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Research Paper: Machine Learning and Rule-based Approaches to Assertion Classification
TLDR
The StAC models that are developed on discharge summaries can be successfully applied to radiology reports and benefit the most from words found in the +/- 4 word window of the target and can outperform ENegEx.
Role of Local Context in Automatic Deidentification of Ungrammatical, Fragmented Text
TLDR
It is shown that one can deidentify medical discharge summaries using support vector machines that rely on a statistical representation of local context, which contributes more to deidentification than dictionaries and hand-tailed heuristics.
Was the Patient Cured? Understanding Semantic Categories and Their Relationships in Patient Records
TLDR
CaRE combines the solutions to de-identification, semantic category recognition, assertion classification, and semantic relationship classification into a single application that facilitates the easy extraction of semantic information from medical text.
Syntactically-Informed Semantic Category Recognizer for Discharge Summaries
TLDR
A statistical semantic category recognizer trained with syntactic and lexical contextual clues, as well as ontological information from UMLS, is presented to identify eight semantic categories in discharge summaries and shows that syntactic context is important for SCR.
Syntactically-informed semantic category recognition in discharge summaries.
TLDR
A statistical semantic category recognizer trained with syntactic and lexical contextual clues, as well as ontological information from UMLS, is presented to identify eight semantic categories in discharge summaries and shows that syntactic context is important for SCR.
Two Approaches to Assertion Classification
TLDR
SNegEx can classify assertions by utilizing the specific syntactic and lexical context of the target, i.e., the word to be classified with an assertion type, in each corpus, and generalizes to both discharge summaries and radiology reports.