Use of Radcube for Extraction of Finding Trends in a Large Radiology Practice

Abstract

The purpose of our study was to demonstrate the use of Natural Language Processing (Leximer), along with Online Analytic Processing, (NLP-OLAP), for extraction of finding trends in a large radiology practice. Prior studies have validated the Natural Language Processing (NLP) program, Leximer for classifying unstructured radiology reports based on the presence of positive radiology findings (F (POS)) and negative radiology findings (F (NEG)). The F (POS) included new relevant radiology findings and any change in status from prior imaging. Electronic radiology reports from 1995-2002 and data from analysis of these reports with NLP-Leximer were saved in a data warehouse and exported to a multidimensional structure called the Radcube. Various relational queries on the data in the Radcube were performed using OLAP technique. Thus, NLP-OLAP was applied to determine trends of F (POS) in different radiology exams for different patient and examination attributes. Pivot tables were exported from NLP-OLAP interface to Microsoft Excel for statistical analysis. Radcube allowed rapid and comprehensive analysis of F (POS) and F (NEG) trends in a large radiology report database. Trends of F (POS) were extracted for different patient attributes such as age groups, gender, clinical indications, diseases with ICD codes, patient types (inpatient, ambulatory), imaging characteristics such as imaging modalities, referring physicians, radiology subspecialties, and body regions. Data analysis showed substantial differences between F (POS) rates for different imaging modalities ranging from 23.1% (mammography, 49,163/212,906) to 85.8% (nuclear medicine, 93,852/109,374; p < 0.0001). In conclusion, NLP-OLAP can help in analysis of yield of different radiology exams from a large radiology report database.

Recommendations for Additional Imaging in Radiology Reports: Multifactorial Analysis of 5.9 Million Examinations

“…In our entire sample, there were 307 300 abdominal CT scans, of which 49 145 (16%) had at least one RAI. One reason for our lower rate could be that the automated NLP method for detecting RAI may be somewhat insensitive compared with manual abstraction, though our method studies have consistently yielded sensitivities well above 90% (9)(10)(11)(12)(13)(14). However, if our NLP method had a systematic and consistent false-negative rate for RAI, it would not affect odds ratio estimates, and our inferences based on them would hold true.…”

“…We specifically excluded generic statements such as "clinical correlation," as well as those calling for correlation with surgery, biopsy, or endoscopy and/or colonoscopy. The same NLP system also produces an output for each report that codes any clinically important findings and returns "negative" when none are detected in the text (9,10,14). This was used to create a "positive findings" variable for subsequent analysis with values of yes and no.…”

“…Our group has previously reported on automatic detection and characterization of recommendations contained in radiology interpretations by using a clinical data warehouse supplemented with natural language processing (NLP) of report texts (9)(10)(11)(12)(13)(14). These articles described and validated the NLP technique for extracting recommendations (9)(10)(11); reported on types, rates, and trends of recommendations (11,12,14); and specifically tested the contribution of examinations induced by recommendations for repeat studies to selected high-cost procedure volumes (13).…”

“…These articles described and validated the NLP technique for extracting recommendations (9)(10)(11); reported on types, rates, and trends of recommendations (11,12,14); and specifically tested the contribution of examinations induced by recommendations for repeat studies to selected high-cost procedure volumes (13). This study complements and extends these efforts by increasing the sample to include all diagnostic imaging reported by our radiologists since 1995, with multivariable logistic regression to adjust for interaction between factors, and evaluates the effect of radiologist experience and patient age on RAI rates over time.…”

Machine learning and radiology

“…Dang et al employed NLP/NLU technologies to find trends in a large radiology practice (Dang et al, 2009). Experimental results showed that NLP/NLU technologies could help to analyze yield of different radiology exams from a large radiology report database based on presence of positive/negative radiology findings.…”