Feasibility study of intelligent healthcare based on digital twin and data mining

Its use of historical and real-time data and algorithmic models to digitally create virtual models of physical entities to simulate, validate, predict, and control the full lifecycle processes of physical entities is widely acknowledged as a digital twin. A digital twin is essentially a digital replica of a physical entity or process. With the advancement of data mining and digital twin technology, digital twin combined with medical information data mining is gaining popularity, and this paper conducts corresponding research based on data mining and digital twin technology. First, the sensors on the patient are read. Second, the sensor data is extracted from EEG data features. Finally the patient’s medical history data is data mined from the SEER database, primarily for specific cancer patients, and an overall assessment of the patient’s medical health status and feedback to the patient is provided through the integration of the above data.