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

3

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

5

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.)

6

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

6

Variables: age, gender and place admitted from

Demographic information

8

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

8

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%

9

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%)

10

Uses AutoPrognosis – our state-of-the-art automated machine learning framework – to issue forecasts

Overview of our Adjutorium system

12

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

13

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

13

15

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

16

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

17

18

Demonstrator

19

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

21