Machine learning clinical practice

The implementation of machine learning in clinical practice is not a simple superposition of technologies, but rather a promotion of the transformation from "empirical medicine" to "data-driven medicine" by reshaping the processing paradigm of medical data. Its core role is not only reflected in the improvement of specific clinical indicators, but also in the construction of a new medical service model of "precise diagnosis - individualized treatment - full-process management", providing a technical path to solve global health challenges such as uneven medical resources and aging.

Data Integration Services

We provide comprehensive data integration for clinical records from various healthcare institutions.

Data Cleaning Solutions

Utilizing AI for efficient data cleaning and normalization processes in healthcare datasets.

Feature Extraction Tools

Extracting relevant medical concepts from unstructured data for structured machine learning applications.

ML Model Development

Developing traditional and deep learning models for analyzing healthcare data and improving outcomes.

Standardized process

Process missing values ​​(such as using multiple interpolation to fill in laboratory indicators), abnormal values ​​(such as body temperature > 45°C is judged as equipment failure), extract frequency domain indicators of heart rate variability (HRV) (such as LF/HF ratio), and automatically extract radiomic features such as lung nodule volume and lobulation sign through 3D convolutional neural network.