Symptom Monitoring App Use Associated With Medication Adherence Among Woman Survivors of Breast Cancer on Adjuvant Endocrine Therapy [0.03%]
与乳腺癌幸存者依从内分泌治疗药物有关的症状监测应用程序使用情况
Rebecca A Krukowski,Xin Hu,Sara Arshad et al.
Rebecca A Krukowski et al.
Purpose: Oral adjuvant endocrine therapy (AET) reduces the risk of cancer recurrence and death for women with hormone receptor-positive (HR+) breast cancer. Because of adverse symptoms and socioecologic barriers, AET adhe...
Randomized Controlled Trial
JCO clinical cancer informatics. 2024 Dec:8:e2400179. DOI:10.1200/CCI-24-00179 2024
Development and Validation of Dynamic 5-Year Breast Cancer Risk Model Using Repeated Mammograms [0.03%]
基于重复乳腺X线摄影的动态5年乳腺癌风险模型的开发与验证
Shu Jiang,Debbie L Bennett,Bernard A Rosner et al.
Shu Jiang et al.
Purpose: Current image-based long-term risk prediction models do not fully use previous screening mammogram images. Dynamic prediction models have not been investigated for use in routine care. ...
Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer [0.03%]
合成数据在异质性和罕见的医疗人口中的可操作性：患有癌症的青少年和年轻人
Joshi Hogenboom,Aiara Lobo Gomes,Andre Dekker et al.
Joshi Hogenboom et al.
Purpose: Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharin...
Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review [0.03%]
人工智能在成人癌症幸存者症状监测中的应用：系统综述
Sanam Tabataba Vakili,Darren Haywood,Deborah Kirk et al.
Sanam Tabataba Vakili et al.
Purpose: The adoption of artificial intelligence (AI) in health care may afford new avenues for personalized and patient-centered care. This systematic review explored the role of AI in symptom monitoring for adult cancer...
Metastatic Versus Localized Disease as Inclusion Criteria That Can Be Automatically Extracted From Randomized Controlled Trials Using Natural Language Processing [0.03%]
基于随机对照试验利用自然语言处理自动提取的转移性疾病与局部性疾病作为纳入标准
Paul Windisch,Fabio Dennstädt,Carole Koechli et al.
Paul Windisch et al.
Purpose: Extracting inclusion and exclusion criteria in a structured, automated fashion remains a challenge to developing better search functionalities or automating systematic reviews of randomized controlled trials in o...
Development, Validation, and Clinical Utility of Electronic Patient-Reported Outcome Measure-Enhanced Prediction Models for Overall Survival in Patients With Advanced Non-Small Cell Lung Cancer Receiving Immunotherapy [0.03%]
用于接受免疫治疗的晚期非小细胞肺癌患者的电子患者报告结果测量增强的总生存预测模型的开发、验证和临床效用
Kuan Liao,Sabine N van der Veer,Fabio Gomes et al.
Kuan Liao et al.
Purpose: Electronic patient-reported outcome measures (ePROMs) are increasingly collected routinely in clinical practice and may be prognostic for survival in adults with advanced non-small cell lung cancer (NSCLC) in add...
Automated Electronic Health Record Data Extraction and Curation Using ExtractEHR [0.03%]
利用ExtractEHR进行电子健康档案的自动化数据提取和管理
Tamara P Miller,Kelly D Getz,Edward Krause et al.
Tamara P Miller et al.
Purpose: Although the potential transformative effect of electronic health record (EHR) data on clinical research in adult patient populations has been very extensively discussed, the effect on pediatric oncology research...
Kirk D Wyatt,Brian T Furner,Samuel L Volchenboum
Kirk D Wyatt
@PedsDataCommons discusses automated approaches for data extraction from electronic health records.
Using Machine Learning Models to Predict Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer [0.03%]
使用机器学习模型预测新辅助化疗在乳腺癌中的病理完全缓解反应
Rayhan Erlangga Rahadian,Hong Qi Tan,Bryan Shihan Ho et al.
Rayhan Erlangga Rahadian et al.
Purpose: Neoadjuvant chemotherapy (NAC) is increasingly used in breast cancer. Predictive modeling is useful in predicting pathologic complete response (pCR) to NAC. We test machine learning (ML) models to predict pCR in ...
Perceptions of Implementing Real-Time Electronic Patient-Reported Outcomes and Digital Analytics in a Majority-Minority Cancer Center [0.03%]
majority-minority癌症中心实施实时电子患者报告结果和数字分析的感知状况
Daniela Arcos,Mary Dagsi,Reem Nasr et al.
Daniela Arcos et al.
Purpose: Electronic patient-reported outcome (ePRO) tools are increasingly used to provide first-hand information on patient's symptoms and quality of life. This study explored how patients and health care providers (HCPs...