ML Risk Classification Engine

Chorus uses machine learning models to assist in identifying potential patient risk signals across multiple physiological measurements.

The risk classification engine evaluates patterns within biosignals, including:

• ECG waveform irregularities
• Blood pressure variability
• Oxygen saturation changes
• Blood glucose trends
• Weight fluctuations and fluid retention indicators

Rather than relying solely on threshold alerts, the classification engine analyzes patterns across multiple observations to detect subtle changes in patient health status.

Risk classifications generated by the system are surfaced to clinicians for review and validation.