Publications by authors named "Stephen Tze-Inn Wu"
Using SNOMED-CT to encode summary level data - a corpus analysis.
AMIA Jt Summits Transl Sci Proc
August 2012
Extracting and encoding clinical information captured in free text with standard medical terminologies is vital to enable secondary use of electronic medical records (EMRs) for clinical decision support, improved patient safety, and clinical/translational research. A critical portion of free text is comprised of 'summary level' information in the form of problem lists, diagnoses and reasons of visit. We conducted a systematic analysis of SNOMED-CT in representing the summary level information utilizing a large collection of summary level data in the form of itemized entries.
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- The paper discusses the Mayo Clinic's coreference resolution system, MedCoref, developed for the i2b2/VA/Cincinnati shared task, focusing on linking entities across medical documents.
- It employs a multi-pass sieve algorithm combining deterministic rules and machine learning to enhance accuracy in identifying treatment, problems, tests, people, and pronouns in clinical notes.
- The system achieved strong performance scores of 0.836 and 0.843 for the training and test sets, respectively, demonstrating the efficacy of combining simple rules with advanced techniques in complex data environments.