Comprehensive temporal information detection from clinical text: Medical events, time, and TLINK identification

Sunghwan Sohn Dr., Kavishwar B. Wagholikar, Dingcheng Li, Siddhartha R. Jonnalagadda, Cui Tao, Ravikumar Komandur Elayavilli, Hongfang Liu

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Background: Temporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge. Objective: To construct automated systems for EVENT/ TIMEX3 extraction and temporal link (TLINK) identification from clinical text. Materials and methods: The i2b2 organizers provided 190 annotated discharge summaries as the training set and 120 discharge summaries as the test set. Our Event system used a conditional random field classifier with a variety of features including lexical information, natural language elements, and medical ontology. The TIMEX3 system employed a rule-based method using regular expression pattern match and systematic reasoning to determine normalized values. The TLINK system employed both rule-based reasoning and machine learning. All three systems were built in an Apache Unstructured Information Management Architecture framework. Results: Our TIMEX3 system performed the best (F-measure of 0.900, value accuracy 0.731) among the challenge teams. The Event system produced an F-measure of 0.870, and the TLINK system an F-measure of 0.537. Conclusions: Our TIMEX3 system demonstrated good capability of regular expression rules to extract and normalize time information. Event and TLINK machine learning systems required well-defined feature sets to perform well. We could also leverage expert knowledge as part of the machine learning features to further improve TLINK identification performance.

Original languageEnglish (US)
Pages (from-to)836-842
Number of pages7
JournalJournal of the American Medical Informatics Association
Issue number5
StatePublished - 2013

ASJC Scopus subject areas

  • Health Informatics


Dive into the research topics of 'Comprehensive temporal information detection from clinical text: Medical events, time, and TLINK identification'. Together they form a unique fingerprint.

Cite this