Research on medical data governance and multi-source data fusion based on integrated learning
For the problems of data dispersion,heterogeneous and low quality in medical data governance,a multi-source data fusion method based on integrated learning is proposed.By integrating a variety of machine learning algorithms,data from different medical departments and different systems are cleaned integrated and analyzed,and efficient data utilization and quality improvement are realized.The results show that the ensemble learning algorithm achieves the highest classification performance on multi-source datasets with an accuracy of 85.32%,outperforming other classification methods on single-source datasets.Studies have proved that multi-source data fusion combined with integrated learning can significantly improve the performance of medical data classification models and provide moreaccurate support for clinical decision-making.
medical data governancemulti-source dataintegrated learningclassification
NETL
NSTL
万方数据
