In previous projects we have developed a combination of rule-based and machine-learning methods to identify diseses that a patient has or does not have (“disease status”), including identification of various co-morbidities, problems, tests and treatments. We also work on the extraction of medication-related information (such as medication name, dosage, reason for taking, frequency, duration etc.) and clinical temporal text mining. These tasks were assessed as part of an international text mining challenge in the clinical/healthcare domain:

• 2012: Temporal mining of clinical narratives (ranked shared 1st for the temporal expression extraction task)
• 2011: Sentiment analysis of suicide notes (ranked 8th/26 teams, invited talk)
• 2009: Medication extraction from clinical notes (ranked shared 2nd-3rd/19 teams, invited talk)
• 2008: Extraction of obesity and co-morbidity status from hospital discharge summaries (ranked 1st for the explicit extraction, invited talk)

For more details, see Natural Language Processing for Clinical Data: Continuous Success at i2b2 Challenges.