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An interpretable natural language processing system for written medical examination assessment
Abeed Sarker1, Ari Z Klein1, Janet Mee2
1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Journal of Biomedical Informatics
|August 18, 2019
Summary
A new natural language processing (NLP) system, Intelligent Clinical Text Evaluator (INCITE), automates medical exam grading by identifying key concepts in responses. This NLP tool significantly improves assessment efficiency and consistency.
Area of Science:
- Medical Education
- Natural Language Processing
- Computational Linguistics
Background:
- Assessing written medical examinations is time-consuming and costly for medical experts.
- Automating this process can improve efficiency and reduce inconsistencies.
Purpose of the Study:
- To develop a natural language processing (NLP) system to expedite the assessment of unstructured answers in medical examinations.
- The system aims to automatically identify relevant concepts in examinee responses.
Main Methods:
- A semi-supervised NLP system, Intelligent Clinical Text Evaluator (INCITE), was developed.
- It uses customized text comparison and similarity functions, including fuzzy matching and set intersection.
- Dynamic, customizable similarity thresholds tailor the system for different response lengths.
Main Results:
- INCITE achieved an average F1-score of 0.89 against human annotations.
- Key performance improvements were driven by fuzzy text matching, dynamic thresholding, and supervised learning.
- The system demonstrated high precision (0.87) and recall (0.91).
Conclusions:
- The INCITE system shows high performance and interpretability, aiding medical exam assessment.
- It helps mitigate inconsistencies inherent in manual grading.
- Even small amounts of annotated data significantly enhance system performance.
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