Enabling Patient-side Disease Prediction via the Integration of Patient Narratives

TL;DR

This paper introduces PoMP, a novel approach enabling patients to predict diseases using personal health narratives and demographic data, thus facilitating early intervention and reducing healthcare navigation time.

Contribution

The paper presents a new patient-side disease prediction method that leverages health narratives, addressing the challenge of limited access to traditional diagnostic data.

Findings

PoMP effectively predicts diseases from patient narratives.

Patients gain better understanding and can seek timely medical help.

Reduces time and effort in healthcare navigation.

Abstract

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.