Research on Data Value Based on Demand Forecast of Online Medical Platform
The rapid development of digital technology has advanced global digitalization.As a vital resource for driving economic growth,the value assessment of data has not yet formed a unified paradigm.With the expansion of data scale,the data quality disparity and data distortion urgently need to be addressed.The valua-tion of data is considered beneficial for evaluating data quality,filtering out distorted data,and promoting data trade,making its importance self-evident.In this paper,an online medical platform is used as the context,and the Shapley value method is employed to evaluate the value of data in doctor-patient matching.The efficiency of data value on the platform is intended to be improved through the data valuation results.First,the demand of doctors for the next year is predicted through the historical characteristics of doctors and the XGBoost model.Second,an operational profit function of hospitals based on the prediction results is constructed according to the online service mode of the medical platform,forming a value chain of data,model,and benefit.Finally,the Shapley value is used to assess data value,the effectiveness of the value assessment method is validated,and the characteristics of high-value data on the platform are analyzed.It is found that data value can be captured according to the task by the Shapley value.Cost reduction and efficiency increase of the model can be realized by filtering data based on the Shapley value,then enhancing the efficiency of application of data.Additionally,false data on the platform can be find,improving data quality.Therefore,appropriate valuation of data is seen as beneficial for better business understanding of the platform and enhancement of the efficiency of data.
data valuationonline medical platformShapley value
NETL
NSTL
万方数据
