Consolidation of Cancer Registry and Administrative Claims Data on Cancer Diagnosis and Treatment in the US Military Health System [0.03%]
美国军事卫生系统中癌症登记和行政声明数据的整合在癌症诊断和治疗中的应用
Yvonne L Eaglehouse,Amie B Park,Matthew W Georg et al.
Yvonne L Eaglehouse et al.
Purpose: Linked cancer registry and medical claims data have increased the capacity for cancer research. However, few efforts have described methods to select information between data sources, which may affect data use. W...
Real-World Outcomes for Patients Treated With Immune Checkpoint Inhibitors in the Veterans Affairs System [0.03%]
退伍军人事务系统中使用免疫检查点抑制剂治疗患者的现实世界结果
Jennifer La,David Cheng,Mary T Brophy et al.
Jennifer La et al.
Purpose: Increasingly broad patient groups are being treated with immune checkpoint inhibitors (ICIs) in clinical practice, but few studies have assessed their usage and outcomes in large, comprehensive real-world cohorts...
Dimitris Bertsimas,Holly Wiberg
Dimitris Bertsimas
Tailoring Therapy for Children With Neuroblastoma on the Basis of Risk Group Classification: Past, Present, and Future [0.03%]
基于危险分组分类对神经母细胞瘤患儿进行个体化治疗的现在、过去和未来
Wayne H Liang,Sara M Federico,Wendy B London et al.
Wayne H Liang et al.
For children with neuroblastoma, the likelihood of cure varies widely according to age at diagnosis, disease stage, and tumor biology. Treatments are tailored for children with this clinically heterogeneous malignancy on the basis of a comb...
Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports [0.03%]
利用规则来提高从多机构前列腺病理报告中提取信息的机器学习效果
Enrico Santus,Tal Schuster,Amir M Tahmasebi et al.
Enrico Santus et al.
Purpose: Literature on clinical note mining has highlighted the superiority of machine learning (ML) over hand-crafted rules. Nevertheless, most studies assume the availability of large training sets, which is rarely the ...
Multicenter Study
JCO clinical cancer informatics. 2020 Oct:4:865-874. DOI:10.1200/CCI.20.00028 2020
Effect of an Artificial Intelligence Clinical Decision Support System on Treatment Decisions for Complex Breast Cancer [0.03%]
人工智能临床决策支持系统对复杂乳腺癌治疗决策的影响
Fengrui Xu,Martín-J Sepúlveda,Zefei Jiang et al.
Fengrui Xu et al.
Purpose: To examine the impact of a clinical decision support system (CDSS) on breast cancer treatment decisions and adherence to National Comprehensive Cancer Center (NCCN) guidelines. ...
Observational Study
JCO clinical cancer informatics. 2020 Sep:4:824-838. DOI:10.1200/CCI.20.00018 2020
21 Code of Federal Regulations Part 11-Compliant Digital Signature Solution for Cancer Clinical Trials: A Single-Institution Feasibility Study [0.03%]
符合21条联邦法规第11部分的癌症临床试验数字签名解决方案：单中心可行性研究
Therica M Miller,Jenny Lester,Lorna Kwan et al.
Therica M Miller et al.
Purpose: Inefficiencies in the clinical trial infrastructure result in protracted trial completion timelines, physician-investigator turnover, and a shrinking skilled labor force and present obstacles to research particip...
Use of Wearable Activity Tracker in Patients With Cancer Undergoing Chemotherapy: Toward Evaluating Risk of Unplanned Health Care Encounters [0.03%]
穿戴式活动追踪器在癌症化疗患者中的应用：迈向评估非计划医疗接触的风险
Tanachat Nilanon,Luciano P Nocera,Alexander S Martin et al.
Tanachat Nilanon et al.
Purpose: Unplanned health care encounters (UHEs) such as emergency room visits can occur commonly during cancer chemotherapy treatments. Patients at an increased risk of UHEs are typically identified by clinicians using p...
Observational Study
JCO clinical cancer informatics. 2020 Sep:4:839-853. DOI:10.1200/CCI.20.00023 2020
Unsupervised Resolution of Histomorphologic Heterogeneity in Renal Cell Carcinoma Using a Brain Tumor-Educated Neural Network [0.03%]
使用脑瘤教育神经网络进行肾细胞癌的组织形态异质性无监督解析
Kevin Faust,Adil Roohi,Alberto J Leon et al.
Kevin Faust et al.
Purpose: Applications of deep learning to histopathology have proven capable of expert-level performance, but approaches have largely focused on supervised classification tasks requiring context-specific training and depl...
Matthew Nagy,Nathan Radakovich,Aziz Nazha
Matthew Nagy
The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth hold...