BUILDING THE DIGITAL ECOSYSTEM FOR TOMORROW’S … · 1. Design an ML-driven ecosystem for...

23 October 2018 ConnHlth-P-233 v1.3

Commercially confidentialJ.FINN

AI in Pharma Summit

BUILDING THE DIGITAL ECOSYSTEM FOR

TOMORROW’S CLINICAL TRIALS

23 October 2018 ConnHlth-P-233 v1.3COMMERCIALLY CONFIDENTIAL 2

▪ $30-40 million average cost (Phase 1+2+3)

▪ Average drop out rate 30%

▪ Average cost per new recruit $22k

▪ 80% of all trials do not complete

Harnessing mobile human data can:

▪ Inform better trial design

▪ Better understand outcomes

▪ Improve clinical likelihood of success

SITUATION

Clinical Trials

23 October 2018 ConnHlth-P-233 v1.3Commercially confidential

The stress response pathway

OVERVIEW

Chronic illness

e.g. heart disease, stroke, diabetes, UC, respiratory

Chronic stress

multiple factors

Pathophysiological

effects

Co-morbidity

of depression

cardiovascular

metabolic

respiratory

immune

nervous and

endocrine

Clinical trials involving chronic conditions must factor in the co-morbidity of stress


CHALLENGE

Clinical insight during trials

Stress impacts behaviour and physiology:

▪ regime compliance may be compromised

▪ drug effect may be changed

What if you could develop an ML-driven

stress monitoring system to provide deeper

clinical insight during trials?

Continuous monitoring can:

▪ mitigate the impact of stress on compliance

▪ enable investigation of stress as a

confounding factor

▪ inform better adaptive trial design through real

time data


1. Design an ML-driven ecosystem for

non-invasively assessing stress during

clinical trials (CTs)

2. Design user-friendly wearable and CT data

collection app

3. Design a widget for CTMS dashboards to

present ML stress predictions

▪ produced an ML-driven numerical estimator for

stress

▪ consulted with psychiatrists, neuroscientists and

clinical trial experts to generate a robust design

To develop an unobtrusive digital system, we

had to be as obtrusive as possible

Verum Goals

SYSTEM OVERVIEW


GETTING THE RIGHT COMBINATION OF SENSORS

Biometric sensor data Data collection

ECG (500 Hz)

EDA (500 Hz)

EMG (500 Hz)

Respiration (500 Hz)

Skin Temp

Air Temp and Humidity

Pupil Tracking

Accelerometer

Sleep


Self-reporting Data collection

BEHAVIOURAL AND CONTEXTUAL DATA GATHERING

Numeric Rating Scale

State Trait Anxiety Index

Perceived Stress Scale

Free Text Input

Brief Voice Analysis

Log Intake

Log Activity


Trained to self-reported stress

▪ Trial size n=10, 4 days per person

Tested:

▪ Different ML techniques

– Normal linear regression (baseline)

– Ridge regression

– Random forest regression

– Support Vector Regression

▪ Different sensor combinations (256)

▪ Different methods of imputing missing data

Developing the machine learning algorithm

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A day in the life

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Our ML prediction of stress levels aligns well with self-reported stress

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Stress experiment – 2 hr

VALIDATION

▪ Calming period (VR)

▪ Stress inducing (VR, Stroop, alphabet transposition, mathematical questions)

▪ Calming period

▪ Stress questionnaires asked at each stage

10:05 10:20 10:35 10:50 11:05 11:20 11:35 11:50

Elicited Stress

Stress period Relax period

Predicted Stress


▪ The best results used 3 different pathways

▪ Pragmatic system will operate with 2 sensors

▪ Design challenges to building out a digital ecosystem

– 14 skill sets, 36 people

▪ Standard ML techniques must be adapted to cope with

data at very different rates

▪ Improved performance would require a data set

balanced between high and low stress events

– requires data synthesis/extension e.g via GANs

Voice

Error = 3.4

ECG/EMG

Error = 5.9

EDA

Error = 7.8

2.7 2.7

4.7

2.2

Project Insights


Sensors integrated into user-friendly wearables


Bringing the AI prediction to existing CTMS dashboard

CLINICAL TRIAL DASHBOARD WIDGET


Benefits of the Verum platform

CLINICAL TRIALS OF THE FUTURE

An AI-driven stress monitoring system can provide deeper clinical

insight during trials, helping clinicians make more informed decisions.

Verum can:

▪ Mitigate the impact of stress on compliance

▪ Enable investigation of stress as a confounding factor

▪ Enable better profiling of drug effects and adverse events

▪ Enable study monitors to reach out to patients swiftly to respond to

adverse events

▪ Inform better adaptive trial design through bigger, real-time

contextualised data sets

23 October 2018 ConnHlth-P-233 v1.3

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BUILDING THE DIGITAL ECOSYSTEM FOR TOMORROW’S … · 1. Design an ML-driven ecosystem for...

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