  In this paper, we study a novel problem: "automatic prescription
recommendation for PD patients." To realize this goal, we first build a dataset
by collecting 1) symptoms of PD patients, and 2) their prescription drug
provided by neurologists. Then, we build a novel computer-aided prescription
model by learning the relation between observed symptoms and prescription drug.
Finally, for the new coming patients, we could recommend (predict) suitable
prescription drug on their observed symptoms by our prescription model. From
the methodology part, our proposed model, namely Prescription viA Learning
lAtent Symptoms (PALAS), could recommend prescription using the multi-modality
representation of the data. In PALAS, a latent symptom space is learned to
better model the relationship between symptoms and prescription drug, as there
is a large semantic gap between them. Moreover, we present an efficient
alternating optimization method for PALAS. We evaluated our method using the
data collected from 136 PD patients at Nanjing Brain Hospital, which can be
regarded as a large dataset in PD research community. The experimental results
demonstrate the effectiveness and clinical potential of our method in this
recommendation task, if compared with other competing methods.
