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.
Abstract (translated)
疾病预测在现代医疗保健中具有重要的意义，因为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，最最新的疾病预测方法往往高度依赖实验室检查结果（例如血液测试和医学影像X光片）。从患者的角度来看，获取这样的数据进行精确的疾病预测通常是一个复杂的任务，并且只有在患者就诊后才能提供。为了从患者侧使疾病预测可用，我们提出了Personalized Medical Disease Prediction（PoMP）方法，该方法通过患者健康状况的文本描述和人口学信息预测疾病。通过应用PoMP，患者可以更清晰地了解他们的疾病情况，从而直接寻求适当的医疗专家，从而减少在寻找合适医生过程中花费的时间，使医疗沟通更加便捷。我们使用来自Haoodf的现实生活中数据进行了广泛的实验，以展示PoMP的有效性。
URL
https://arxiv.org/abs/2405.02935
