Early detection and effective treatment of cancer relies on many factors. Ensuring your data is good and comprehensive is critical. Well-characterized patient and tumor data enable better treatment selection and outcomes analysis. Unfortunately, much of this data is trapped in unstructured text in pathology and radiology reports, and may only become usable through manual extraction by cancer registrars.
How can unstructured cancer data be used to support early detection and treatment?
Powered by I2E, Linguamatics Health extracts cancer-related data from pathology and radiology reports to be integrated into EHRs, data warehouses, care coordination platforms, and predictive models.
- Extract clinical attributes such as cancer stage, tumor size, histology, and biomarker values
- Quickly identify high complexity cancer cases to better co-ordinate care
- Support real-time monitoring for early signs of cancer such as pulmonary nodules in radiology reports
- Identify key documents and clinical attributes for cancer research to support clinical trial recruitment and big data analytics
- Mine scientific literature for insights into genetic mutation and disease association
To find out more:
Download our cancer insights and NLP whitepaper.
Or read our application note on the power of text mining for precision medicine research.