Automated Identification of Radiotherapy Courses From US Department of Veterans Affairs Administrative Data [0.03%]
从美国退伍军人事务管理数据中自动识别放疗过程
William Schreyer,Ryan Melson,Christopher Anderson et al.
William Schreyer et al.
Purpose: Radiotherapy is a critically important cancer treatment; however, its details are often not well represented in electronic health record data sets. US Veterans' radiation courses are further distributed across a ...
Catherine M DesRoches,Liz Salmi
Catherine M DesRoches
Development of a Composite Measure to Identify Priority Areas of Need for Cancer Screening Interventions [0.03%]
癌症筛查干预优先需求领域识别的复合测度研究及其应用开发
David N Karp,Khaldoun Hamade,Christopher M McNair et al.
David N Karp et al.
Purpose: Cancer centers and health systems are tasked with deciding where to deploy community interventions to reduce the burden of cancer within their catchment areas. Few methods exist to prioritize communities in a sys...
Rapid Clinical Evidence Explorer: A Generative Pre-Trained Transformer-Powered Tool for Automated Oncology Evidence Extraction [0.03%]
基于生成预训练变压器的自动肿瘤学证据提取工具——快速临床证据探索器
Eunyoung Im,Bomi Kim,Sunghoon Kang et al.
Eunyoung Im et al.
Purpose: The rapid expansion of scientific literature has made it increasingly challenging for clinicians and researchers to efficiently identify relevant evidence. While large language models (LLMs) offer promising solut...
Predicting Chemotherapy Response in Patients With Advanced or Metastatic Pancreatic Cancer Using Machine Learning [0.03%]
使用机器学习预测晚期或转移性胰腺癌患者对化疗的反应
Jamin Koo,Gyucheol Choi,Jaekyung Cheon et al.
Jamin Koo et al.
Purpose: Selecting an optimal first-line chemotherapy regimen for advanced or metastatic pancreatic cancer is challenging because of varying efficacy and toxicity profiles of fluorouracil, leucovorin, irinotecan, and oxal...
Development of a Dynamic Counterfactual Risk Stratification Strategy for Newly Diagnosed Patients With AML Treated With Venetoclax and Azacitidine [0.03%]
用于新诊断的急性髓系白血病患者接受维奈克拉和地西他滨治疗的动态反事实风险分层策略的开发
Nazmul Islam,Justin L Dale,Jamie S Reuben et al.
Nazmul Islam et al.
Purpose: The objective of this study was to develop a flexible risk stratification strategy for AML that is specific for venetoclax plus azacitidine (ven/aza), addresses real-world data (RWD) issues, and is also adaptable...
Reimagining Cancer Care With Generative Artificial Intelligence: The Promise of Large Language Models [0.03%]
借助生成式人工智能重塑癌症治疗：大型语言模型的前景
Ji-Eun Irene Yum,Syed Arsalan Ahmed Naqvi,Ben Zhou et al.
Ji-Eun Irene Yum et al.
The emergence of state-of-the-art large language models (LLMs), which hold the ability to generalize to diverse natural language processing tasks, has led to new opportunities in health care. Oncology is especially well-suited to leverage t...
SmokeBERT: A Bidirectional Encoder Representations From Transformers-Based Model for Quantitative Smoking History Extraction From Clinical Narratives to Improve Lung Cancer Screening [0.03%]
烟雾BERT：一种基于双向编码器表示的临床叙述转型者模型，用于量化吸烟史以改善肺癌筛查
Yiming Xue,Yunzheng Zhu,Luoting Zhuang et al.
Yiming Xue et al.
Purpose: Tobacco use is a major risk factor for diseases such as cancer. Granular quantitative details of smoking (eg, pack years and years since quitting) are essential for assessing disease risk and determining eligibil...
Toward Clinical Readiness: Critical Reflections on PATHOMIQ_PRAD and Artificial Intelligence Histologic Classifiers in Prostate Cancer [0.03%]
迈向临床应用：对PATHOMIQ_PRAD和人工智能前列腺癌组织学分类器的批判性思考
Schawanya Kaewpitoon Rattanapitoon,Thirayu Meererksom,Nav La et al.
Schawanya Kaewpitoon Rattanapitoon et al.
Reply to: Toward Clinical Readiness: Critical Reflections on PATHOMIQ_PRAD and Artificial Intelligence Histologic Classifiers in Prostate Cancer [0.03%]
PATHOMIQ_PRAD和前列腺癌人工智慧組織分類器的臨床應用性評論.reply
Ross Liao,Magdalena Fay,Omar Y Mian
Ross Liao