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Performance Evaluation of an Information Technology Intervention Regarding Charging for Inpatient Medical Materials at a Regional Teaching Hospital in Taiwan: Empirical Study

Performance Evaluation of an Information Technology Intervention Regarding Charging for Inpatient Medical Materials at a Regional Teaching Hospital in Taiwan: Empirical Study

Both Huang et al [35] and Chang and Lin [34] modified the IS Success Model to best explore the impact of introducing an IS to the shift system on the performance and satisfaction of nursing staff.Furthermore, in the IS discipline, “user satisfaction” and “system

Min-Chi Liao, I-Chun Lin

JMIR Mhealth Uhealth 2020;8(3):e16381


Correction: A Web-Based Acceptance-Facilitating Intervention for Identifying Patients’ Acceptance, Uptake, and Adherence of Internet- and Mobile-Based Pain Interventions: Randomized Controlled Trial

Correction: A Web-Based Acceptance-Facilitating Intervention for Identifying Patients’ Acceptance, Uptake, and Adherence of Internet- and Mobile-Based Pain Interventions: Randomized Controlled Trial

The corrected references listed below use the new reference number in accordance with the renumbering.In Reference 3, author “OPENMinds” has been removed.In Reference 20, “van SA” has been changed to “van Straten A”.Reference 24 was previously “Paganini S, Lin

Jiaxi Lin, Bianca Faust, David Daniel Ebert, Lena Krämer, Harald Baumeister

J Med Internet Res 2019;21(2):e12015


Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy

Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy

Energy expenditure was calculated using the following equation: COSMED K4b2 EE (kcal/min)=([1.2285*RER]+3.821)*VO2 where VO2 is the oxygen consumption in liters per minute. All data were processed according to the following procedures:1.

Amit Pande, Prasant Mohapatra, Alina Nicorici, Jay J Han

JMIR Rehabil Assist Technol 2016;3(2):e7


Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review

Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review

The action for the ventilation setting was binary, that is, for each 10-min time step, the RL agent needed to decide whether the ventilation should be set on (continued MV) or off (weaned from MV).

Siqi Liu, Kay Choong See, Kee Yuan Ngiam, Leo Anthony Celi, Xingzhi Sun, Mengling Feng

J Med Internet Res 2020;22(7):e18477