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

{"title":"An interpretable machine learning model for dengue detection with clinical hematological data","authors":"Izaz Ahmmed Tuhin ,&nbsp;A.K.M.Fazlul Kobir Siam ,&nbsp;Md Mahfuzur Rahman Shanto ,&nbsp;Md Rajib Mia ,&nbsp;Imran Mahmud ,&nbsp;Apurba Ghosh","doi":"10.1016/j.health.2025.100430","DOIUrl":"10.1016/j.health.2025.100430","url":null,"abstract":"<div><div>Dengue fever remains a major global health concern that demands rapid and accurate diagnosis to prevent severe complications and support timely patient care. Traditional approaches relying on environmental variables often lack patient-level precision, limiting their clinical applicability. This study focuses on hematological parameters as more reliable indicators for early dengue detection. A novel machine learning framework, DengueStackX-19, was developed using 1,523 clinically verified patient records from Jamalpur 250-Bedded General Hospital, Jamalpur, Bangladesh. The dataset underwent rigorous preprocessing, normalization, and imbalance handling using various resampling techniques. Comparative evaluation across five balancing methods demonstrated that DengueStackX-19 consistently achieved the highest accuracy and robustness, performing effectively both before and after outlier removal. The model achieved 93.65 % accuracy and 89.63 % F1 during 10-fold cross-validation under SMOTEENN, and further attained 96.38 % accuracy and 94.20 % F1 in dengue classification, demonstrating robust generalization and consistent high performance across evaluation phases. Sensitivity analysis further verified its stability under feature perturbations. To ensure interpretability, SHAP and LIME were applied to identify the hematological factors most influential to the model's predictions, and the resulting patterns aligned with established clinical understanding. The model was deployed as an accessible web-based diagnostic tool, allowing healthcare professionals to perform real-time dengue detection without specialized laboratory infrastructure. This study demonstrates that hematology-driven AI models can significantly enhance diagnostic accuracy, reduce decision-making time, and improve patient outcomes, particularly in resource-limited settings.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100430"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465963","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}

{"title":"A machine learning framework for identifying phenotypes in chronic kidney disease","authors":"Marzieh Amiri Shahbazi ,&nbsp;Mohammad Abdullah Al-Mamun ,&nbsp;Todd Brothers ,&nbsp;Imtiaz Ahmed","doi":"10.1016/j.health.2025.100425","DOIUrl":"10.1016/j.health.2025.100425","url":null,"abstract":"<div><div>Identifying meaningful patient phenotypes is a cornerstone of data-driven healthcare, enabling risk stratification, resource allocation, and the design of personalized care strategies. Achieving this requires robust analytical methods that can uncover hidden structure in high-dimensional clinical data while ensuring stability and interpretability of results. In this study, we present a machine learning framework for phenotypic clustering that combines partition-based (<span><math><mi>k</mi></math></span>-means) and probabilistic (latent class analysis, LCA) approaches. By comparing subgroup assignments across these complementary methods, the framework provides an internal validation of clustering assignments. Rather than relying on a single method, the framework validates subgroup assignments through cross-method agreement, strengthening confidence in the robustness of the identified phenotypes and their utility for decision support. We apply the proposed framework to patients with chronic kidney disease (CKD) stratified by prior history of acute kidney injury (AKI), illustrating its value in uncovering population-level heterogeneity. While the mechanisms linking AKI to CKD phenotypic patterns remain poorly understood historically, this study investigates CKD trajectories in patients with and without prior AKI and identifies key phenotypic patterns. The analysis revealed consistent phenotypic structures, with over 80% agreement between the two clustering approaches. Distinct phenotypic patterns emerged between the AKI and non-AKI cohorts, with cardiovascular conditions consistently dominating in both groups. These findings demonstrate how stratified clustering can uncover risk signatures that traditional CKD staging systems may overlook. By combining complementary clustering algorithms, the framework strengthens the analytic foundation of phenotyping studies. Moreover, it enables the design of phenotype specific care pathways such as cluster aware monitoring panels and tailored coordination strategies, thus underscoring the broader potential of data-driven analytics to advance personalized medicine and healthcare decision support.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100425"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361877","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}

{"title":"An investigation of treatment barriers for End-Stage Kidney Disease patients using advanced analytics","authors":"Olga Bountali ,&nbsp;Sila Cetinkaya ,&nbsp;Michael Hahsler ,&nbsp;Farnaz Nourbakhsh ,&nbsp;Zhenghang Xu ,&nbsp;Henry Quinones","doi":"10.1016/j.health.2025.100438","DOIUrl":"10.1016/j.health.2025.100438","url":null,"abstract":"<div><div>This study uses advanced analytics to investigate the treatment barriers faced by unfunded patients suffering from end-stage kidney disease at Parkland Hospital. Under the Emergency Medical Treatment and Labor Act (EMTALA) federal law, these patients can receive dialysis only under emergency conditions. This practice, commonly known as “emergent dialysis,” routes patients through the Emergency Room (ER) for a screening assessment to determine whether they will be accepted for treatment. Utilizing a data set from Parkland Hospital on patient ER visits seeking emergent dialysis, we leverage descriptive analytics and statistical methods to investigate (i) the impact of this accept/reject decision process on patient outcomes and (ii) the potential influence of operational, medical, and behavioral factors, such as the ER load, patient acuity level, and accept/reject patient history on it. Our research highlights an unanticipated burden caused by a subset of occasional dialysis patients with notably infrequent visits—the aspect that should not be overlooked. It also pinpoints discrepancies across patients, e.g., counterintuitively, patients accepted for treatment experienced shorter wait times before the decision was made than those rejected. More importantly, our work reveals that operational and behavioral factors influence the decision-making process substantially, much more than medical ones. The above findings underscore the critical role of analytics in our model. Our work further employs prescriptive analytics and simulation optimization approaches to provide recommendations on how policymakers can leverage the insights above to make more effective decisions that improve care delivery for this vulnerable population.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100438"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617926","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}

{"title":"A scalable methodology for optimizing hospital surgical schedules considering efficiency, flexibility, and improved patient outcomes","authors":"Jiaqi Suo ,&nbsp;Claudio Martani ,&nbsp;Timothy B. Lescun ,&nbsp;Cherri A. Krug","doi":"10.1016/j.health.2025.100413","DOIUrl":"10.1016/j.health.2025.100413","url":null,"abstract":"<div><div>Hospitals face challenges in efficiently adapting treatment delivery to growing and changing demands. The main challenge arises from accommodating diverse patients requiring specific surgical resources and attention. Traditional scheduling methods often fail to address the dynamic nature of these environments, which are characterized by numerous uncertainties and stakeholders’ complex and changing needs. This study presents a novel methodology designed to enhance hospital operational efficiency while considering the interests of all stakeholders, including hospital administrators, medical staff (doctors, nurses, technicians), and patients. This requires a nuanced approach to effectively handle unpredictable treatment demands, resource availability, and patient requirements. The methodology systematically progresses from defining constraints and resources to modeling uncertainties generating and evaluating optimal schedules through iterative processes. This study develops and applies a 12-step method to optimize the surgery scheduling for the farm animal section of the Purdue Veterinary Hospital over a defined period. The application shows the practical benefits of the proposed approach by modeling dynamic surgical demands and exploring various scheduling possibilities within resource constraints. The results reveal that the proposed method effectively accommodates increased operational demands while managing delays, accidents, and illness costs.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100413"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047823","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}

{"title":"A comprehensive diagnostic framework for hepatitis C using structured data and predictive analytics","authors":"Behnaz Motamedi,&nbsp;Balázs Villányi","doi":"10.1016/j.health.2025.100412","DOIUrl":"10.1016/j.health.2025.100412","url":null,"abstract":"<div><div>This study posits that a structured preprocessing and feature selection methodology might substantially improve the classification accuracy and generalizability of machine learning (ML) models in predicting stages of hepatitis C virus (HCV) using clinical and demographic data. The HCV is a chronic liver ailment characterized by many phases, necessitating precise and prompt categorization for optimal therapy. Although ML presents opportunities for stage prediction, issues such as class imbalance, missing data, and feature redundancy limit model efficacy and generalizability. To test this theory, we established an extensive four-phase preparation pipeline: Baseline imputes missing values using class-specific means; Refine mitigates outliers through class-specific medians and normalization; Balanced addresses class imbalance across five stages employing localized random affine shadow-sampling; and Augmented incorporates a clustering-based feature derived from an ensemble of K-means and Gaussian mixture models, combined with principal component analysis. The prediction model was developed by optimizing feature selection with the ReliefF approach and a random forest classifier employing random search. The resultant model exhibited outstanding performance, attaining an accuracy of 0.9983, precision of 0.9984, recall of 0.9983, F1-score of 0.9984, and Matthews correlation coefficient (MCC) of 0.9979 on the training set. It achieved an accuracy of 0.9977, precision of 0.9976, recall of 0.9981, F1-score of 0.9978, and MCC of 0.9973 on the independent test. The ensemble clustering component demonstrated reasonable validity, shown by an adjusted Rand index of 1.0, a moderate silhouette coefficient of 0.4702, and a Davies–Bouldin score of 1.1745, modestly outperforming individual clustering methods. The findings support the hypothesis and demonstrate that thorough preprocessing, stringent feature selection, and model optimization provide a highly accurate and generalizable tool for predicting HCV stages, hence improving clinical diagnosis and treatment strategies.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100412"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879452","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}

{"title":"An analytical study of worker well-being and COVID-19 impact using Bayesian panel modeling","authors":"Makoto Nakakita ,&nbsp;Tomoki Toyabe ,&nbsp;Naoki Kubota ,&nbsp;Wakuo Saito ,&nbsp;Teruo Nakatsuma","doi":"10.1016/j.health.2025.100434","DOIUrl":"10.1016/j.health.2025.100434","url":null,"abstract":"<div><div>This study investigates how the determinants of Japanese workers’ well-being shifted before and during the COVID-19 pandemic. We estimate a Bayesian hierarchical panel model and Markov chain Monte Carlo sampling is implemented with the ancillarity–sufficiency interweaving strategy to handle the high parameter-to-sample ratio efficiently. Consequently, we observed that positive drivers include marriage, good health, job satisfaction, and conversion from nonregular to regular employment, whereas male gender, turnover intention, reduced family contact, and pandemic-related financial concerns lower well-being. Age traces a U-shape, and weekday sleep shows an inverse-U pattern. Although the evidence is correlational and confined to self-reported data from one country, the analysis clarifies how socio-economic and workplace factors interact with a major external shock.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100434"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578579","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}

{"title":"An attention-guided graph spiking approach for seizure localization and detection in healthcare","authors":"Resmi Cherian ,&nbsp;E. Grace Mary Kanaga","doi":"10.1016/j.health.2025.100437","DOIUrl":"10.1016/j.health.2025.100437","url":null,"abstract":"<div><div>Epilepsy is a chronic neurological disorder with recurrent seizures, posing significant challenges for timely diagnosis and treatment. The manual identification of seizures from long-term EEG is labour-intensive, time-consuming, and heavily dependent on expertise, which underscores the need for automated seizure detection systems. This study proposes a patient-specific hybrid Graph Neural Network–Spiking Neural Network (GNN–SNN) framework, integrating attention-driven channel importance estimation with graph-based spiking dynamics for interpretable seizure detection. The EEG channels are represented as graph nodes with attention layers modeling their spatial relationships, and Adaptive Leaky Integrate-and-Fire neurons represent biologically inspired temporal dynamics. A key feature of the framework is its capacity to measure channel-wise contributions through attention weights, which yields human-interpretable information about which EEG channels contribute most to seizure detection. Each model is trained and optimized independently for every patient to learn their unique spatiotemporal EEG patterns, preserving the patient-specific design while maintaining a uniform architectural pattern for all subjects. The proposed model achieves robust performance on the CHB-MIT dataset, with 98.94 % accuracy, 95.01 % sensitivity, and 99.23 % specificity, while improving interpretability for EEG-based seizure detection. Although this model emphasizes dominant EEG channels, the model is not suitable for clinical localization and would require validation by intracranial EEG (iEEG) for translational applications. The integration of graph attention mechanisms with spiking computation provides better seizure detection with physiologically interpretable insights into EEG channel contributions. Future work will focus on validating these interpretability results on clinical gold standards, generalizing to patient-independent scenarios, and scaling up the framework for energy-efficient, real-time seizure monitoring.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100437"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578577","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}

{"title":"An analytics-based model for securing the healthcare drug distribution network with blockchain","authors":"Herry Irawan ,&nbsp;Adinda Amalia Putri Abidin ,&nbsp;Andry Alamsyah","doi":"10.1016/j.health.2025.100440","DOIUrl":"10.1016/j.health.2025.100440","url":null,"abstract":"<div><div>The surge of counterfeit pharmaceuticals and vaccines in Indonesia poses a significant public health threat, compromising treatment effectiveness and jeopardising patient safety. Current pharmaceutical supply chain systems are constrained in scalability, transparency, and real-time verification, impeding initiatives to guarantee medicine authenticity, including expiration and dose accuracy. This paper suggests a blockchain-based paradigm specifically designed for Indonesia's pharmaceutical supply chain, focusing on traceability, security, and regulatory compliance.</div><div>The research employs a qualitative methodology that incorporates literature review, stakeholder interviews, and interface prototyping. A smart contract simulation is executed to verify essential supply chain operations, including medicine serialization, batch approval, recall, and dispensing control. The experimental assessment indicates that the contract operates with minimal latency and deterministic enforcement, guaranteeing dependable real-time validation at the point of care.</div><div>The preliminary results indicate that the blockchain prototype augments traceability, mitigates counterfeit distribution, and facilitates coordination among stakeholders, including producers, regulators, healthcare professionals, and patients. The results highlight blockchain's capacity to facilitate policy reform and digital transformation in pharmaceutical governance, enhancing regulatory compliance and public health outcomes in Indonesia.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100440"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683793","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}

{"title":"A penalized regression and machine learning approach for quality-of-life prediction in psoriasis patients","authors":"Teerawat Simmachan ,&nbsp;Napatsawan Lerdpraserdpakorn ,&nbsp;Jarupa Deesrisuk ,&nbsp;Chanadda Sriwipat ,&nbsp;Subij Shakya ,&nbsp;Pichit Boonkrong","doi":"10.1016/j.health.2025.100417","DOIUrl":"10.1016/j.health.2025.100417","url":null,"abstract":"<div><div>Psoriasis is a chronic inflammatory skin disease that significantly affects patients’ quality of life (QoL), as measured by the Dermatology Life Quality Index (DLQI). This study employs penalized regression and machine learning (ML) techniques to develop predictive models for DLQI in psoriasis patients. Using a dataset of 149 Thai patients, 16 models including multiple linear regression (MLR), five penalized regression models, five Random Forest (RF) models, and five Support Vector Regression (SVR) models were trained. Feature selection was performed using ridge, LASSO, adaptive LASSO, elastic net, and adaptive elastic net to optimize predictive accuracy and interpretability. Results indicate that RF-L1L2, a Random Forest model trained on elastic net-selected features, achieved the best performance with the lowest Root Mean Square Error (RMSE) of 5.6344, and lowest Mean Absolute Pencentage Error (MAPE) of 35.5404, outperforming traditional regression models. Bland–Altman analysis further confirmed the superiority of RF models in reducing systematic bias and improving predictive agreement. However, our findings should be interpreted with caution due to the limitations of small-sample size modeling. Key features included four psychological stress factors, age, Psoriasis Area and Severity Index (PASI), comorbidities and gender, reinforcing the interplay between physical and mental health. SHapley Additive exPlanations (SHAP) was employed in model explainability. Integrating ML models into clinical decision-making, can enhance patient stratification and personalized treatment strategies, with potential applications in AI-driven healthcare solutions.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100417"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095131","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}

{"title":"An analytical framework for enhancing cancer care efficiency in North London hospitals","authors":"Elizabeth A. Cooke ,&nbsp;Nadia A.S. Smith ,&nbsp;Donna Chung ,&nbsp;Ben Goretzki ,&nbsp;Spencer A. Thomas ,&nbsp;Adrienne Flanagan ,&nbsp;Craig Gerrand ,&nbsp;Neal Navani ,&nbsp;Prabhakar Rajan ,&nbsp;Ashoke Roy ,&nbsp;Clare Schilling ,&nbsp;Ellie Smyth ,&nbsp;Paul Stimpson ,&nbsp;Sandra J. Strauss ,&nbsp;Derralynn Hughes","doi":"10.1016/j.health.2025.100406","DOIUrl":"10.1016/j.health.2025.100406","url":null,"abstract":"<div><div>We use mathematical and statistical techniques on operational data to examine the impact of different factors on the time to treatment for cancer patients in North London hospitals. Understanding the factors which prolong the time between referral and treatment starting for cancer patients on pathways which cross healthcare providers is imperative to improved patient care. We analyse three tumour pathways which involve transfer of patients between hospitals: sarcoma, urological, and head and neck cancers. Several factors impact on the time to first treatment including demographic characteristics, day of the week first seen and method of communicating the cancer diagnosis. In particular, we found that head and neck patients from lower socioeconomic areas were more likely to have longer times from referral to treatment. Patients with sarcoma who were first seen on a Sunday are more likely to breach the 28-day faster diagnosis standard. This analysis is an important first step in highlighting where focus is needed to improve cancer care pathways. Understanding and mitigating the factors influencing the length of time between referral and treatment could enhance the efficiency of cancer care pathways and, consequently, patient outcomes.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100406"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632687","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}