1. Computational Physiology for Critical Care Monitoring
Stuart Russell, UC Berkeley
Joint work with Geoff Manley, Mitch Cohen, Kristan Staudenmayer, Diane Morabito (UCSF), Norm Aleks, Nimar Arora, Shaunak Chatterjee (UCB)
Truly blessed by collaboration with Geoff Manley, Prof of Neurology and Neurosurgery at UCSF and Chief of Neurotrauma, SFGH
I’m an AI researcher and also chair of CS; excuse biomedical ignorance

2. This is what an ICU looks like: ventilator, fluids, monitors;
~200 medical procedures per day, many potentially fatal

3. Critical care $300B/yr in US, high morbidity/mortality
Goal: improve outcomes, reduce length of stay, do science
Approach:
Large-scale data repository for worldwide research use
Currently 60GB, 16 ICU beds monitored 24/7, soon multi-institutional
First release any day now ….
Data mining for outcome prediction, early warning, etc.
Real-time model-based estimation of patient state
(And systems physiology model-building)

4. Critical care state estimation
Given
~140 initial presentation fields
~40 real-time sensor streams
~1500 asynchronous measures (blood, drugs, etc.)
Compute posterior probability distribution for
~100 (patho)physiological state variables
Method
Patient-adaptive dynamic Bayesian network (DBN): stochastic models of physiology and sensor dynamics (c.f. Guyton et al., 1972, 354-variable nonlinear ODE)
Flexible across time scales, models, sensors (images, text, etc.)
Can incorporate genetic factors (observed or unobserved)
DBN technology =~ stochastic differential equations x discrete Markov chains (factored)

5. Human physiology v0.1

6. Brain Neurotransmitters Heart Vasculature Blood flow
Medullary cardiovascular center
Cardiac parasympathetic output
Cardiac sympathetic output
Card. M2
Card. β1
Card. β2
Vasc. α1
Vasc. α2
Vasc. β2
Heart rate
Cardiac contrac-tility
Venous tone
Arterio-lar tone
Cardiac preload
Capillary pressure
Cardiac stroke volume
Cardiac output
Vascular resistance
Mean arterial blood pressure
Barorecep-tor discharge

7. Brain Neurotransmitters Heart Vasculature Blood flow
Medullary cardiovascular center
Medullary cardiovascular center
Cardiac parasympathetic output
Cardiac sympathetic output
Cardiac parasympathetic output
Cardiac sympathetic output
Card. M2
Card. β1
Card. β2
Vasc. α1
Vasc. α2
Vasc. β2
Card. M2
Card. β1
Card. β2
Vasc. α1
Vasc. α2
Vasc. β2
Heart rate
Cardiac contrac-tility
Venous tone
Arterio-lar tone
Heart rate
Cardiac contrac-tility
Venous tone
Arterio-lar tone
Cardiac preload
Capillary pressure
Cardiac preload
Capillary pressure
Cardiac stroke volume
Cardiac stroke volume
Example doesn’t show: patient-specific parameter variables (eg blood volume),
Cardiac output
Vascular resistance
Cardiac output
Vascular resistance
Mean arterial blood pressure
Mean arterial blood pressure
Barorecep-tor discharge
Barorecep-tor discharge

8. Medullary cardiovascular center Medullary cardiovascular center
Setpoint inputs from ANS, CNS, intracranial, blood
Setpoint inputs from ANS, CNS, intracranial, blood
Medullary cardiovascular center
Medullary cardiovascular center
Cardiac parasympathetic output
Cardiac sympathetic output
Cardiac parasympathetic output
Cardiac sympathetic output
Card. M2
Card. β1
Card. β2
Vasc. α1
Vasc. α2
Vasc. β2
Card. M2
Card. β1
Card. β2
Vasc. α1
Vasc. α2
Vasc. β2
Heart rate
Cardiac contrac-tility
Venous tone
Blood [volume]
Pulm. [intra-thoracic press.]
Arterio-lar tone
Heart rate
Cardiac contrac-tility
Venous tone
Blood [volume]
Pulm. [intra-thoracic press.]
Arterio-lar tone
Cardiac preload
Capillary pressure
Cardiac preload
Capillary pressure
Cardiac stroke volume
Cardiac stroke volume
Blood [transu-dation]
Blood [transu-dation]
Cardiac output
Vascular resistance
Cardiac output
Vascular resistance
Mean arterial blood pressure
Mean arterial blood pressure
Intracranial physiology
Tissues-NOS [perfusion]
GI/Liver [perfusion]
Barorecep-tor discharge
Intracranial physiology
Tissues-NOS [perfusion]
GI/Liver [perfusion]
Barorecep-tor discharge

9. Medullary cardiovascular center Medullary cardiovascular center
Setpoint inputs from ANS, CNS, intracranial, blood
Setpoint inputs from ANS, CNS, intracranial, blood
Medullary cardiovascular center
Medullary cardiovascular center
Cardiac parasympathetic output
Cardiac sympathetic output
Cardiac parasympathetic output
Cardiac sympathetic output
Card. M2
Card. β1
Card. β2
Vasc. α1
Vasc. α2
Vasc. β2
Card. M2
Card. β1
Card. β2
Vasc. α1
Vasc. α2
Vasc. β2
Heart rate
Cardiac contrac-tility
Venous tone
Blood [volume]
Pulm. [intra-thoracic press.]
Arterio-lar tone
Heart rate
Cardiac contrac-tility
Venous tone
Blood [volume]
Pulm. [intra-thoracic press.]
Arterio-lar tone
Cardiac preload
Capillary pressure
Cardiac preload
Capillary pressure
Heart rate sensor model
Heart rate sensor model
Cardiac stroke volume
Cardiac stroke volume
Central venous pressure sensor model
Blood [transu-dation]
Central venous pressure sensor model
Blood [transu-dation]
Cardiac output
Vascular resistance
Cardiac output
Vascular resistance
Mean arterial blood pressure
Mean arterial blood pressure
MAP sensor model
MAP sensor model
Intracranial physiology
Tissues-NOS [perfusion]
GI/Liver [perfusion]
Barorecep-tor discharge
Intracranial physiology
Tissues-NOS [perfusion]
GI/Liver [perfusion]
Barorecep-tor discharge

10. Medullary cardiovascular center Medullary cardiovascular center
Setpoint inputs from ANS, CNS, intracranial, blood
Setpoint inputs from ANS, CNS, intracranial, blood
Medullary cardiovascular center
Medullary cardiovascular center
PK [conc. of phenyl-ephrine]
PK [conc. of phenyl-ephrine]
Cardiac parasympathetic output
Cardiac sympathetic output
Cardiac parasympathetic output
Cardiac sympathetic output
Card. M2
Card. β1
Card. β2
Vasc. α1
Vasc. α2
Vasc. β2
Card. M2
Card. β1
Card. β2
Vasc. α1
Vasc. α2
Vasc. β2
Heart rate
Cardiac contrac-tility
Venous tone
Blood [volume]
Pulm. [intra-thoracic press.]
Arterio-lar tone
Heart rate
Cardiac contrac-tility
Venous tone
Blood [volume]
Pulm. [intra-thoracic press.]
Arterio-lar tone
Cardiac preload
Capillary pressure
Cardiac preload
Capillary pressure
Heart rate sensor model
Heart rate sensor model
Cardiac stroke volume
Cardiac stroke volume
Central venous pressure sensor model
Blood [transu-dation]
Central venous pressure sensor model
Blood [transu-dation]
Cardiac output
Vascular resistance
Cardiac output
Vascular resistance
Mean arterial blood pressure
Mean arterial blood pressure
MAP sensor model
MAP sensor model
Intracranial physiology
Tissues-NOS [perfusion]
GI/Liver [perfusion]
Barorecep-tor discharge
Intracranial physiology
Tissues-NOS [perfusion]
GI/Liver [perfusion]
Barorecep-tor discharge

11. Real data are messy

12. A DBN that reflects that model, along with probabilistic occurrence and blood-pressure transition models 12

13. Maybe another from Diane?13

14. ALARM
2 days, 19 false alarms, 7 true alarms, 1 false+true; all correct 14

15. Zoomed in, filtered. When done, segue: when does this do poorly
Zoomed in, filtered. When done, segue: when does this do poorly? When blood pressure changes quickly – because the blood pressure transition doesn’t allow for it (and if I make it broader, artifacts aren’t detected). System needs more knowledge of blood pressure physiology (and some redundant data, say from heart rate, would be useful too). How to do this? 15

16. Next Steps Reduce ICU false alarms from >90% to <5%
Demonstrate clinically relevant inferences, e.g.,
Vascular stiffness
Erroneous drug administration
Pulmonary artery pressure (w/o catheter!)
Extend physiology model to all major systems
Multiscale: connect physiology to molecules