Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling [0.03%]
个性化肿瘤学的计算模型导航：克服挑战和拓展视野
Melike Sirlanci,David Albers,Jennifer Kwak et al.
Melike Sirlanci et al.
Objectives: We discuss challenges using computational modeling approaches for personalized prediction in clinical practice to predict treatment response for rare diseases treated by novel therapies using clinical oncology...
Transport-based transfer learning on Electronic Health Records: application to detection of treatment disparities [0.03%]
基于传输的电子健康记录迁移学习：治疗差异检测应用
Wanxin Li,Saad Ahmed,Yongjin P Park et al.
Wanxin Li et al.
Objectives: Electronic Health Records (EHRs) sampled from different populations can introduce unwanted biases, limit individual-level data sharing, and make the data and fitted model hardly transferable across different p...
Negative descriptors in electronic health records of patients with diabetes [0.03%]
糖尿病患者电子健康档案中的负面描述词
Tony Y Sun,Mika Baugh,Emily R Gordon et al.
Tony Y Sun et al.
Background: Negative descriptors in electronic health records (EHR) contribute to worse health outcomes; studies show they are also more prevalent in EHRs of women and racial minorities and affect downstream research bias...
Multimodal deep learning for immunotherapy response prediction and biomarker discovery in non-small cell lung cancer [0.03%]
基于多模态深度学习的非小细胞肺癌免疫治疗反应预测和生物标志物发现模型
Zijun Wang,Xi Liu,Kaitai Han et al.
Zijun Wang et al.
Objective: Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address t...
Incorporating preprints in systematic reviews: a preliminary study of a novel method for rapid evidence synthesis [0.03%]
预印本纳入系统评价的初步研究：一种快速证据合成的新方法研究
Jiayi Tong,Yifei Sun,Rebecca A Hubbard et al.
Jiayi Tong et al.
Objectives: By October 1, 2024, over 450,000 COVID-19 manuscripts were published, with 10% posted as unreviewed preprints. While they accelerate knowledge sharing, their inconsistent quality complicates systematic studies...
Prediction of postoperative infections by strategic data imputation and explainable machine learning [0.03%]
基于策略性数据插补和可解释机器学习的术后感染预测模型研究
Hugo Guillen-Ramirez,Daniel Sanchez-Taltavull,Stéphanie Perrodin et al.
Hugo Guillen-Ramirez et al.
Objectives: Infections following healthcare-associated interventions drive patient morbidity and mortality, making early detection essential. Traditional predictive models utilize preoperative surgical characteristics. Th...
Enabling inclusive systematic reviews: incorporating preprint articles with large language model-driven evaluations [0.03%]
利用大型语言模型驱动的评估纳入预印本文章开展包容性系统评价
Rui Yang,Jiayi Tong,Haoyuan Wang et al.
Rui Yang et al.
Objectives: Systematic reviews in comparative effectiveness research require timely evidence synthesis. With the rapid advancement of medical research, preprint articles play an increasingly important role in accelerating...
Electronic health records-based algorithms to screen for U.S. Centers for Disease Control and Prevention tier 1 genetic diseases: a scoping review [0.03%]
基于电子健康记录的算法筛查美国疾病控制与预防中心一级遗传病：系统综述
William R Harris,Marianna S Hernandez,Khanh N H Ngo et al.
William R Harris et al.
Objective: Missed diagnosis of genetic conditions is a persistent challenge in clinical care, particularly for familial hypercholesterolemia (FH), hereditary breast and ovarian cancer (HBOC), and Lynch syndrome-conditions...
Effect of electronic drug-drug interaction alerts on patient and clinician outcomes: a systematic review [0.03%]
电子药物相互作用警报对患者和临床医生的影响：系统性综述
Anne M Holbrook,Jessyca Matos Silva,Junaid Ahmed Yaser Faruque et al.
Anne M Holbrook et al.
Objectives: Drug interaction checking software is ubiquitous in clinical decision support systems (CDSS-DI) but patient relevance and accuracy are variable and the impact on patient outcomes is unproven. We compared the e...
Envisioning the future of primary care: intervention strategies to support patient-centered communication feedback technology [0.03%]
展望初级保健的未来：支持以患者为中心的沟通反馈技术的干预策略
Raina Langevin,Deepthi Mohanraj,Libby Shah et al.
Raina Langevin et al.
Objective: Clinician implicit bias can impede patient-centered communication, leading to health care inequities. While the field of implicit bias education is evolving with advances in technology, clinicians' perspectives...