ABSTRACT
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.
ABSTRACT
The short forms of medical concepts or expressions (i.e., acronyms/abbreviations) are prevalent in clinical documentation. Given the limited number of potential short forms, they are also highly ambiguous. Resolving the ambiguity of short forms is essential in clinical natural language processing (NLP). However, one prerequisite for resolving ambiguity of short forms is to have a sense inventory. This paper outlines our process of identifying 141 potential short forms with randomly sampled phrases from a large clinical corpus. We assessed various features in their ability to disambiguate medical and non-medical usages. We identified 68% of our short forms as primarily serving medical usages, whereas 12% had non-medical usages. The remaining 19% showed alternating usage based upon case form. Our short forms had an average of 3.58 senses. Usages could be distinguished using basic trigram/bigram/line information. Our initial findings will be applicable for automatic usage/sense resolution.
Subject(s)
Abbreviations as Topic , Medical Records , Narrative Medicine , Humans , Natural Language Processing , Unified Medical Language SystemABSTRACT
In the era of precision medicine, accurately identifying familial conditions is crucial for providing target treatment. However, it is challenging to identify familial conditions without detailed family history information. In this work, we studied the documentation of family history of premature cardiovascular disease and hypercholesterolemia. The information on patients' family history of stroke within the Patient-provided information (PPI) forms was compared with the information gathered by clinicians in clinical notes. The agreement between PPI and clinical notes on absence of family history information in PPI was substantially higher compared to presence of family history.