标记数据有限的病理学报告中肿瘤属性分类的丰富注释
Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data
摘要：
精准医疗有可能彻底改变医疗保健，但患者的大部分数据都被锁定在非结构化的自由文本中，限制了有效的个性化治疗的研究和交付。由于高质量注释所需的高水平专业知识，从临床笔记中生成用于信息抽取的大型注释数据集通常具有挑战性且昂贵。为了使小数据集的自然语言处理，我们开发了一种新的丰富的层次注释方案和算法，监督行注意力（SLA），并应用该算法预测分类肿瘤属性从肾脏和结肠癌病理报告加州大学旧金山分校（UCSF）。虽然以前的工作只注释文档级别的标签，我们还要求注释者丰富传统的标签，要求他们也突出显示相关的行或潜在的行的最终标签，这导致每个文档所需的注释时间增加了20%。通过丰富的注释，我们开发了一种简单且可解释的机器学习算法，该算法首先预测文档中的相关行，然后预测肿瘤属性。我们的研究结果表明，在每个癌症32，64，128和186个标记文档的小数据集大小中，SLA只需要一半的标记文档数量作为最先进的方法，就可以在我们进行的绝大多数比较中获得类似或更好的微观f1和宏观f1分数。考虑到增加的注释时间，这导致总注释时间比现有技术减少40%。
英文摘要：
Precision medicine has the potential to revolutionize healthcare, but much of the data for patients is locked away in unstructured free-text, limiting research and delivery of effective personalized treatments. Generating large annotated datasets for information extraction from clinical notes is often challenging and expensive due to the high level of expertise needed for high quality annotations. To enable natural language processing for small dataset sizes, we develop a novel enriched hierarchical annotation scheme and algorithm, Supervised Line Attention (SLA), and apply this algorithm to predicting categorical tumor attributes from kidney and colon cancer pathology reports from the University of California San Francisco (UCSF). Whereas previous work only annotated document level labels, we in addition ask the annotators to enrich the traditional label by asking them to also highlight the relevant line or potentially lines for the final label, which leads to a 20% increase of annotation time required per document. With the enriched annotations, we develop a simple and interpretable machine learning algorithm that first predicts the relevant lines in the document and then predicts the tumor attribute. Our results show across the small dataset sizes of 32, 64, 128, and 186 labeled documents per cancer, SLA only requires half the number of labeled documents as state-of-the-art methods to achieve similar or better micro-f1 and macro-f1 scores for the vast majority of comparisons that we made. Accounting for the increased annotation time, this leads to a 40% reduction in total annotation time over the state of the art.
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