classification 慢性肾脏疾病分期分类的深度学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-05-20 DOI: 10.1016/j.health.2025.100398

Gayathri Hegde M , P Deepa Shenoy , Venugopal KR , Arvind Canchi

{"title":"A Deep Learning Framework for Chronic Kidney Disease stage classification","authors":"Gayathri Hegde M , P Deepa Shenoy , Venugopal KR , Arvind Canchi","doi":"10.1016/j.health.2025.100398","DOIUrl":"10.1016/j.health.2025.100398","url":null,"abstract":"<div><div>Chronic Kidney Disease (CKD) has become more prevalent, leading to a gradual decline in kidney function and, ultimately, in renal failure. Timely detection of the CKD stage is essential for enhancing healthcare services and decreasing morbidity and mortality. Hence, this study proposes a Metaheuristic-Hybrid Metaheuritstic eXplainable Artificial Intelligence (MHMXAI) driven Feature Selection (FS) approach and Deep Learning (DL) models for CKD stage prediction. MHMXAI approach selects the features with the highest scores from the Metaheuristic algorithm-Eagle Search Strategy, Hybrid Metaheuristic algorithm-Great Salmon Run-Thermal Exchange Optimization and eXplainable AI (XAI) tools like Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) for their effectiveness. To evaluate the proposed method, eight DL models — Feedforward Neural Network, Recurrent Neural Network, Deep Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU) and Bidirectional GRU were trained on selected features using different FS methods, as well as complete dataset. The models were assessed using performance metrics such as accuracy, precision, recall, F1-Score, Loss, Validation Loss and computation time. The CNN model outperformed others, achieving an accuracy between 98%-99.5% for all FS methods. Statistical tests, including the Friedman and Nemenyi post-hoc test, identified the CNN model trained with MHMXAI-selected features as the most robust choice for CKD stage prediction. These findings demonstrate that the proposed MHMXAI method effectively integrates metaheuristic algorithms and XAI tools, improving CKD stage prediction accuracy and clinical interpretability.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100398"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115378","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 : 2025-05-14 DOI: 10.1016/j.health.2025.100396

Sajal Chakroborty

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 : 2025-05-03 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":"2025-05-03","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 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 : 2025-04-27 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":"2025-04-27","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

An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals 一个集成的机器学习和超参数优化框架，用于利用光容积脉搏波信号进行无创肌酐估计

Healthcare analytics (New York, N.Y.) Pub Date : 2025-04-22 DOI: 10.1016/j.health.2025.100395

Parama Sridevi, Zawad Arefin, Sheikh Iqbal Ahamed

{"title":"An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals","authors":"Parama Sridevi, Zawad Arefin, Sheikh Iqbal Ahamed","doi":"10.1016/j.health.2025.100395","DOIUrl":"10.1016/j.health.2025.100395","url":null,"abstract":"<div><div>Frequent measurement of creatinine levels is vital for patients with chronic kidney disease. Traditional creatinine level measurement requires invasive blood test which has several disadvantages like discomfort, anxiety, panic, pain, risk of infection, etc. To address the issue, we propose a noninvasive machine learning (ML) model-based method to estimate creatinine level using photoplethysmography (PPG) signal. We obtained the PPG signal and gold-standard serum creatinine level of 404 patients from the Medical News Mart for Concentrated Care III (MIMIC III) database. In data preprocessing, we analyzed the PPG signal following several steps and created PPG feature set. We used multiple feature engineering methods to identify the most important features. We integrated Optuna, a hyperparameter optimization framework, with every ML model to get the optimal hyperparameters. We developed five ML models and compared their performance both with and without the application of Optuna. We found that Optuna significantly improves every model's performance. With Optuna, extreme gradient boosting (XGBoost) performed best among all five models. This XGBoost model had an accuracy of 85.2 %, an average k-fold cross validation score (k = 10) of 0.70, and a “receiver operating characteristic area under the curve” (ROC-AUC) score of 0.80. With the high performance exhibited by our developed model, the study can play a crucial role in the field of noninvasive creatinine estimation and diagnosis of chronic kidney disease.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100395"},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887787","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