Transforming Healthcare Data
Harnessing AI for clinical data cleaning, normalization, and machine learning model development.
Data Collection
Gathering large-scale clinical records to enhance healthcare analytics.
Machine Learning
The implementation of machine learning in clinical scenarios needs to take into account medical professionalism, data privacy and model reliability. The process can be broken down into the following key stages, each of which involves specific machine learning technology and medical scenario adaptation.
Data Normalization
The field definitions of blood routine analyzers, imaging equipment, and electronic medical record systems in different hospitals are inconsistent. Different standards may be used for tests on the same patient at different times. Structured numerical blood pressure, text medical record descriptions, image CT, and time-series signal electrocardiograms need to be standardized in a coordinated manner.