Virtual Clinical Trials in Oncology-Overview, Challenges, Policy Considerations, and Future Directions [0.03%]
肿瘤学虚拟临床试验概述、挑战、政策考量及未来方向
Kushal T Kadakia,Malke Asaad,Erica Adlakha et al.
Kushal T Kadakia et al.
Validation of a Mortality Composite Score in the Real-World Setting: Overcoming Source-Specific Disparities and Biases [0.03%]
在真实世界环境下验证死亡综合评分：克服特定数据来源的差异和偏见
Michelle H Lerman,Benjamin Holmes,Daniel St Hilaire et al.
Michelle H Lerman et al.
Purpose: This study tested whether a composite mortality score could overcome gaps and potential biases in individual real-world mortality data sources. Complete and accurate mortality data are necessary to calculate impo...
James Moon,Michael LeBlanc,Megan Othus
James Moon
Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records [0.03%]
基于电子健康记录的自然语言处理识别癌症治疗方法
Jiaming Zeng,Imon Banerjee,A Solomon Henry et al.
Jiaming Zeng et al.
Purpose: Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to ident...
Patient and Physician Attitudes Toward Telemedicine in Cancer Clinics Following the COVID-19 Pandemic [0.03%]
新冠肺炎大流行后癌症诊所的患者和医师对远程医疗的态度
Chase J Wehrle,Sang W Lee,Aditya K Devarakonda et al.
Chase J Wehrle et al.
Purpose: COVID-19 has infected more than 94 million people worldwide and caused more than 2 million deaths. Patients with cancer are at significantly increased risk compared with the general population. Telemedicine repre...
Computing the Hazard Ratios Associated With Explanatory Variables Using Machine Learning Models of Survival Data [0.03%]
基于生存数据的机器学习模型中的解释变量的风险比计算方法研究
Sameer Sundrani,James Lu
Sameer Sundrani
Purpose: The application of Cox proportional hazards (CoxPH) models to survival data and the derivation of hazard ratio (HR) are well established. Although nonlinear, tree-based machine learning (ML) models have been deve...
Are Regulations Safe? Reflections From Developing a Digital Cancer Decision-Support Tool [0.03%]
法规安全吗？来自开发数字癌症决策支持工具的反思
Ciarán D McInerney,Beverly C Scott,Owen A Johnson
Ciarán D McInerney
Purpose: Informatics solutions to early diagnosis of cancer in primary care are increasingly prevalent, but it is not clear whether existing and planned standards and regulations sufficiently address patients' safety nor ...
Risk Prediction Using Bayesian Networks: An Immunotherapy Case Study in Patients With Metastatic Renal Cell Carcinoma [0.03%]
基于贝叶斯网络的免疫治疗风险预测：转移性肾细胞癌患者的案例研究
Alind Gupta,Paul Arora,Darren Brenner et al.
Alind Gupta et al.
Purpose: To address the need for more accurate risk stratification models for cancer immuno-oncology, this study aimed to develop a machine-learned Bayesian network model (BNM) for predicting outcomes in patients with met...
Toward Personalized Radiation Therapy of Liver Metastasis: Importance of Serial Blood Biomarkers [0.03%]
基于血清生物标志物的个性化肝转移放射治疗研究的重要性
Ali Ajdari,Yunhe Xie,Christian Richter et al.
Ali Ajdari et al.
Purpose: To assess the added value of serial blood biomarkers in liver metastasis stereotactic body radiation therapy (SBRT). Materials and methods: ...
Development of Machine Learning Algorithms for the Prediction of Financial Toxicity in Localized Breast Cancer Following Surgical Treatment [0.03%]
局部乳腺癌手术治疗后金融毒性的预测的机器学习算法开发
Chris Sidey-Gibbons,André Pfob,Malke Asaad et al.
Chris Sidey-Gibbons et al.
Purpose: Financial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but identification of need is difficult. We sought to dev...