Ying Liu, Junping Zhang, Shanshan Li, Wen Chen, Rongqian Wu, Zejin Hao, Jixiong Xu
Prediction of TNFRSF9 expression and molecular pathological features in thyroid cancer using machine learning to construct Pathomics models
Endocrine
paper presents revised technology acceptance model examine determines mobile healthcare systems mhs acceptance healthcare professionalsconformation factor analysis performed test reliability validity measurement model structural equation modeling technique used evaluate causal modelthe results indicated compatibility perceived usefulness perceived ease use significantly affected healthcare professional behavioral intent mhs selfefficacy strong indirect impact healthcare professional behavioral intent mediators perceived usefulness perceived ease use yet hypotheses technical support training effects perceived usefulness perceived ease use supportedthis paper provides initial insights factors likely significant antecedents planning implementing mobile healthcare enhance professionals mhs acceptance proposed model variables explained 70 variance behavioral intention use mhs study needed explore extra significant antecedents new itis acceptance mobile healthcare privacy security issue system information quality limitations mobile devices may interesting factors implementing mobile healthcare could conducted qualitative research