Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care
Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care
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DOI：
10.1634/theoncologist.2018-0257
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发表时间：
2019-06-01
期刊：
影响因子：
5.8
通讯作者：
Chin, Lynda
中科院分区：
文献类型：
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作者：
Simon, George;DiNardo, Courtney D.;Chin, Lynda
背景 科学的快速进步对社区环境中及时采用循证护理提出了挑战。为了弥合可能性与实践之间的差距，我们研究了开发人工智能 (AI) 应用程序的方法，该应用程序可以提供实时的针对特定患者的决策支持。材料和方法肿瘤专家顾问（OEA）旨在模拟点对点咨询，具有三个核心功能：患者病史总结、治疗方案推荐和管理咨询。机器学习算法经过训练，可以构建患者癌症病史的动态摘要，并建议批准的治疗或研究性试验选项。所有使用的患者数据都是回顾性累积的。为大约 1,000 名独特的患者建立了基本事实。包含超过 2300 万篇已发表摘要的完整 Medline 数据库被用作文献语料库。结果 OEA 在电子病历中搜索不同来源以从非结构化文本文档中提取复杂临床概念的准确性各不相同，非时间依赖性概念（例如诊断）的 F1 分数为 90%-96%，时间依赖性概念（例如治疗史时间线）的 F1 分数为 63%-65%。根据构建的患者档案，OEA 建议与支持证据相关的批准治疗方案（99.9% 召回率；88% 精确度），并筛选符合条件的临床试验（97.9% 召回率；96.9% 精确度）。结论 我们的结果证明了人工智能应用程序在背景下构建纵向患者档案并提出循证治疗和试验方案的技术可行性。我们的经验强调了临床和人工智能领域之间合作的必要性，以及从设计到培训到测试的整个过程中对临床专业知识的要求。对实践的影响 人工智能 (AI) 支持的数字顾问（例如肿瘤专家顾问）有潜力增强执业肿瘤科医生的能力并更新其知识库。通过从不同的数据源构建动态患者档案，并组织和审查与特定患者相关的大量文献，此类人工智能应用程序可以使肿瘤学家能够根据患者的最新科学证据考虑所有治疗方案，并帮助他们花更少的时间“寻找和收集”信息，而将更多时间花在患者身上。然而，实现这一目标不仅需要人工智能技术的成熟，还需要临床专家的积极参与和领导。
Background Rapid advances in science challenge the timely adoption of evidence-based care in community settings. To bridge the gap between what is possible and what is practiced, we researched approaches to developing an artificial intelligence (AI) application that can provide real-time patient-specific decision support. Materials and Methods The Oncology Expert Advisor (OEA) was designed to simulate peer-to-peer consultation with three core functions: patient history summarization, treatment options recommendation, and management advisory. Machine-learning algorithms were trained to construct a dynamic summary of patients cancer history and to suggest approved therapy or investigative trial options. All patient data used were retrospectively accrued. Ground truth was established for approximately 1,000 unique patients. The full Medline database of more than 23 million published abstracts was used as the literature corpus. Results OEA's accuracies of searching disparate sources within electronic medical records to extract complex clinical concepts from unstructured text documents varied, with F1 scores of 90%-96% for non-time-dependent concepts (e.g., diagnosis) and F1 scores of 63%-65% for time-dependent concepts (e.g., therapy history timeline). Based on constructed patient profiles, OEA suggests approved therapy options linked to supporting evidence (99.9% recall; 88% precision), and screens for eligible clinical trials on (97.9% recall; 96.9% precision). Conclusion Our results demonstrated technical feasibility of an AI-powered application to construct longitudinal patient profiles in context and to suggest evidence-based treatment and trial options. Our experience highlighted the necessity of collaboration across clinical and AI domains, and the requirement of clinical expertise throughout the process, from design to training to testing. Implications for Practice Artificial intelligence (AI)-powered digital advisors such as the Oncology Expert Advisor have the potential to augment the capacity and update the knowledge base of practicing oncologists. By constructing dynamic patient profiles from disparate data sources and organizing and vetting vast literature for relevance to a specific patient, such AI applications could empower oncologists to consider all therapy options based on the latest scientific evidence for their patients, and help them spend less time on information "hunting and gathering" and more time with the patients. However, realization of this will require not only AI technology maturation but also active participation and leadership by clincial experts.