In this paper, we present our approach to extracting structured information
from unstructured Electronic Health Records (EHR) [2] which can be used to, for
example, study adverse drug reactions in patients due to chemicals in their
products. Our solution uses a combination of Natural Language Processing (NLP)
techniques and a web-based annotation tool to optimize the performance of a
custom Named Entity Recognition (NER) [1] model trained on a limited amount of
EHR training data. This work was presented at the first Health Search and Data
Mining Workshop (HSDM 2020) [26]. We showcase a combination of tools and
techniques leveraging the recent advancements in NLP aimed at targeting domain
shifts by applying transfer learning and language model pre-training techniques
[3]. We present a comparison of our technique to the current popular approaches
and show the effective increase in performance of the NER model and the
reduction in time to annotate data.A key observation of the results presented
is that the F1 score of model (0.734) trained with our approach with just 50%
of available training data outperforms the F1 score of the blank spaCy model
without language model component (0.704) trained with 100% of the available
training data. We also demonstrate an annotation tool to minimize domain expert
time and the manual effort required to generate such a training dataset.
Further, we plan to release the annotated dataset as well as the pre-trained
model to the community to further research in medical health records.

本文介绍了一种从非结构化电子医疗记录中提取结构化信息的方法，该方法通过自然语言处理技术和网络注释工具的组合应用，优化了使用少量训练数据训练的定制命名实体识别模型的性能，并展示了该技术与现有方法相比的优势。研究结果表明，在仅使用 50% 的训练数据的情况下，我们的方法训练的模型的 F1 得分可达到 0.734，而当前流行的方法训练的不带语言模型组件的 spaCy 模型的 F1 得分为 0.704。