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Disease Registries

  1. Challenge of capturing unstructured data for disease registries
  2. How can NLP be used to provide robustness to disease registries?
  3. Use cases

Streamline data abstraction for disease registries with Linguamatics NLP

The challenge of capturing unstructured data for disease registries

Better disease management may be obtained by adding vital information in the form of unstructured data to your disease registries. Ensuring your disease population’s data is good and comprehensive is critical. Well-characterized patient and disease data enables new disease discoveries and better treatment selection. Unfortunately, much of this data is trapped in unstructured text in medical reports such as pathology, imaging, and genetic reports. The data may only become usable through manual extraction by clinical teams.

How can natural language processing (NLP) be used to provide robustness to disease registries?

The NLP solution

The Linguamatics NLP platform extracts disease-related data from electronic health record (EHR) data, and outside reports such as: clinician notes, laboratory reports, pathology imaging, and genetic testing reports. NLP is a great method of supporting disease registrars in an Augmented Intelligence workflow by pre extracting information. This data can then be integrated into disease registries, allowing data-rich registries to be utilized both prospectively for clinical trials and retrospectively for informed decisions on disease interventions utilizing real world data.

Create enriched registries for cancer, diabetes, heart failure patients, chronic obstructive Pulmonary Disease (COPD), various rare diseases, and others.

  • For cancer: extract clinical attributes such as cancer stage, grade, tumor size, histology, lymph node involvement, TNM and biomarker values
  • For diabetes: Body mass index (BMI), laboratory values such as hemoglobin A1c, retinopathies, skin ulcerations, annual screenings, etc.
  • For heart failure patients: ejection fraction (EF), dyspnea, fatigue, edema, exercise intolerance, cough, weight gain, decreased concentration, etc.
  • For COPD: pulmonary function tests dyspnea, wheezing, fatigue, unintended weight loss
  • For overall contributing factors for any disease: BMI, smoking status, alcohol consumption, and behavioral factors
  • Adding social determinants of health to all registries, such as: limited access to proper medications and healthy foods, barriers to physical activity, high stress levels and social isolation.

    Use cases

    Read about how Huntsman Cancer Institute map their results into HCI Clinical Cancer Registry.


    Download our datasheet below to read more about what some of our customers are doing with NLP.

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