SeekDoc: Seeking eligible doctors from electronic health record

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...

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Autores principales:	Lu Jiang, hasha Xie, Yuqi Wang, Xin Xu, Xiaosa Zhao, Ye Zhang, Jianan Wang, Lihong Hu
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	healthcare electronic health record heterogeneous information network autoencoder extreme gradient boosting(xgboost) Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/decc0aa79333435c8e863368667db695
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spelling	oai:doaj.org-article:decc0aa79333435c8e863368667db6952021-11-09T02:07:20ZSeekDoc: Seeking eligible doctors from electronic health record10.3934/mbe.20212711551-0018https://doaj.org/article/decc0aa79333435c8e863368667db6952021-06-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021271?viewType=HTMLhttps://doaj.org/toc/1551-0018With 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.Lu Jianghasha XieYuqi WangXin XuXiaosa ZhaoYe ZhangJianan WangLihong Hu AIMS Pressarticlehealthcareelectronic health recordheterogeneous information networkautoencoderextreme gradient boosting(xgboost)BiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5347-5363 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	healthcare electronic health record heterogeneous information network autoencoder extreme gradient boosting(xgboost) Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	healthcare electronic health record heterogeneous information network autoencoder extreme gradient boosting(xgboost) Biotechnology TP248.13-248.65 Mathematics QA1-939 Lu Jiang hasha Xie Yuqi Wang Xin Xu Xiaosa Zhao Ye Zhang Jianan Wang Lihong Hu SeekDoc: Seeking eligible doctors from electronic health record
description	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.
format	article
author	Lu Jiang hasha Xie Yuqi Wang Xin Xu Xiaosa Zhao Ye Zhang Jianan Wang Lihong Hu
author_facet	Lu Jiang hasha Xie Yuqi Wang Xin Xu Xiaosa Zhao Ye Zhang Jianan Wang Lihong Hu
author_sort	Lu Jiang
title	SeekDoc: Seeking eligible doctors from electronic health record
title_short	SeekDoc: Seeking eligible doctors from electronic health record
title_full	SeekDoc: Seeking eligible doctors from electronic health record
title_fullStr	SeekDoc: Seeking eligible doctors from electronic health record
title_full_unstemmed	SeekDoc: Seeking eligible doctors from electronic health record
title_sort	seekdoc: seeking eligible doctors from electronic health record
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/decc0aa79333435c8e863368667db695
work_keys_str_mv	AT lujiang seekdocseekingeligibledoctorsfromelectronichealthrecord AT hashaxie seekdocseekingeligibledoctorsfromelectronichealthrecord AT yuqiwang seekdocseekingeligibledoctorsfromelectronichealthrecord AT xinxu seekdocseekingeligibledoctorsfromelectronichealthrecord AT xiaosazhao seekdocseekingeligibledoctorsfromelectronichealthrecord AT yezhang seekdocseekingeligibledoctorsfromelectronichealthrecord AT jiananwang seekdocseekingeligibledoctorsfromelectronichealthrecord AT lihonghu seekdocseekingeligibledoctorsfromelectronichealthrecord
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SeekDoc: Seeking eligible doctors from electronic health record

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