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. Section tagging is an important early step in natural language processing applications applied to clinical notes.
To improve recognition of section headers, we have developed SecTag. SecTag recognizes note section headers using NLP, Bayesian, spelling correction, and scoring techniques. The algorithm can auto-train through multiple iterations on a single corpus.
To improve recognition of section headers, we have developed:
| SecTag | Application to recognition clinical note section headers. It is Perl-based module that applies normalization, spelling correction, and Naive-Bayesian scoring to label and predict sections. It outputs HL7 Clinical Document Architecture (CDA) XML-documents. |
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| SecTag section header terminology | This terminology is freely available in SQL or CSV format below. |
| Attachment | Size |
|---|---|
| Description of SecTag Terminology.rtf | 11.12 KB |
| SecTag_Terminology.sql.txt (Save without the .txt extension) | 424 KB |
| SecTag concepts.csv | 835.62 KB |
| concepts_rel.csv | 29.83 KB |