ABSTRACT
Advances in image quality produced by computed tomography (CT) and the growth in the number of image studies currently performed has made the management of incidental pulmonary nodules (IPNs) a challenging task. This research aims to identify IPNs in radiology reports of chest and abdominal CT by Natural Language Processing techiniques to recognize IPN in sentences of radiology reports. Our preliminary analysis indicates vastly different pulmonary incidental findings rates for two different patient groups.
Subject(s)
Decision Support Systems, Clinical/organization & administration , Diagnosis, Computer-Assisted/methods , Machine Learning , Natural Language Processing , Radiography, Abdominal/statistics & numerical data , Radiology Information Systems/supply & distribution , Data Mining/methods , Humans , Illinois/epidemiology , Incidental Findings , Pilot Projects , Radiography, Abdominal/classification , Radiology Information Systems/classification , Reproducibility of Results , Sensitivity and Specificity , Terminology as Topic , Vocabulary, ControlledABSTRACT
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.