  Disease prediction holds considerable significance in modern healthcare,
because of its crucial role in facilitating early intervention and implementing
effective prevention measures. However, most recent disease prediction
approaches heavily rely on laboratory test outcomes (e.g., blood tests and
medical imaging from X-rays). Gaining access to such data for precise disease
prediction is often a complex task from the standpoint of a patient and is
always only available post-patient consultation. To make disease prediction
available from patient-side, we propose Personalized Medical Disease Prediction
(PoMP), which predicts diseases using patient health narratives including
textual descriptions and demographic information. By applying PoMP, patients
can gain a clearer comprehension of their conditions, empowering them to
directly seek appropriate medical specialists and thereby reducing the time
spent navigating healthcare communication to locate suitable doctors. We
conducted extensive experiments using real-world data from Haodf to showcase
the effectiveness of PoMP.
