Transforming Healthcare Data
Utilizing advanced ML models to analyze and structure clinical records for improved patient outcomes.
Machine learning technology applications
Use outlier detection algorithms (such as isolation forests) to identify noise in medical data (such as abnormal indicators caused by equipment failure), fill in missing test results based on multiple imputation or deep learning models (such as variational autoencoders VAE), and perform feature scaling (such as Z-score standardization) on data collected from different hospitals and different devices to avoid model bias.
Data Model Development
We specialize in developing machine learning models for clinical data analysis and healthcare insights.
Feature Extraction Tools
Extract relevant medical concepts from unstructured data, converting them into structured formats for analysis.
Our models include traditional algorithms and deep learning approaches for comprehensive healthcare data analysis.
Machine Learning Models
