KMCI employs part-of-speech information to develop a shallow sentence parse, and performs variant generation and normalization using the SPECIALIST Lexicon and related tools.
MEDI (MEDication Indication) is an ensemble medication indication resource for primary and secondary uses of electronic medical record (EMR) data. MEDI was created based on multiple commonly used medication resources (RxNorm, MedlinePlus, SIDER 2, and Wikipedia ) and by leveraging both ontology and natural language processing (NLP) techniques.
The system was developed and tested using a perioperative management system using a training set of 24,599 entries and a test set of 24,857 entries from Vanderbilt University. Our goal was to develop a high performance, easily-maintained algorithm to identify medication and food allergies and sensitivities from unstructured allergy entries in electronic medical record (EHR) systems.
Clinical notes are often divided into sections, or segments, such as "history of present illness" or "past medical history." These sections often have subsections as well, such as the "cardiovascular exam" section of the "physical exam." One can gain greater understanding of clinical notes by recognition of the section in which a concept lives. For instance, both a "past medical history" and the "family medical history" sections can contain a list of diseases, but the context decribes very different import to the patient about whom the note was written.