SeekDoc: Seeking eligible doctors from electronic health record

Lu Jiang; Shasha Xie; Yuqi Wang; Xin Xu; Xiaosa Zhao; Ye Zhang; Jianan Wang; Lihong Hu; Lu Jiang; Shasha Xie; Yuqi Wang; Xin Xu; Xiaosa Zhao; Ye Zhang; Jianan Wang; Lihong Hu

doi:10.3934/mbe.2021271

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 5347-5363. doi: 10.3934/mbe.2021271

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Research article Special Issues

SeekDoc: Seeking eligible doctors from electronic health record

Northeast Normal University, Changchun 130117, China

Received: 22 March 2021 Accepted: 07 June 2021 Published: 16 June 2021

With the development of online medical service platform, patients can find more medical information resources and obtain better medical treatment. However, it is difficult for patients to discover the most suitable doctors from the complex information resources. Therefore, the analysis and mining of Electronic Health Record(EHR) is very important for patients' timely and accurate treatment. Discovering the most suitable doctor is actually predicting the exact performance of the doctor for a specific disease. We believe that "a curative/bad treatment is likely to be caused by a good/bad doctor, and a good/bad doctor has a higher/lower evaluation by the patient(s)". In this paper, we propose a novel approach named SeekDoc, which is to seek the most effective doctor for a specific disease. Specifically, we build a doctor-disease heterogeneous information network and collect patients reviews and rating records for doctors. Then, we embed the comprehensive comment data for doctors and the constructed heterogeneous information network. Next, we use the autoencoder mechanism to learn the embedded features, which is an effective learning algorithm for constructing the latent feature representation in an unsupervised manner. After this learning, the latent features are input into the extreme gradient boosting (XGBoost) algorithm to improve their detection capabilities. Finally, extensive experiments show that our method can effectively and efficiently predict the doctor's experience score for specific diseases and has good performance compared with other algorithms.
- healthcare,
- electronic health record,
- heterogeneous information network,
- autoencoder,
- extreme gradient boosting(XGBoost)
Citation: Lu Jiang, Shasha Xie, Yuqi Wang, Xin Xu, Xiaosa Zhao, Ye Zhang, Jianan Wang, Lihong Hu. SeekDoc: Seeking eligible doctors from electronic health record[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5347-5363. doi: 10.3934/mbe.2021271

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Abstract

With the development of online medical service platform, patients can find more medical information resources and obtain better medical treatment. However, it is difficult for patients to discover the most suitable doctors from the complex information resources. Therefore, the analysis and mining of Electronic Health Record(EHR) is very important for patients' timely and accurate treatment. Discovering the most suitable doctor is actually predicting the exact performance of the doctor for a specific disease. We believe that "a curative/bad treatment is likely to be caused by a good/bad doctor, and a good/bad doctor has a higher/lower evaluation by the patient(s)". In this paper, we propose a novel approach named SeekDoc, which is to seek the most effective doctor for a specific disease. Specifically, we build a doctor-disease heterogeneous information network and collect patients reviews and rating records for doctors. Then, we embed the comprehensive comment data for doctors and the constructed heterogeneous information network. Next, we use the autoencoder mechanism to learn the embedded features, which is an effective learning algorithm for constructing the latent feature representation in an unsupervised manner. After this learning, the latent features are input into the extreme gradient boosting (XGBoost) algorithm to improve their detection capabilities. Finally, extensive experiments show that our method can effectively and efficiently predict the doctor's experience score for specific diseases and has good performance compared with other algorithms.

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