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Cambridge Centre for AI in Medicine
vanderschaar-lab.com
Using machine learning and PHE data to help hospitals cope with COVID-19
Prof. Mihaela van der Schaar
April 1, 2020
Our team
Cambridge Centre for AI in MedicineML-AIM lab: Dr. Ahmed Alaa, Zhaozhi Qian, Prof. Mihaela van der Schaar –Director, Cambridge Centre for AI in Medicine
Dr. Ari Ercole – Intensive Care Unit, Addenbrookes HospitalProf. Stefan Scholtes – Dennis Gillings Professor of Health Management, Cambridge Judge Business SchoolProf. Daniela DeAngelis – Deputy Director, MRC Biostatistics Unit
PHE + NHS Improvement + NHS EnglandDr. Anees Pari – Consultant, Public Health MedicineDr. Geraldine Linehan - Medical Director of Commissioning, NHS England and NHS Improvement – East of England
Real challenge posed by COVID-19 is managing limited healthcare resources
Protecting the NHS is a key priority
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National level Hospital level
* NHS Statistical Press Notice, January 2020
Critical care beds*
4,123Occupancy rate
83%
Time since first case
# cases
Capacity
Even with a national “flat curve,” hospital-specific demand may still surpass capacity
Time since first case
# hospital-specific cases
Social policies = flatten the curve!
Help healthcare professionals manage hospital-level resources
Using anonymized PHE CHESS data and Cambridge ML-AIM Lab’s machine learning methods to:
forecast personalized risk for each patient forecast personalized patient benefit from resources forecast which treatments are needed by each patient and when forecast which resources are needed by each patient and when forecast future resource requirements at the hospital level based
on current demand
To provide evidence that reliably assists the difficult decisions clinicians and managers have to make to save lives
Our goal
Decisions that healthcare professionals need to make
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General ward
Intensive careWhen will patients
be discharged?
Which patients should be on ventilators,
and for how long?Which patients
should go to the ICU?
Which patients can safely go home?
Achieve the best possible utilization of available resourcesIdentify personalized benefit from resources (at the current time)
Limits on resources (ICU beds, ventilators, etc.)
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Intensive care
arrival rate required ICU capacitymean ICU occupancyx =
COVID-19 Hospitalization in England Surveillance System Data collected until March 29, 2020
Different types of variables:– personal information– laboratory details– hospitalization details– risk factors– outcomes
Data is continuously updated
CHESS Data
Hospitalization datesN
umbe
r of p
atie
nts
March 29, 2020
Data hosted by 1,694 COVID patients in the CHESS dataset until March 29, 2020
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Variables: age, gender and place admitted from
Demographic information
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Age (years)
Frac
tion
of p
atie
nts Average =
62.5 years
Age ranges (years)
% o
f pat
ient
s
5.5%2.7% 4.4%
7.6%
15.4%20.4%
44%
Gender % of patients
Male 65.4%
Female 34.6%
Place admitted from % of patients
Acute Trust hospital 2.97%
Home 88.6%
Nursing home 1.73%
Penal establishment 0.24%
Residential home 1.48%
Temp accommodation 0.99%
Other UK hospital 0.99%
Other 1.73%
Patie
nt a
dmis
sion
from
Statistics of resource utilization
A total of 1,694 patientsA total of 649 ICU admissions
Ventilation:– 307 invasive ventilations– 54 non-invasive ventilations
Still hospitalized
No ICU admission
Hospital admission
ICU admission
DischargedDeath9%9%
38% 62%
29% 53%
Days since hospitalization
Frac
tion
of p
atie
nts
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Who is being admitted to the ICU?
Variable Admitted to ICU Not admitted to ICU
Age 61.5 70.8
Gender (Male) 70% 57%
Onset to diagnosis (days) 4.27 1.69
Chronic respiratory 6.9% 5.3%
Asthma 10.1% 4.9%
Hypertension 22.7% 9.9%
Chronic heart disease 8.1% 8.4%
Chronic renal disease 3.1% 3.8%
Chronic liver disease 1.2% 0.6%
Diabetes 17% 7%
Immunosuppression Disease 1.5% 1.8%
Obesity 6.6% 2.0%
Pregnancy 0.3% 0.0%
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Who is being ventilated?
Variable Ventilated Not ventilated
Age 63.1 68.5
Gender (Male) 40 (70%) 1128 (60%)
Onset to diagnosis (days) 4.44 1.96
Chronic respiratory 3 (7.6%) 105 (4.9%)
Asthma 4 (8.7%) 118 (5.1%)
Hypertension 13 (28.2%) 245 (9.4%)
Chronic heart disease 6 (8.5%) 149 (8.5%)
Chronic renal disease 6 (3.3%) 61 (3.3%)
Chronic liver disease 1 (1.1%) 14 (0.5%)
Diabetes 10 (18.0%) 187 (7.8%)
Immunosuppression Disease 2 (2.2%) 30 (1.5%)
Obesity 7 (11.3%) 69 (1.8%)
Pregnancy 1 (0.3%) 5 (0.2%)
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Uses AutoPrognosis – our state-of-the-art automated machine learning framework – to issue forecasts
Overview of our Adjutorium system
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CHESS data
(continuously updated)
Multi-class event prediction model
Time-to-event prediction model
AutoPrognosis*Survival Quilts **
Trained model
* A. M. Alaa and M. van der Schaar, ICML 2018.
Training
Automatically crafts machine learning ensembles Model is updated as more data is added to CHESS dataset
** C. Lee, A. M. Alaa, W. Zame and M. van der Schaar, AISTATS 2019.
Forecast personalized risk for each patient Forecast personalized patient benefit from resources Forecast which treatments are needed by each patient and when Forecast future resource requirements
Forecasting in real time with Adjutorium
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General ward
Time
ICU
Bed
s
Occupancy
3%2%20%
15%1%30%
Resource utilization forecast
Occupancy
Personalized risk
Accuracy of predicting mortality
AUC-ROC accuracy of our model in predicting mortalityTrained on 850 patients, tested on 197 patients
Model AUC-ROC
Our model: all features 0.871 ± 0.002
Our model: age + specific comorbidities + Time from symptoms to hospitalization
0.862 ± 0.003
Our model: age + specific comorbidities 0.836 ± 0.002
Our model: age + no. of comorbidities 0.833 ± 0.003
Our model: age 0.799 ± 0.003
Cox Regression: all features 0.773 ± 0.003
Charlson Comorbidity Index 0.596 ± 0.002
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Value of information
Evaluating variable importance by dropping one variable at a time(needs refinement based on more data)
Impact on AUC-ROC
Accuracy of predicting ICU admissions
AUC-ROC accuracy in predicting whether a patient will be admitted to ICU based on info available at hospital admissionTrained on 950 patients, tested on 285 patients
Model AUC-ROCOur model: all features 0.835 ± 0.001Our model: age + specific comorbidities 0.778 ± 0.002Our model: age + no. of comorbidities 0.781 ± 0.002Our model: age 0.770 ± 0.002Cox Regression: all features 0.771 ± 0.002Charlson Comorbidity Index 0.556 ± 0.013
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Accuracy of predicting ventilation
AUC-ROC accuracy in predicting whether a patient will need ventilation based on info available at hospital admissionTrained on 810 patients, tested on 276 patients
Model AUC-ROCOur model: all features 0.771 ± 0.002Our model: age + specific comorbidities 0.761 ± 0.001Our model: age + no. of comorbidities 0.720 ± 0.003Our model: age 0.691 ± 0.001Cox Regression: all features 0.690 ± 0.002Charlson Comorbidity Index 0.618 ± 0.002
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Demonstrator
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Demonstrator
ML-methods to address COVID challenges: Further opportunities
- Estimate longitudinal trajectories of disease
- Develop early warning systems
- Develop personalized treatment recommendations for each patient
- Inform policies and improve collaboration
- Manage uncertainty
- Expedite clinical trials
https://www.linkedin.com/pulse/responding-covid-19-ai-machine-learning-mihaela-van-der-schaar
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