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Identification of Semantically Similar Sentences in Clinical Notes: Iterative Intermediate Training Using Multi-Task

Diwakar Mahajan¹, Ananya Poddar¹, Jennifer J Liang¹

¹IBM Research, Yorktown Heights, NY, United States.

JMIR Medical Informatics

|November 27, 2020

Summary

This study developed an iterative training approach for clinical text similarity, achieving state-of-the-art results in the 2019 ClinicalSTS challenge by effectively leveraging labeled data and advanced language models.

Keywords:

deep learning electronic health records multi-task learning natural language processing semantic textual similarity transfer learning

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Area of Science:

Natural Language Processing
Clinical Informatics
Machine Learning

Background:

Electronic health records (EHRs) contain redundant information due to templated notes and copy-pasting.
Effective utilization of EHR data is hindered by information redundancy.
Measuring semantic similarity in clinical text is crucial for information condensation.

Purpose of the Study:

To enhance semantic textual similarity performance in the clinical domain.
To improve the robustness of clinical text similarity models.
To leverage manually labeled data and contextualized embeddings for better results.

Main Methods:

Utilized the ClinicalSTS dataset of 1642 annotated clinical text pairs.
Developed an iterative intermediate training approach using multi-task learning (IIT-MTL).
Applied IIT-MTL to ClinicalBERT and ensembled with BioBERT, MT-DNN, RoBERTa, and handcrafted features.

Main Results:

Achieved state-of-the-art performance in the 2019 n2c2/OHNLP ClinicalSTS challenge.
Ranked first among 87 submitted systems with a Pearson correlation coefficient of 0.9010.
The winning system was an ensemble model incorporating IIT-MTL on ClinicalBERT, BioBERT, MT-DNN, and medication features.

Conclusions:

IIT-MTL effectively utilizes annotated data from related tasks for improved performance on data-limited target tasks.
This approach offers new possibilities for optimized dataset selection in the clinical domain.
The study contributes to generating more robust and universal contextual text representations.