title: Using the UMLS and Simple Statistical Methods to Semantically Categorize Causes of Death on Death Certificates.
abstract: Cause of death data is an invaluable resource for shaping our understanding of population health. Mortality statistics is one of the principal sources of health information and in many countries the most reliable source of health data. 1 A quick classification process for this data can significantly improve public health efforts. Currently, cause of death data is captured in unstructured form requiring months to process. We think this process can be automated, at least partially, using simple statistical Natural Language Processing, NLP, techniques and the Unified Medical Language System, UMLS, as a vocabulary resource. A system, Medical Match Master, MMM, was built to exercise this theory. We evaluate this simple NLP approach in the classification of causes of death. This technique performed well if we engaged the use of a large biomedical vocabulary and applied certain syntactic maneuvers made possible by textual relationships within the vocabulary.
authors.affiliation: Pathology Informatics Core, UC Davis Dept. of Pathology and Laboratory Medicine, UC Davis School of Medicine, Davis, CA.
authors.forename: Bill
authors.initials: B
authors.lastname: Riedl
authors.forename: Nhan
authors.initials: N
authors.lastname: Than
authors.forename: Michael
authors.initials: M
authors.lastname: Hogarth
mesh_terms: D002423:Cause of Death; D003644:Death Certificates; D006801:Humans; D016247:Information Storage and Retrieval; D009323:Natural Language Processing; D012660:Semantics; D017432:Unified Medical Language System; D018875:Vocabulary, Controlled