Timmerman, J. (2014) Text classification in dictated radiology reports using a machine learning algorithm. Master's Thesis / Essay, Computing Science.
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Abstract
The main goal of this study was to build a successful machine learning system for classifying texts in dictated, free-text radiology reports for use in a re-structured, standardized radiology report. At the start of the study we conducted interviews with referring clinicians to determine the ideal structure for radiology reports on the malignant lymphoma. Based on these interviews two report templates were developed, and the information in free-text radiology reports, written in Dutch, was annotated. A computational system that uses a machine learning technique specifically designed for learning sequences, called a Linear Chain Conditional Random Fields machine learner, was trained on classifying information in the annotated free-text reports. A post processing step was added to the system to correct specific tokens that were misclassified by the machine learner. The classified texts in the free-text reports were automatically re-structured into the developed templates to form standardized, structured reports. A group of five clinicians took part in a user study to evaluate the re-structured reports. The post processing step increased the system's F-score from 88.18 (micro averaged) / 87.85 (macro averaged) to 89.30 (micro averaged) / 88.60 (macro averaged). Results from the user evaluation study suggest that standardizing and improving the global structure of the radiology report increases the clinicians' impressions on clarity and organization of elements, while also decreasing impressions on report complexity. Our study shows that a computational system that uses a machine learning approach can be used to re-structure and standardize the information contained in free-text radiology reports and that the resulting restructured reports are superior over conventional free-text reports.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 08:02 |
| Last Modified: | 15 Feb 2018 08:02 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/12392 |
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