RATIONALE AND OBJECTIVE: We sought to develop a Bayesian-filter that could distinguish positive radiology computed tomography (CT) reports of appendicitis from negative reports with no appendicitis. MATERIALS AND METHODS: Standard unstructured electronic text radiology reports containing the key word appendicitis were obtained using a Java-based text search engine from a hospital General Electric PACS system. A total of 500 randomly selected reports from multiple radiologists were then manually categorized and merged into two separate text files: 250 positive reports and 250 negative findings of appendicitis. The two text files were then processed by the freely available UNIX-based software dbacl 1.9, a digramic Bayesian classifier for text recognition, on a Linux based Pentium 4 system. The software was then trained on the two separate merged text files categories of positive and negative appendicitis. The ability of the Bayesian filter to discriminate between reports of negative and positive appendicitis images was then tested on 100 randomly selected reports of appendicitis: 50 positive cases and 50 negative cases. RESULTS: The training time for the Bayesian filter was approximately 2 seconds. The Bayesian filter subsequently was able to categorize 50 of 50 positive reports of appendicitis and 50 of 50 reports of negative appendicitis, in less than 10 seconds. CONCLUSION: A Bayesian-filter system can be used to quickly categorize radiology report findings and automatically determine after training, with a high degree of accuracy, whether the reports have text findings of a specific diagnosis. The Bayesian filter can potentially be applied to any type of radiologic report finding and any relevant category.
RATIONALE AND OBJECTIVE: We sought to develop a Bayesian-filter that could distinguish positive radiology computed tomography (CT) reports of appendicitis from negative reports with no appendicitis. MATERIALS AND METHODS: Standard unstructured electronic text radiology reports containing the key word appendicitis were obtained using a Java-based text search engine from a hospital General Electric PACS system. A total of 500 randomly selected reports from multiple radiologists were then manually categorized and merged into two separate text files: 250 positive reports and 250 negative findings of appendicitis. The two text files were then processed by the freely available UNIX-based software dbacl 1.9, a digramic Bayesian classifier for text recognition, on a Linux based Pentium 4 system. The software was then trained on the two separate merged text files categories of positive and negative appendicitis. The ability of the Bayesian filter to discriminate between reports of negative and positive appendicitis images was then tested on 100 randomly selected reports of appendicitis: 50 positive cases and 50 negative cases. RESULTS: The training time for the Bayesian filter was approximately 2 seconds. The Bayesian filter subsequently was able to categorize 50 of 50 positive reports of appendicitis and 50 of 50 reports of negative appendicitis, in less than 10 seconds. CONCLUSION: A Bayesian-filter system can be used to quickly categorize radiology report findings and automatically determine after training, with a high degree of accuracy, whether the reports have text findings of a specific diagnosis. The Bayesian filter can potentially be applied to any type of radiologic report finding and any relevant category.