Automated identification and assignment of colonoscopy surveillance recommendations for individuals with colorectal polyps
Automated identification and assignment of colonoscopy surveillance recommendations for individuals with colorectal polyps
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DOI：
10.1016/j.gie.2021.05.036
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发表时间：
2021-10-13
影响因子：
7.7
通讯作者：
Hsu, William
中科院分区：
文献类型：
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作者：
Peterson, Emma;May, Folasade P.;Hsu, William
背景和目标：确定结直肠息肉患者的监测间隔是至关重要的，但要做到可靠既耗时又具有挑战性。我们提出了一个管道的开发和评估，该管道利用自然语言处理技术自动提取和分析来自自由文本结肠镜检查和病理报告的相关息肉发现。利用这些信息，我们将个体患者分为6个结肠镜检查后监测间隔期，由美国结直肠癌多学会工作组定义。使用一组来自单个卫生系统中324名患者的546份随机选择的结肠镜检查和病理学报告，我们使用统计分类器和基于规则的方法的组合来从每种报告类型中提取息肉属性，将属性与独特的息肉相关联，并通过整合来自两种报告类型的信息将患者分类为6个风险类别之一。然后，我们通过确定该算法的阳性预测值（PPV）、灵敏度和F分数，并与胃肠病学家确定的监测间隔进行比较，评估了管道的性能。该管道是使用346份报告开发的（224例结肠镜检查和122例病理学检查），并在来自100例患者的200份报告（100份结肠镜检查和100份病理学检查）的独立测试集上进行评价。在结肠镜检查的目标实体中，我们的平均PPV、灵敏度和F评分分别为0.92、0.95和0.93。病理提取达到PPV、灵敏度和F评分为0.95、0.97和0.96。该系统实现了92%的整体准确性，在分配推荐的间隔监测colonoscopy.Conclusions：这项研究表明，使用机器学习自动提取结果和分类患者适当的风险类别和相应的监测间隔的可行性。建立该系统可以促进筛查结肠镜检查后的主动和及时随访，并实现对预防计划和提供者的实时质量评估。
Background and Aims: Determining surveillance intervals for patients with colorectal polyps is critical but time-consuming and challenging to do reliably. We present the development and assessment of a pipeline that leverages natural language processing techniques to automatically extract and analyze relevant polyp findings from free-text colonoscopy and pathology reports. Using this information, we categorized individual patients into 6 postcolonoscopy surveillance intervals defined by the U.S. Multi-Society Task Force on Colorectal Cancer.Methods: Using a set of 546 randomly selected colonoscopy and pathology reports from 324 patients in a single health system, we used a combination of statistical classifiers and rule-based methods to extract polyp properties from each report type, associate properties with unique polyps, and classify a patient into 1 of 6 risk categories by integrating information from both report types. We then assessed the pipeline's performance by determining the positive predictive value (PPV), sensitivity, and F-score of the algorithm, compared with the determination of surveillance intervals by a gastroenterologist.Results: The pipeline was developed using 346 reports (224 colonoscopy and 122 pathology) from 224 patients and evaluated on an independent test set of 200 reports (100 colonoscopy and 100 pathology) from 100 patients. We achieved an average PPV, sensitivity, and F-score of .92, .95, and .93, respectively, across targeted entities for colonoscopy. Pathology extraction achieved a PPV, sensitivity, and F-score of .95, .97, and .96. The system achieved an overall accuracy of 92% in assigning the recommended interval for surveillance colonoscopy.Conclusions: This study demonstrates the feasibility of using machine learning to automatically extract findings and classify patients to appropriate risk categories and corresponding surveillance intervals. Incorporating this system can facilitate proactive and timely follow-up after screening colonoscopy and enable real-time quality assessment of prevention programs and providers.