Evaluating Methods for Identifying Cancer in Free-Text Pathology Reports Using Various Machine Learning and Data Preprocessing Approaches.
Automated detection methods can address delays and incompleteness in cancer case reporting. Existing automated efforts are largely dependent on complex dictionaries and coded data. Using a gold standard of manually reviewed pathology reports, we evaluated the performance of alternative input formats and decision models on a convenience sample of free-text pathology reports. Results showed that the input format significantly impacted performance, and specific algorithms yielded better results for presicion, recall and accuracy. We conclude that our approach is sufficiently accurate for practical purposes and represents a generalized process.
Indiana University School of Informatics and Computing, Indianapolis, IN, USA.
Suranga Nath
SN
Kasthurirathne
Regenstrief Institute, Indianapolis, IN, USA.
Brian E
BE
Dixon
Regenstrief Institute, Indianapolis, IN, USA.
Shaun J
SJ
Grannis
D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D057286:Electronic Health Records / Q000145:classification*; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing; D009369:Neoplasms / Q000175:diagnosis* / Q000473:pathology; D010336:Pathology / Q000145:classification*