Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction

Sankaran Narayanan, Kaivalya Mannam, Sreeranga P Rajan, P Venkat Rangan


Abstract
We evaluate several biomedical contextual embeddings (based on BERT, ELMo, and Flair) for the detection of medication entities such as Drugs and Adverse Drug Events (ADE) from Electronic Health Records (EHR) using the 2018 ADE and Medication Extraction (Track 2) n2c2 data-set. We identify best practices for transfer learning, such as language-model fine-tuning and scalar mix. Our transfer learning models achieve strong performance in the overall task (F1=92.91%) as well as in ADE identification (F1=53.08%). Flair-based embeddings out-perform in the identification of context-dependent entities such as ADE. BERT-based embeddings out-perform in recognizing clinical terminology such as Drug and Form entities. ELMo-based embeddings deliver competitive performance in all entities. We develop a sentence-augmentation method for enhanced ADE identification benefiting BERT-based and ELMo-based models by up to 3.13% in F1 gains. Finally, we show that a simple ensemble of these models out-paces most current methods in ADE extraction (F1=55.77%).
Anthology ID:
2020.clinicalnlp-1.6
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
55–64
Language:
URL:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.6
DOI:
10.18653/v1/2020.clinicalnlp-1.6
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.clinicalnlp-1.6.pdf