Automatically extracted family history, symptoms, diseases, clinical procedures, medications, allergies and lab results from electronic health records can provide valuable data and insights to clinical practice. The data and insights can also be used to predict complex drug interactions in individual patients.
Putting together and relating information extracted from clinical diaries, reports from diagnostic imaging procedures, lab results, discharge notes, etc. provides unique insights that can be delivered to the care team or help improve the lives of a patient group.
Innovative visualizations can bring sense into the avalanche of data extracted from electronic health records. A patient timeline together with highlights for the most important allergies, history, chronic diseases or a graph depicting the links between patients in a clinical trial helps solving today’s health challenges.
The information that can be extracted from millions of health records can help researchers profile patient groups or develop new treatments. Priberam’s text analytics and cognitive search engines provide an advanced information retrieval platform for health data.
This project will take advantage of the massive analysis of non-structured information that currently is not processed to improve the diagnosis and treatment of the patients.