Follow-up Recommendation Detection on Radiology Reports with Incidental Pulmonary Nodules.
Journal:
Studies in health technology and informatics
Published Date:
Jan 1, 2015
Abstract
The management of follow-up recommendations is fundamental for the appropriate care of patients with incidental pulmonary findings. The lack of communication of these important findings can result in important actionable information being lost in healthcare provider electronic documents. This study aims to analyze follow-up recommendations in radiology reports containing pulmonary incidental findings by using Natural Language Processing and Regular Expressions. Our evaluation highlights the different follow-up recommendation rates for oncology and non-oncology patient cohorts. The results reveal the need for a context-sensitive approach to tracking different patient cohorts in an enterprise-wide assessment.
Authors
Keywords
Data Mining
Decision Support Systems, Clinical
Diagnosis, Computer-Assisted
Humans
Illinois
Incidental Findings
Machine Learning
Natural Language Processing
Pilot Projects
Radiography, Abdominal
Radiology Information Systems
Referral and Consultation
Reproducibility of Results
Sensitivity and Specificity
Terminology as Topic
Vocabulary, Controlled