论文标题
一种生物医学管道，可检测临床和非临床命名实体
A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities
论文作者
论文摘要
与生物医学命名实体识别的任务有关的挑战是：现有方法考虑了较少数量的生物医学实体（例如疾病，症状，蛋白质，基因）；这些方法不考虑健康的社会决定因素（年龄，性别，就业，种族），这是与患者健康有关的非医学因素。我们提出了一条机器学习管道，该管道通过以下方式改善了以前的努力：首先，它认识到许多生物医学实体类型以外的许多生物医学实体类型；其次，它考虑了与患者健康有关的非临床因素。该管道还包括阶段，例如预处理，令牌化，映射嵌入查找和指定的实体识别任务，以从自由文本中提取生物医学实体。我们提供了一个新的数据集，我们通过策划COVID-19案例报告来准备。所提出的方法的表现优于五个基准数据集上的基线方法，其宏观和微平均F1得分约为90，而我们的数据集则分别为95.25和93.18的宏观和微平均F1得分。
There are a few challenges related to the task of biomedical named entity recognition, which are: the existing methods consider a fewer number of biomedical entities (e.g., disease, symptom, proteins, genes); and these methods do not consider the social determinants of health (age, gender, employment, race), which are the non-medical factors related to patients' health. We propose a machine learning pipeline that improves on previous efforts in the following ways: first, it recognizes many biomedical entity types other than the standard ones; second, it considers non-clinical factors related to patient's health. This pipeline also consists of stages, such as preprocessing, tokenization, mapping embedding lookup and named entity recognition task to extract biomedical named entities from the free texts. We present a new dataset that we prepare by curating the COVID-19 case reports. The proposed approach outperforms the baseline methods on five benchmark datasets with macro-and micro-average F1 scores around 90, as well as our dataset with a macro-and micro-average F1 score of 95.25 and 93.18 respectively.
