Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables [0.03%]
基于机器学习的泌尿系统感染预测：寻找最有信息量变量的研究
Sajjad Farashi,Hossein Emad Momtaz
Sajjad Farashi
Background: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold sta...
Indirect determination of hemoglobin A2 reference intervals in Pakistani infants using data mining [0.03%]
利用数据挖掘间接确定巴基斯坦婴儿HBA2参考区间上限值
Muhammad Shariq Shaikh,Sibtain Ahmed,Saba Farrukh et al.
Muhammad Shariq Shaikh et al.
Background: Reference intervals (RIs) are crucial for distinguishing healthy from sick individuals and vary across age groups. Hemoglobinopathies are common in Pakistan, making the quantification of hemoglobin variants es...
Observational Study
BMC medical informatics and decision making. 2025 Jan 9;25(1):15. DOI:10.1186/s12911-025-02857-4 2025
Explainable unsupervised anomaly detection for healthcare insurance data [0.03%]
用于健康保险数据的可解释的无监督异常检测
Hannes De Meulemeester,Frank De Smet,Johan van Dorst et al.
Hannes De Meulemeester et al.
Background: Waste and fraud are important problems for health insurers to deal with. With the advent of big data, these insurers are looking more and more towards data mining and machine learning methods to help in detect...
Developing and evaluating a gamified self-management application for inflammatory bowel disease using the ADDIE model and Sukr framework [0.03%]
使用ADDIE模型和Sukr框架开发和评估炎症性肠病游戏化自我管理应用程序及评价
Narges Norouzkhani,Somaye Norouzi,Mahbobeh Faramarzi et al.
Narges Norouzkhani et al.
Background: The prevalence and chronic nature of Inflammatory Bowel Diseases (IBD) is a significant global concern. As the essential part of treatments approach, patient adherence to treatment protocols and self-managemen...
Investigation of the causal relationship between patient portal utilization and patient's self-care self-efficacy and satisfaction in care among patients with cancer [0.03%]
调查患者门户使用与癌症患者的自我护理效能和护理满意度之间的因果关系
Jaeyoung Park,Shilin Guo,Muxuan Liang et al.
Jaeyoung Park et al.
Objective: The objective of this study was to examine the causal relationship between the usage of patient portals and patients' self-care self-efficacy and satisfaction in care outcomes in the context of cancer care. ...
Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review [0.03%]
皮肤图像分析在检测和量化银屑病、白癜风和斑秃病变中的应用：系统文献综述
Athanasios Kallipolitis,Konstantinos Moutselos,Argyriοs Zafeiriou et al.
Athanasios Kallipolitis et al.
Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and automated detection as well as qua...
Examining cancer patient preferences during three stages of decision making and family involvement: a multicenter survey study in China [0.03%]
中国多中心调查研究：三项关于癌症患者在治疗决策过程中及其家属参与情况的调查研究
Siyu Yan,Danqi Wang,Qiao Huang et al.
Siyu Yan et al.
Background: Medical decision-making is a complex multi-stage process. Chinese cancer patients' preference for participation in decision-making stages, family involvement and influencing factors remain unclear. ...
CT-based nomogram predicts esophageal gastric variceal bleeding in noncirrhotic portal hypertension caused by hepatic schistosomiasis [0.03%]
基于CT的预测模型可评估华支睾吸虫病非肝硬化门脉高压患者食管胃静脉曲张出血风险
Wei Cheng,Ke-Ying Wang,Wen-Qiang Li et al.
Wei Cheng et al.
Background: To construct a nomogram combining CT varices vein evaluation and clinical laboratory tests for predicting the risk of esophageal gastric variceal bleeding (EGVB) in patients with noncirrhotic portal hypertensi...
Correction: Early warning score validation methodologies and performance metrics: a systematic review [0.03%]
对早期预警评分验证方法和性能指标的系统综述的更正
Andrew Hao Sen Fang,Wan Tin Lim,Tharmmambal Balakrishnan
Andrew Hao Sen Fang
A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection [0.03%]
一种基于特征融合的混合CNN-Bi-LSTM模型的癫痫发作检测方法研究
Xiaoshuai Cao,Shaojie Zheng,Jincan Zhang et al.
Xiaoshuai Cao et al.
Background: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent year...