Monitor patients intelligently with AI.
Data Science & Analytics
Module-by-module breakdown of AI in Patient Monitoring and Management, from foundations to a certified capstone project.
Signals
โข ECG, SpO2, respiration and continuous vitals: sampling, resolution and artefact
โข Motion artefact, lead-off and the alarm burden this creates
โข Wearable versus bedside data quality, and what each can support clinically
Processing
โข Filtering, resampling and handling irregular and missing observations
โข Feature extraction, including heart-rate variability and waveform morphology
โข Windowing and label alignment for event prediction
Prediction
โข Early warning scores as the baseline any model must beat
โข Sepsis and deterioration prediction, and the published failures worth studying
โข Lead time versus precision, and the ward capacity to respond
Alarms
โข Alarm fatigue as the dominant clinical failure mode of monitoring systems
โข Alarm suppression, escalation logic and the safety case for silencing anything
โข Human factors in the design of a bedside or remote monitoring display
Remote Care
โข Remote monitoring and hospital-at-home programme design
โข Connectivity, device management and data governance outside the hospital
โข Evaluating whether monitoring changed outcomes rather than just generating data
e-Certificate and e-Marksheet issued on successful completion.