SA-MPF: A Status-Aware Mask Prediction Framework for Online Disease Diagnosis

An increasing number of individuals are turning to online self-diagnosis by matching their symptoms with potential medical conditions. This process involves two primary components: symptom inquiry and disease prediction. Existing works employ two separate modules to learn these tasks individually. Nevertheless, this intuitive approach encounters low data efficiency due to the separate learning of each module. In addition, previous research incorporates symptom statuses solely as part of the input without any additional modeling. However, this oversight neglects the importance of symptom status, which indicates whether the user has experienced the symptom. The status significantly influences both symptom inquiry strategies and disease prediction. To address these challenges, we propose a Status-Aware Mask Prediction Framework for online disease diagnosis, called SA-MPF. SA-MPF formalizes symptom inquiry and disease prediction as a single masked token prediction task, distinguishing them solely through the masked token type. Furthermore, we introduce a masked status prediction task, which unifies the prediction of symptom or disease statuses in a similar manner to masked token prediction, thereby enhancing the modeling of symptom and disease statuses. We evaluate SA-MPF on several datasets collected from various sources. The experimental results demonstrate substantial improvements achieved by SA-MPF. For example, on the GMD-12 dataset, SAMPF demonstrates a noteworthy 5% improvement in diagnostic accuracy, from 82% to 87%.
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