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Clinical TempEval 2016 evaluated temporal information extraction systems on the clinical domain. Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were trained and evaluated on a corpus of clinical and pathology notes from the Mayo Clinic, annotated with an extension of TimeML for the clinical domain. 14 teams submitted a total of 40 system runs, with the best systems achieving near-human performance on identifying events and times. On identifying temporal relations, there was a gap between the best systems and human performance, but the gap was less than half the gap of Clinical TempEval 2015.
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Bethard et al. (2016) studied this question.