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Adapting and evaluating a deep learning language model for clinical why-question answering.

Andrew Wen¹, Mohamed Y Elwazir^2,3, Sungrim Moon¹

¹Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.

|July 2, 2020

Summary

This study adapted a deep learning model for clinical why-question answering, achieving moderate accuracy. While not performing deep reasoning, it shows promise for clinical information extraction.

Keywords:

artificial intelligence clinical decision-making evaluation studies natural language processing question answering

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Area of Science:

Natural Language Processing
Clinical Informatics
Artificial Intelligence

Background:

Patient-specific clinical text contains valuable information.
Answering "why" questions in clinical notes is challenging.
Automated methods are needed for efficient clinical information extraction.

Purpose of the Study:

To adapt and evaluate a deep learning language model for clinical "why"-question answering.
To assess the performance of Bidirectional Encoder Representations from Transformers (BERT) models on clinical notes.
To analyze errors and identify areas for improvement in clinical why-question answering.

Main Methods:

Trained BERT models using various data sources for SQuAD 2.0 style why-question answering (why-QA).
Evaluated models based on accuracy and partial match metrics.
Conducted error analysis to understand model limitations.

Main Results:

The best performing model achieved an accuracy of 0.707 (0.760 by partial match).
Customizing training data for clinical language improved accuracy by 6%.
Error analysis indicated a lack of deep reasoning capabilities.

Conclusions:

BERT models demonstrate moderate accuracy for clinical why-QA.
Clinical why-QA may require more advanced solutions.
The model can serve as a useful tool for question-driven clinical information extraction.