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.

翻译：疾病预测在现代医疗中具有重要意义，因其在促进早期干预和实施有效预防措施方面发挥着关键作用。然而，近期大多数疾病预测方法严重依赖实验室检测结果（如血液检测和X光等医学影像）。从患者角度而言，获取此类数据以实现精准疾病预测往往是一项复杂任务，且通常仅在患者就诊后才能获得。为使疾病预测能够从患者端实现，我们提出了个性化医学疾病预测（PoMP）方法，该方法利用患者健康叙事（包括文本描述和人口统计信息）来预测疾病。通过应用PoMP，患者能够更清晰地了解自身状况，从而直接寻求合适的医学专家，减少在医疗沟通中寻找合适医生所需的时间。我们利用好大夫在线的真实数据进行了大量实验，以验证PoMP的有效性。