JMIR Publications

ABSTRACT

Background:

Family history information is important to assess the risk of inherited medical conditions. Natural language processing has the potential to extract this information from unstructured free-text notes to improve patient care and decision-making. We describe the end-to-end information extraction system the Medical University of South Carolina team developed when participating in the 2019 n2c2/OHNLP shared task.

Objective:

This task involves identifying mentions of family members and observations in electronic health record text notes, and recognizing the two types of relations (family member-living status relations and family member-observation relations). Our system aims to achieve a high level of performance by integrating heuristics and advanced information extraction methods. Our efforts also include improving the performance of two subtasks by exploiting additional labeled data and clinical text-based embedding models.

Methods:

We present a hybrid method that combines machine learning and rule-based approaches. We implemented an end-to-end system with multiple information extraction and attribute classification components. For entity identification, we trained bidirectional long short-term memory deep learning models. These models incorporated static word embeddings and context-dependent embeddings. We created a voting ensemble that combined the predictions of all individual models. For relation extraction, we trained two relation extraction models. The first model determined the living status of each family member. The second model identified observations associated with each family member. We implemented online gradient descent models to extract related entity pairs. As part of post-challenge efforts, we used the BioCreative/OHNLP 2018 corpus and trained new models with the union of these two data sets. We also pre-trained language models using clinical notes from the MIMIC-III clinical database.

Results:

The voting ensemble achieved better performance than individual classifiers. In the entity identification task, our top performing system reached a precision of 78.90% and a recall of 83.84%. Our NLP system for entity identification took 3rd place out of 17 teams in the challenge. We ranked 4th out of 9 teams in the relation extraction task. Our system substantially benefited from the combination of the two data sets. Compared to our official submission with F1-scores of 81.30% and 64.94% for entity identification and relation extraction, respectively, the revised system yielded significantly better performance (p < 0.05) with F1-scores of 86.02% and 72.48%.

Conclusions:

We demonstrated that a hybrid model could be used to successfully extract family history information recorded in unstructured free-text notes. In this study, our approach to entity identification as a sequence labeling problem produced satisfactory results. Our post-challenge efforts significantly improved performance by leveraging additional labeled data and using word vector representations learned from large collections of clinical notes.