Study hypothesis: Machine Learning algorithms and techniques previously developed for use in
the robotics field can be applied to the field of medicine. These state-of-the-art, feature
extraction and machine learning techniques can utilize patient vital sign data from bedside
monitors to discover hidden relationships within the physiological waveforms and identify
physiological trends or concerning conditions that are predictive of various clinical events.
These algorithms could potentially provide preemptive alerts to clinicians of a developing
patient problem, well before any human could detect a worrisome combination of events or
trend in the data.
1. Collect physiological waveform and numeric trend data from patient vital signs monitors
in ICUs at the University of Colorado Hospital and Children's Hospital Colorado.
2. Combine the physiological data from patient monitors with clinical data obtained from
patient Electronic Medical Records including IV fluids, medications, ventilator
settings, urine output, etc. for use in developing models of various clinical
3. Apply Machine Learning techniques to these models to identify physiological waveform
features and trend information, which are characteristic and predictive of common
clinical conditions including but not limited to:
- Post-operative atrial fibrillation and other cardiac dysrhythmias
- Post-operative cardiac tamponade
- Tension pneumothorax
- Optimal post-operative and post-resuscitation fluid needs
- Intracranial hypertension and cerebral perfusion pressure