Clinical questionnaire filling based on question answering framework

Abstract

Background: Electronic Health Records (EHR) are the foundation of much medical research. However, analyzing the text data of EHRs directly is an challenging task. Therefore, physicians often use questionnaires to first convert text data to structured data. Filling in these questionnaires requires a considerable amount of time and medical knowledge. It is a significant task to develop an algorithm to make computers fill out these questionnaires automatically.

Objective: This research aims to build a deep learning model that can automatically complete questionnaires with given medical text.

Methods: This task is a part of Information Extraction (IE), but it differs from the existing tasks of medical IE. Because of the questions in questionnaires are closed-end type, which refers to making a selection among given options, we could treat this task as a classification problem. However, conventional classification algorithms are resource-consuming when filling out one entire questionnaire with one model. They also could not use the question information to guide the questionnaire filling task. To handle these issues, we propose a neural network model based on question answering (QA) framework in this paper. With this framework, our neural network can fill out one complete questionnaire using only one model.

Results: We perform experiments on three real-world Chinese medical datasets and related clinical questionnaires. Our model respectively achieves F1 scores of 0.9273, 0.8834, and 0.9846. The results outperform several baseline models.

Conclusion: The strong performance of our QA model will allow us to build a system which can help physicians to fill out questionnaires and convert text data to structured data. This system can reduce the workload of physicians.

Keywords: EHR; Information extraction; Medical text; Multi-label classification; Question answering.