Ongoing
Timely diagnosis of heart failure is essential as adequate treatment alters the course of the disease, and thereby improving outcomes and quality of life. This project aims to develop a detection support tool based on artificial intelligence.
Dyspnea is a common reason for contacting primary care and the underlying etiologies range from trivial causes to serious conditions, such as heart failure. Timely diagnosis of heart failure is essential as adequate treatment alters the course of the disease, and thereby improving outcomes and quality of life.
Unfortunately, heart failure is often diagnosed in a more advanced stage. We propose that early identification can be optimized with the help of computers. We aim to construct a heart failure model based on machine learning techniques using primary care data obtained from electronic consultation notes as routinely collected in the Amsterdam primary care network.
Contact: Ralf Harskamp: r.e.harskamp@amsterdamumc.nl
