About HECTA
Our Healthcare Text Analytics (HECTA) team is focused on developing techniques for large-scale extraction and management of un- and semi-structured health textual resources, and on health-related information synthesis. We currently run a number of projects to extract various structured data from unstructured clinical narratives and electronic healthcare records (EHRs), see bellow. Another strand in healthcare text mining is the extraction of subjective information from patient-generated data, such as tweets, blogs or patient's narratives. Specific topics include:
- Synthesis of information from unstructured electronic health-care records, patient narratives and literature to support clinical decision support
- Clinical documentation, text mining and terminology management
- Integration and analytics of health-care linked-data
- Sentiment mining of health-related social media
- Temporal text mining
Our Projects 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.
View our projects