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Authorship Correction: A Clinical Decision Support Engine Based on a National Medication Repository for the Detection of Potential Duplicate Medications: Design and Evaluation

Authorship Correction: A Clinical Decision Support Engine Based on a National Medication Repository for the Detection of Potential Duplicate Medications: Design and Evaluation

Both Cheng-Yi Yang and Yu-Sheng Lo should have been designated as equal contributors on this article.The correction will appear in the online version of the paper on the JMIR website on July 5, 2019, together with the publication of this correction notice.

Cheng-Yi Yang, Yu-Sheng Lo, Ray-Jade Chen, Chien-Tsai Liu

JMIR Med Inform 2019;7(3):e15063


Author Contribution Correction: An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

Author Contribution Correction: An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation” (J Med Internet Res 2019;21(2):e12341) inadvertently marked Yu-Sheng Lo as an equal contributor when that designation should have only been applied to Cheng-Yi

Cheng-Yi Yang, Ray-Jade Chen, Wan-Lin Chou, Yuarn-Jang Lee, Yu-Sheng Lo

J Med Internet Res 2019;21(3):e13699


An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study

An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study

A sample of 1255 registered nurses with at least 1 month experience in the Chi Mei Medical Center (Taiwan) was randomly selected to complete the Chinese version of the 20-item MBI-HSS [3] in August 2016.

Yi-Lien Lee, Willy Chou, Tsair-Wei Chien, Po-Hsin Chou, Yu-Tsen Yeh, Huan-Fang Lee

JMIR Med Inform 2020;8(5):e16528


Social Media Users’ Perception of Telemedicine and mHealth in China: Exploratory Study

Social Media Users’ Perception of Telemedicine and mHealth in China: Exploratory Study

They are organized by specific forums; we have examined one of them in this paper, and it is entitled “telemedicine or mHealth” (or “yuan cheng yi liao or yi dong yi liao ” in Chinese). The selection is consistent with our research focus in this paper.

Ricky Leung, Huibin Guo, Xuan Pan

JMIR Mhealth Uhealth 2018;6(9):e181


Improving Patient Access to Diabetic Retinopathy Screening Through Telemedicine

Improving Patient Access to Diabetic Retinopathy Screening Through Telemedicine

MPH1LifeBridge Health - Sinai Hospital of Baltimore2401 W Belvedere AveBaltimore, MDUnited States410 601 8061twandy@lifebridgehealth.orgKiritsyMichaelMBA, MD1DurandDanielMD11LifeBridge Health - Sinai Hospital of BaltimoreBaltimore, MDUnited StatesCorresponding Author: Tiffany

Tiffany Wandy, Michael Kiritsy, Daniel Durand

iproc 2019;5(1):e15193


Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model

Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model

When θ denoted all parameters of the model, the objective function ℒ(D, θ) was formulated as follows:ℒ(D, θ) = −Summation (yi log [p (y|x)] + [1−yi] log [1−p (y|x]) / NResultsIn this study, we present a detailed analysis of the performance of our DNN model.

Chi-Shiang Wang, Pei-Ju Lin, Ching-Lan Cheng, Shu-Hua Tai, Yea-Huei Kao Yang, Jung-Hsien Chiang

J Med Internet Res 2019;21(2):e11016