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首页> 外文会议>IEEE International Conference on Data Mining >Dr. Right!: Embedding-Based Adaptively-Weighted Mixture Multi-classification Model for Finding Right Doctors with Healthcare Experience Data
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Dr. Right!: Embedding-Based Adaptively-Weighted Mixture Multi-classification Model for Finding Right Doctors with Healthcare Experience Data

机译：Right !：基于嵌入的自适应加权混合多分类模型，可利用医疗保健经验数据查找合适的医生

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摘要

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.
机译：寻找合适的专业技能合适的医生来满足自己的健康需求是重要且具有挑战性的。在本文中，我们研究了使用不平衡且异类的医疗经验等级数据来寻找针对特定疾病的高水平医生的问题。我们开发了一个数据分析框架，即Dr. Right !，该框架结合了所谓的网络文本嵌入以及可识别数据不平衡的混合多分类模型，以对每种特定疾病的医生进行评分。首先，对了！从在线医院收集针对特定疾病的医生给患者的评论和评分记录，并构建一个医患疾病网络，其中每个边缘权重都是医生，患者和疾病之间的成对平均评分（经验得分）。然后，对了！使用Word2Vec从文本注释中学习患者体验的嵌入，以及通过Node2Vec从医生-患者-疾病网络中学习医生和疾病的嵌入。两种类型的嵌入融合在一起，代表了医患对。有了医生-病人对的嵌入表示，Right！医生！通过学习自适应加权混合多分类模型，可以将医生疾病对映射到经验评分，同时解决数据不平衡和群体异质性的挑战。最后，大量的实验结果证明了Dr. Right的增强性能！用于预测医生针对疾病的经验得分。

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