Health Informatics Journal最新文献_第10页

Using machine learning models to predict coronary artery calcium scores in firefighters. 使用机器学习模型预测消防员冠状动脉钙评分。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-10-17 DOI: 10.1177/14604582251381274

Mingyue Li, Jiali Han, Carolyn Muegge, Terrell Zollinger, Yixi Xu, Laura Y Zhou, Patrick Monahan, Jennifer Wessel, Vanessa Kleinschmidt, Steven Moffatt, Hongmei Nan

{"title":"Using machine learning models to predict coronary artery calcium scores in firefighters.","authors":"Mingyue Li, Jiali Han, Carolyn Muegge, Terrell Zollinger, Yixi Xu, Laura Y Zhou, Patrick Monahan, Jennifer Wessel, Vanessa Kleinschmidt, Steven Moffatt, Hongmei Nan","doi":"10.1177/14604582251381274","DOIUrl":"10.1177/14604582251381274","url":null,"abstract":"Objective: To develop and compare the predictive accuracy of machine learning (ML) models for coronary artery calcium (CAC) prediction among firefighters and to evaluate their cross-validated performance against traditional binary logistic regression (BLR). Methods: This study utilized health records from 416 firefighters who underwent comprehensive health screenings at Ascension Public Safety Medical. CAC was assessed using cardiac computed tomography scans. The degree of CAC was measured using the Agatston scores. 17 clinical and lifestyle related risk variables were collected. Machine learning models, including XGBoost, Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K Nearest Neighbor (KNN), were developed and compared. Additionally, the performance of these ML models was evaluated against traditional binary logistic regression (BLR). Results: Among the 416 firefighters, age (r = 0.28, p < 0.0001), glucose levels (r = 0.13, p = 0.001), monocyte percentages (r = 0.13, p = 0.001), and resting systolic blood pressure (r = 0.13, p = 0.009) were positively associated with CAC. While sodium levels (r = -0.11, p = 0.038), GFR (r = -0.17, p = 0.021), and maximum oxygen volumes (r = -0.19, p = 0.0002) were inversely associated with CAC. XGBoost achieved the highest cross-validated area under the curve (AUC) of 0.770, outperforming NB (0.768), SVM (0.765), RF (0.749), KNN (0.671), and BLR (0.658). Conclusion: Our research demonstrates the efficacy of ML algorithms, particularly XGBoost, in enhancing early detection and preventive strategies for CAC among firefighters. These advancements are crucial for proactive health management in this high-risk group, potentially mitigating risks associated with their demanding profession.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251381274"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced monitoring, alerting and feedback in early risk mitigation for pancreatic cancer. 胰腺癌早期风险缓解的先进监测、预警和反馈

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-10-15 DOI: 10.1177/14604582251387969

George Manias, Nelina Angelova, Diana Kirova, Aristodemos Pnevmatikakis, Pencho Stefanov, Fabio Melillo, Oscar Garcia Perales, Konstantina Kostopoulou, Sofoklis Kyriazakos, Dimosthenis Kyriazis

{"title":"Advanced monitoring, alerting and feedback in early risk mitigation for pancreatic cancer.","authors":"George Manias, Nelina Angelova, Diana Kirova, Aristodemos Pnevmatikakis, Pencho Stefanov, Fabio Melillo, Oscar Garcia Perales, Konstantina Kostopoulou, Sofoklis Kyriazakos, Dimosthenis Kyriazis","doi":"10.1177/14604582251387969","DOIUrl":"10.1177/14604582251387969","url":null,"abstract":"A novel patient monitoring system is introduced, designed to support early risk mitigation in pancreatic cancer through personalized health interventions. The system aims to strengthen patient engagement and proactive care by enabling healthcare practitioners (HCP) to assign dynamic, data-driven and personalized mitigation plans. Central to this system is a user interface that allows HCP to review and assign tailored mitigation plans to patients. These plans are formulated based on primary clinical data and enriched with secondary behavioral data, such as wearable-derived metrics and self-reported inputs. These inputs are continuously collected, transformed into Holistic Health Records (HHRs), and stored in a scalable platform for integration with ML-based trend analysis and visualization. The article outlines the system's technical architecture, patient data evaluation logic, and user experience across both the HCP interface and patient app. Evaluation by end users via questionnaires demonstrated improved adherence to plans and higher-quality behavioral data. This monitoring platform offers a promising tool for facilitating early risk intervention in pancreatic cancer care. By integrating multi-source patient data into actionable strategies and fostering bidirectional engagement, it bridges the gap between clinical insight and patient participation, contributing to holistic health management.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251387969"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictors of and reasons for refusal to participate in a digitally supported remote maintenance pulmonary rehabilitation programme. 拒绝参加数字支持的远程维持肺康复计划的预测因素和原因。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-10-17 DOI: 10.1177/14604582251345329

François Alexandre, Virginie Molinier, Espérance Moine, Sébastien Kuss, François Bughin, Antonin Vernet, Guillaume Coste, Amandine Calvat, Virginie Guerre, Nicolas Oliver, Maurice Hayot, Nelly Heraud

{"title":"Predictors of and reasons for refusal to participate in a digitally supported remote maintenance pulmonary rehabilitation programme.","authors":"François Alexandre, Virginie Molinier, Espérance Moine, Sébastien Kuss, François Bughin, Antonin Vernet, Guillaume Coste, Amandine Calvat, Virginie Guerre, Nicolas Oliver, Maurice Hayot, Nelly Heraud","doi":"10.1177/14604582251345329","DOIUrl":"10.1177/14604582251345329","url":null,"abstract":"Objective: The study aimed to assess the predictors and the reasons for refusal to participate in a digitally supported remote maintenance pulmonary rehabilitation programme (M-PRP). Methods: Patients contacted to integrate a 12-month M-PRP were assessed for clinical and sociodemographic characteristics and completed a series of 11 questionnaires including digital literacy (MDPQ-16), personality traits (BFI-10) and reasons for refusal. Results: Of the 136 patients included, 78 accepted the M-PRP and 58 refused (43%). The likelihood of refusal was associated with low forced expiratory volume in 1s (FEV1), body mass index, neuroticism (BFI-10) and MDPQ-16. Main reasons for refusal were programme constraints (47%), intention to continue physical activity alone (45%), and lack of information technologies (IT) equipment (29%). Conclusion: Digital M-PRP rejection is a common problem. Disease severity and technology issues are main barriers. Particular attention should be paid to patients who state they intend to continue on their own, given conflicting literature data.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251345329"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Navigating through regulatory frameworks for digital therapeutics and biomarkers. 浏览数字疗法和生物标志物的监管框架。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-10-09 DOI: 10.1177/14604582251387656

Cinja Koller, Marc Blanchard, Thomas Hügle

{"title":"Navigating through regulatory frameworks for digital therapeutics and biomarkers.","authors":"Cinja Koller, Marc Blanchard, Thomas Hügle","doi":"10.1177/14604582251387656","DOIUrl":"10.1177/14604582251387656","url":null,"abstract":"Background: Digital health technologies are often subject to regulatory requirements. Regulatory auditing processes are complex but necessary to guarantee quality, efficacy and safety of patients. Evolvements such as digitalized clinical trials, and digital biomarkers require a constant adaption of regulatory frameworks. Objective: This review aims to provide an overview on current regulations and standards for digital therapeutics and digital biomarkers, from technical development to market access. Methods: We conducted an unstructured literature review to identify the relevant guidelines, policies and standards for software based digital therapeutics and digital biomarkers. Results: The principal regulations governing software as a medical device are outlined in Chapter 21 of the Code of Federal Regulations by the US Food and Drug Administration, as well as the European Medical Device Regulation 2017/745. Regulatory pathways, such as the DiGA, are in the process of development, particularly for digital therapeutics, which fall within the purview of software as a medical device. Qualification of (digital) biomarkers is typically voluntary but can play a significant role in the development and approval of digital therapeutics. Conclusions: Fragmented, lacking and diverse regulations around digital biomarkers and digital therapeutics highlight the urge to harmonize and foster regulatory frameworks on an international level.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251387656"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating clinical guidelines with large language models for improved sepsis mortality prediction. 整合临床指南与大型语言模型，以改善败血症死亡率预测。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-11-06 DOI: 10.1177/14604582251387649

Zhen Zhao, Bo An, Tianpeng Zhang, Ruiyi Zhu, Zihao Fan, Guoxing Wang

{"title":"Integrating clinical guidelines with large language models for improved sepsis mortality prediction.","authors":"Zhen Zhao, Bo An, Tianpeng Zhang, Ruiyi Zhu, Zihao Fan, Guoxing Wang","doi":"10.1177/14604582251387649","DOIUrl":"10.1177/14604582251387649","url":null,"abstract":"We develop and validate a clinical guideline-integrated LLM for enhanced sepsis mortality prediction. Using MIMIC-IV data from 24,237 ICU sepsis patients, we fine-tuned a large language model with Low-Rank Adaptation, embedding clinical guidelines into the training process. The model's predictive performance was evaluated using accuracy, F1-score, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Ablation studies assessed the specific contributions of clinical guideline integration. The guideline-enhanced fine-tuned LLM demonstrated moderately higher performance across all evaluation metrics including predictive accuracy (0.819), F1-score (0.815), sensitivity (0.815), specificity (0.822), and AUC (0.852) in predicting mortality risk for septic patients compared to traditional machine learning (highest accuracy: 0.774, AUC: 0.850) and deep learning methods (highest accuracy: 0.762, AUC: 0.841). Ablation experiments demonstrated that explicit integration of clinical guideline knowledge substantially improved performance over both direct prompting (accuracy: 0.709, AUC: 0.706) and fine-tuning without clinical guidelines (accuracy: 0.786, AUC: 0.801). These findings demonstrate that incorporating clinical guidelines into the fine-tuning of large language models outperforms both traditional and deep learning baselines across multiple metrics in sepsis mortality prediction, highlighting the value of explicit domain knowledge integration for clinical AI's robustness.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251387649"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145454110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A pilot for automated pages from the EHR: Improving time between active restraint orders in the pediatric intensive care unit. EHR自动页面的试点：改善儿科重症监护病房主动约束命令之间的时间。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-12-05 DOI: 10.1177/14604582251401402

Jessica Pourian, Aris Oates

{"title":"A pilot for automated pages from the EHR: Improving time between active restraint orders in the pediatric intensive care unit.","authors":"Jessica Pourian, Aris Oates","doi":"10.1177/14604582251401402","DOIUrl":"10.1177/14604582251401402","url":null,"abstract":"Introduction: Pediatric restraint orders require frequent renewal to ensure patient safety. Previously, providers depended on nurses paging them upon order expiration, leading to lapses. Methods: In April 2023, we implemented an alerting system in our 38-bed pediatric intensive care unit (PICU) that sent automated text messages to providers upon order expiration. Pediatric wards and adult ICU served as controls. We analyzed 2 years of restraint order data. An unpaired t-test compared pre- and post-intervention. Results: A total of 1394 orders were included (133 PICU, 628 pediatric wards, 633 adult ICU). In the PICU, time without an active order decreased by 39% (2 h 23 min to 1 h 27 min, p = .24) though this result did not reach statistical significance. Conclusion: Despite not reaching statistical significance, this exploratory case study demonstrated that automated EHR alerts may reduce time without an active restraint order. This pilot led the institution's informatics team to system-wide adoption. While promising, such systems must be balanced against risks like provider alarm fatigue.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251401402"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145688707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Methods to modernize a multimedia, web-based reproductive health education intervention for individuals with sickle cell disease or trait using virtual human narration and user-centered design. 方法：采用虚拟人叙述和以用户为中心的设计，对镰状细胞病患者进行多媒体、基于网络的生殖健康教育干预。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-11-21 DOI: 10.1177/14604582251397311

Alexandre Gomes de Siqueira, Gina M Gehling, Aravind Subramanian, Anna Le, Rishabh Garg, Abigail R Islam, Destiny Gordon, Tyra Reed, Amelia Greenlee, Guettchina Telisnor, Andrea Rangel, Candice J Adams-Mitchell, Brenda W Dyal, Keesha Powell-Roach, Lucien Vandy Black, Miriam Ezenwa, Yingwei Yao, Agatha M Gallo, Sriram Kalyanaraman, Diana J Wilkie

{"title":"Methods to modernize a multimedia, web-based reproductive health education intervention for individuals with sickle cell disease or trait using virtual human narration and user-centered design.","authors":"Alexandre Gomes de Siqueira, Gina M Gehling, Aravind Subramanian, Anna Le, Rishabh Garg, Abigail R Islam, Destiny Gordon, Tyra Reed, Amelia Greenlee, Guettchina Telisnor, Andrea Rangel, Candice J Adams-Mitchell, Brenda W Dyal, Keesha Powell-Roach, Lucien Vandy Black, Miriam Ezenwa, Yingwei Yao, Agatha M Gallo, Sriram Kalyanaraman, Diana J Wilkie","doi":"10.1177/14604582251397311","DOIUrl":"10.1177/14604582251397311","url":null,"abstract":"Objectives: For a population that lacks genetic inheritance knowledge about sickle cell disease/trait, a previously innovative web-based intervention showed significant and sustained knowledge improvement, but current users judged it as \"dated.\" We aimed to modernize the reproductive health intervention, revitalizing its presentation format and providing access to it via all operating systems and browsers. Methods: Young adults (N = 82, mean age 30.3 ± 5.8 years, 76% female, 94% Black, 74% never married) and our interdisciplinary team collaborated in an iterative, user-centered redesign process. We added virtual human narration and interactive interface enhancements to improve accessibility, engagement, and cross-platform compatibility. Functionality testing continued iteratively until all components operated as intended across devices and browsers. Results: After 9 months of redevelopment, the educational program functioned as intended on Windows, Android, and Apple computers and mobile devices. Of the 82 users, 100% enjoyed using it, and 95% indicated it was easy to use. Conclusion: Redevelopment required 6 months longer than expected due to the scope of the updates and integration of advanced features. Designed for an underserved population, the modernized intervention is now undergoing evaluation in a randomized controlled trial. Future directions include the integration of conversational AI and broader application in digital health education.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251397311"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12758982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145574930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of treatment protocols on hospital length of stay for COVID-19 patients: A machine learning analysis of cases in Khuzestan province, Iran. 治疗方案对COVID-19患者住院时间的影响：对伊朗胡齐斯坦省病例的机器学习分析

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-12-08 DOI: 10.1177/14604582251401416

Soheila Shamouni Sayaei, Amir Jamshidnezhad, Javad Zarei, Maryam Haddadzadeh Shoushtari, Mohammad Reza Akhoond

{"title":"Impact of treatment protocols on hospital length of stay for COVID-19 patients: A machine learning analysis of cases in Khuzestan province, Iran.","authors":"Soheila Shamouni Sayaei, Amir Jamshidnezhad, Javad Zarei, Maryam Haddadzadeh Shoushtari, Mohammad Reza Akhoond","doi":"10.1177/14604582251401416","DOIUrl":"10.1177/14604582251401416","url":null,"abstract":"Objective: COVID-19 has heavily burdened healthcare systems worldwide, underscoring the need for accurate treatment decision-making to optimize patient recovery. This study leverages machine learning (ML) to evaluate how treatments affect the length of stay (LOS) for hospitalized COVID-19 patients in Iran. Method: We analyzed clinical data from 1793 patients with 106 features, identifying key variables through detailed profiles. Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Artificial Neural Network (ANN) models were then used to predict LOS based on personalized COVID-19 treatment regimens. Results: Actemra and Bromhexine exhibited the strongest correlation with LOS. In the first experiment, the models achieved average predictive accuracies of 90.0% (SVM), 89.53% (k-NN), and 86.30% (ANN); in the second experiment, the accuracies were 96.8% (SVM), 89.53% (k-NN), and 94.56% (ANN), demonstrating their effectiveness in forecasting hospital stay durations. Conclusion: Our study showed that medications such as Actemra and Bromhexine were associated with the affected factors for predicting LOS, especially when administered early to patients without major comorbidities. Those with conditions such as cardiovascular disease or diabetes had longer stays. The ML models predicted LOS with high accuracy, demonstrating their potential to assist clinical decisions. Overall, early treatment and predictive modeling can enhance patient outcomes and optimize hospital resource use.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251401416"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145702884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of machine learning to identify key factors influencing agricultural workers' mental health: A case study of Thai farmers. 应用机器学习识别影响农业工人心理健康的关键因素：以泰国农民为例

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-10-29 DOI: 10.1177/14604582251388827

Papis Wongchaisuwat, Veerasit Kaewbundit, Saisattha Noomnual

{"title":"Application of machine learning to identify key factors influencing agricultural workers' mental health: A case study of Thai farmers.","authors":"Papis Wongchaisuwat, Veerasit Kaewbundit, Saisattha Noomnual","doi":"10.1177/14604582251388827","DOIUrl":"10.1177/14604582251388827","url":null,"abstract":"Objectives: This study examined the associations between pesticide exposures, perceived farm stressors, COVID-19-related stressors, and mental health disorders among Thai farmers. Methods: A total of 270 participants were interviewed to assess mental health disorders. Information was also collected on household environments, agricultural activities, and perceived farm- and COVID-19-related stressors. After data preprocessing, 211 samples remained for analysis. Multiple linear regression models were employed as a baseline, and their performance was compared with ensemble tree-based models, which can capture more complex, nonlinear patterns. The Boruta feature selection technique and SHAP scores were used to explain associations between mental health and the independent variables. Results: Lower levels of mental health disorder symptoms were associated with higher levels of personal protective equipment (PPE) use. Certain perceived farm stressors and COVID-19-related stressors were also correlated with mental health outcomes. Conclusions: The findings indicate that greater PPE use and good agricultural practices are associated with reduced symptoms of mental health disorders. This pilot study highlights the potential of machine learning models to explore complex public health issues involving multiple, interrelated factors.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251388827"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145403180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of machine learning to predict periodontal disease in US adults: A cross-sectional analysis of NHANES 2009-2014. 机器学习在美国成人牙周病预测中的应用：NHANES 2009-2014的横断面分析

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2025-10-01 Epub Date: 2025-11-14 DOI: 10.1177/14604582251394617

Giang T Vu, Veena Mayya, Babu Mandhidi, Christian King, Bert B Little, Varadraj Gurupur, Astha Singhal

{"title":"Application of machine learning to predict periodontal disease in US adults: A cross-sectional analysis of NHANES 2009-2014.","authors":"Giang T Vu, Veena Mayya, Babu Mandhidi, Christian King, Bert B Little, Varadraj Gurupur, Astha Singhal","doi":"10.1177/14604582251394617","DOIUrl":"10.1177/14604582251394617","url":null,"abstract":"Background: Periodontal disease (PD) is a primary contributor to tooth loss, which negatively affects oral functionality and quality of life. This research aims to investigate the effectiveness of various machine learning (ML) classifiers in identifying PD among U.S. adults. Method: Nineteen features, selected based on prior literature and expert dentist input, were preprocessed using feature engineering techniques. Eleven machine learning classifiers, including basic and ensemble models, were evaluated to identify the best performing model. The interpretability of the model was evaluated using Shapley additive explanations and individual conditional expectation plots to determine key predictors of periodontitis. Results: The predictive efficacy of the ML classifiers is assessed using metrics such as the area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity. The CatBoost classifier performed best in identifying PD. It achieved an AUC of 84.5%, an accuracy of 75.8%, a precision of 75.8%, a sensitivity of 78.8%, and a specificity of 72.5%. Having an annual dentist visit and age emerged as the most influential variables. Conclusions: The ML models utilized in this study exhibited robust predictive performance and can be further improved by incorporating additional clinical parameters. The proposed models effectively identified individuals at high risk for developing PD.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 4","pages":"14604582251394617"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145514808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0