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

• URL: http://arxiv.org/abs/2405.02935v1
• Date: Sun, 5 May 2024 13:54:02 GMT
• Title: Enabling Patient-side Disease Prediction via the Integration of Patient Narratives
• Authors: Zhixiang Su, Yinan Zhang, Jiazheng Jing, Jie Xiao, Zhiqi Shen,
• Abstract summary: We propose Personalized Medical Disease Prediction (PoMP) to make disease prediction available from patient-side. PoMP predicts diseases using patient health narratives including textual descriptions and demographic information. We conducted extensive experiments using real-world data from Haodf to showcase the effectiveness of PoMP.
• Score: 9.14970943544095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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