Support patient search on pathology reports with interactive online learning based data extraction

• Published in: Journal of pathology informatics 2015-01, Vol.6 (1), p.51-51, Article 51
• Main Authors: Zheng, Shuai, Lu, James J., Appin, Christina, Brat, Daniel, Wang, Fusheng
• Format: Article
• Language: English
• Subjects: Controlled vocabularies; Controlled vocabularies, data extraction, online machine learning, pathology reports, patient search; data extraction; Online education; online machine learning; pathology reports; patient search
• ISSN: 2153-3539; 2229-5089; 2153-3539
• DOI: 10.4103/2153-3539.166012

Background: Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user’s interaction with minimal human effort. Methods: We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system’s data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users’ corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. Results: We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of tests. Conclusions: Extracting data from pathology reports could enable more accurate knowledge to support biomedical research and clinical diagnosis. IDEAL-X provides a bridge that takes advantage of online machine learning based data extraction and the knowledge from human’s feedback. By combining iterative online learning and adaptive controlled vocabularies, IDEAL-X can deliver highly adaptive and accurate data extraction to support patient search.