Building a Clinically Relevant Risk Model: Predicting Risk of a Potentially Preventable Acute Care Visit for Patients Starting Antineoplastic Treatment [0.03%]
建立具有临床意义的风险预测模型：抗肿瘤药物治疗起始患者潜在可避免急性期就诊风险的预测模型研究
Bobby Daly,Dmitriy Gorenshteyn,Kevin J Nicholas et al.
Bobby Daly et al.
Purpose: To create a risk prediction model that identifies patients at high risk for a potentially preventable acute care visit (PPACV). Patients and meth...
Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer [0.03%]
乳腺癌早期转移复发的机器学习和机制建模预测
Chiara Nicolò,Cynthia Périer,Melanie Prague et al.
Chiara Nicolò et al.
Purpose: For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression...
Clinical Trials in the Era of Digital Engagement: A SWOG Call to Action [0.03%]
数字化参与时代下的SWOG临床试验行动倡议
Krishna S Gunturu,Don S Dizon,Judy Johnson et al.
Krishna S Gunturu et al.
CIViCpy: A Python Software Development and Analysis Toolkit for the CIViC Knowledgebase [0.03%]
CIViCpy：用于CIViC知识库的Python软件开发和分析工具包
Alex H Wagner,Susanna Kiwala,Adam C Coffman et al.
Alex H Wagner et al.
Purpose: Precision oncology depends on the matching of tumor variants to relevant knowledge describing the clinical significance of those variants. We recently developed the Clinical Interpretations for Variants in Cancer...
Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma [0.03%]
基于CaPTk的脑胶质瘤影像组学研究：多中心无进展生存期和复发模式预测
Anahita Fathi Kazerooni,Hamed Akbari,Gaurav Shukla et al.
Anahita Fathi Kazerooni et al.
Purpose: To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the ti...
Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis [0.03%]
定量评估压缩对数字病理图像分析深度学习的影响
Yijiang Chen,Andrew Janowczyk,Anant Madabhushi
Yijiang Chen
Purpose: Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation o...
OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data [0.03%]
OncoMX：一个在相关癌症和健康数据背景下探索癌症生物标志物的知识库
Hayley M Dingerdissen,Frederic Bastian,K Vijay-Shanker et al.
Hayley M Dingerdissen et al.
Purpose: The purpose of OncoMX1 knowledgebase development was to integrate cancer biomarker and relevant data types into a meta-portal, enabling the research of cancer biomarkers side by side with other pertinent multidim...
Treatment Patterns for Chronic Comorbid Conditions in Patients With Cancer Using a Large-Scale Observational Data Network [0.03%]
基于大规模观察性数据网络的癌症患者的慢性伴发症治疗模式研究
Ruijun Chen,Patrick Ryan,Karthik Natarajan et al.
Ruijun Chen et al.
Purpose: Patients with cancer are predisposed to developing chronic, comorbid conditions that affect prognosis, quality of life, and mortality. While treatment guidelines and care variations for these comorbidities have b...
Developing an FHIR-Based Computational Pipeline for Automatic Population of Case Report Forms for Colorectal Cancer Clinical Trials Using Electronic Health Records [0.03%]
基于FHIR的计算管线开发，用于自动从电子健康记录中填充结直肠癌临床试验病例报告表
Nansu Zong,Andrew Wen,Daniel J Stone et al.
Nansu Zong et al.
Purpose: The Fast Healthcare Interoperability Resources (FHIR) is emerging as a next-generation standards framework developed by HL7 for exchanging electronic health care data. The modeling capability of FHIR in standardi...
Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care [0.03%]
医疗保健中来自联合数据库的保护隐私的分布式机器学习系统评价研究
Fadila Zerka,Samir Barakat,Sean Walsh et al.
Fadila Zerka et al.
Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseas...