Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective

TL;DR

This paper introduces PALAS, a data-driven model that learns the relationship between symptoms and prescriptions for Parkinson's Disease, enabling automatic, personalized prescription recommendations based on observed symptoms.

Contribution

The paper presents a novel learning-based prescription model for PD, utilizing a latent symptom space and an efficient optimization method, supported by a large clinical dataset.

Findings

PALAS outperforms competing methods in prescription accuracy

The model demonstrates clinical potential for personalized PD treatment

Effective in recommending suitable prescriptions based on symptoms

Abstract

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.