In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their content. A lightweight reasoning engine extracts the clinical findings and body locations from MedLEE's semantic representation and correlates them. Our study is illustrative for research in which existing natural language processing software is embedded in a larger system. We manually created a standard reference based on a corpus of neuro and breast radiology reports. The standard reference was used to evaluate the precision and recall of the proposed system and its modules. Our results indicate that the precision of our system is considerably better than its recall (82.32-91.37% vs. 35.67-45.91%). We conducted an error analysis and discuss here the practical usability of the system given its recall and precision performance.

The clinical history and indication (CHI) provided with a radiological examination are critical components of a quality interpretation by the radiologist. A patient's chronic conditions offer the context in which acute symptoms and findings can be interpreted more accurately. Seven pertinent (potentially diagnosis altering) chronic conditions, which are fairly prevalent at our institution, were selected. We analyze if and how in 140 CHIs there was mention of a patient's previously reported chronic condition and if and how the condition was subsequently described in the radiology report using a four-item scheme (Mention/Specialization, Generalization, Common comorbidity, No mention). In 40.7 % of CHIs, the condition was rated Mention/Specialization. Therefore, we reject our first hypothesis that the CHI is a reliable source for obtaining pertinent chronic conditions (≥90.0 %). Nononcological conditions were significantly more likely rated No mention in the CHI than oncological conditions (58.7 versus 8.3 %, P<0.0001). Stat cases were significantly more frequently No mention than non-stat cases (60.0 versus 31.3 %, P=0.0134). We accept our second hypothesis that the condition's rating in the CHI is significantly correlated with its rating of the final radiology report (χ 2 test, P<0.00001). Our study demonstrates an alarming lack of communication of pertinent medical information to the radiologist, which may negatively impact interpretation quality.Presenting automatically aggregated patient information to the radiologist may be a potential avenue for improving interpretation and adding value of the radiology department to the care chain.

The proposed algorithm is a competitive candidate for searching and retrieving terms from a large medical ontology.