End-to-End Joint Entity Extraction and Negation Detection for Clinical Text

Abstract

Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for information extraction. Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER) and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations. We extend a standard hierarchical encoder-decoder NER model and first adopt a shared encoder followed by separate decoders for the two tasks. This architecture performs considerably better than the previous rule-based and machine learning-based systems. To overcome the problem of increased parameter size especially for low-resource settings, we propose the Conditional Softmax Shared Decoder architecture which achieves state-of-art results for NER and negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.

Appendix

1.1 Experiments

Dataset We evaluated our model on two datasets. First is the 2010 i2b2/VA challenge dataset for “test, treatment, problem” (TTP) entity extraction and assertion detection (i2b2 dataset). Unfortunately, only part of this dataset was made public after the challenge, therefore we cannot directly compare with NegEx and ABoW results. We followed the original data split from [1] of 170 notes for training and 256 for testing. The second dataset is proprietary and consists of 4,200 de-identified annotated clinical notes with medical conditions (proprietary dataset). Below is a summary of the datasets (Table 3).

Table 3 Overview of the i2b2 and the proprietary medical condition datasets

Model settings Word, character and tag embeddings are 100, 25, and 50 dimensions, respectively. Word embeddings are initialized using GloVe, while character and tag embeddings are learned. Character and word encoders have 50, and 100 hidden units, respectively, while the decoder LSTM has a hidden size of 50. Dropout is used after every LSTM, as well as for word embedding input. We use Adam as an optimizer. Our model is built using MXNet. Hyperparameters are tuned using Bayesian Optimization [13].

Training details Our models are trained until convergence, and we use the development set for both tasks to evaluate performance for early stopping. We performed two sets of experiments. The first set evaluates the performance of NER and negation assertion of the baseline, two decoder, shared decoder and conditional softmax decoder models on i2b2 and the medical condition datasets. The second set uses low resource settings, where we evaluate the performance of negation assertion of the conditional softmax decoder model on 5, 10 and 20% of the proprietary medical condition training data. Development and test sets are kept at the original size.