Dr. Right!: Embedding-Based Adaptively-Weighted Mixture Multi-Classification Model for Finding Right Doctors with Healthcare Experience Data


Finding a right doctor with suitable expertise that meets one's health needs is important yet challenging. In this paper, we study the problem of finding high-rated doctors for a specific disease using imbalanced and heterogeneous healthcare experience rating data. We develop a data analytical framework, namely Dr. Right!, which incorporates the so-called network-textual embeddings, together with data-imbalance-aware mixture multi-classification models to rate doctors per specific disease. First, Dr. Right! collects the comments and rating records from patients for doctors on specific diseases from an online hospital and constructs a doctor-patient-disease network, where every edge weight is a pairwise average rating (experience score) among doctors, patients, and diseases. Then, Dr. Right! learns the embeddings of patient experiences from textual comments using the Word2Vec, as well as the embeddings of doctors and diseases from the doctor-patient-disease network via the Node2Vec. The two types of embeddings are fused to represent a doctor-patient pair. With the embedding representations of doctor-patient pairs, Dr. Right! learns an adaptively-weighted mixture multi-classification model to map a doctor-disease pair to an experience rating score, while addressing the challenges of data imbalance and group heterogeneity. Finally, extensive experimental results demonstrate the enhanced performances of Dr. Right! for predicting the disease-specific experience scores of doctors.

Meeting Name

2018 IEEE International Conference on Data Mining, ICDM 2018 (2018: Nov. 17-18, Singapore, Singapore)


Computer Science

Research Center/Lab(s)

Intelligent Systems Center


This research was supported by the National Science Foundation of China (NSFC) via the grant numbers: 61773199, 71732002, as well as the Fundamental Research Funds for the Central Universities.

Keywords and Phrases

Embeddings; Health care; Medicine; Mixtures; Adaptively weighted learning; Data imbalance; Doctor disease pair; Edge weights; Experience data; Feature embedding; Multi-classification; Patient experiences; Data mining; Disease specific; Healthcare; Mixture multi classicifation

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)

1550-4786; 2374-8486

Document Type

Article - Conference proceedings

Document Version


File Type





© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Nov 2018