Correlating Lab Test Results in Clinical Notes with Structured Lab Data: A Case Study in HbA1c and Glucose

It is widely acknowledged that information extraction of unstructured clinical notes using natural language processing (NLP) and text mining is essential for secondary use of clinical data for clinical research and practice. Lab test results are currently structured in most of the electronic health record (EHR) systems. However, for referral patients or lab tests that can be done in non-clinical setting, the results can be captured in unstructured clinical notes. In this study, we proposed a rule-based information extraction system to extract the lab test results with temporal information from clinical notes. The lab test results of glucose and HbA1c from 104 randomly sampled diabetes patients selected from 1996 to 2015 are extracted and further correlated with structured lab test information in the Mayo Clinic EHRs. The system has high F1-scores of 0.964, 0.967 and 0.966 in glucose, HbA1c and overall extraction, respectively.