This document provides information about Lisa Falco, the Director of Data Science at Ava Women's Health. It discusses Ava's vision of being a long-term companion for women through all stages of their reproductive lives. It describes how Ava's artificial intelligence platform uses data collected from a wearable bracelet and mobile app to provide women with insights. The document outlines Ava's algorithms for fertility tracking and how the algorithms are evolving from using clinical data to using the growing dataset of user data. It discusses challenges around product use and ensuring digital health products solve real problems.
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• MSc in Engineering Physics, Chalmers, Sweden, 2001
• PhD in Biomedical image analysis, EPFL, Switzerland, 2006
• Solianis Monitoring, Zürich, 2006 - 2011
• Data scientist in the algorithm development team
• Non-invasive glucose monitoring
• Scanco Medical, Zürich, 2011 - 2015
• Product manager
• micro-Computer Tomography
• Joined AVA in 2015 as Director of Data Science
MY WAY TO AVA
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1. AVA – The vision and solution
2. The basics - Why fertility tracking works
3. Algorithms – How the methods evolves with the data
4. Future – the challenges ahead
CONTENTS
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Women face various health challenges along their reproductive life stages
THE CHALLENGE
Understand your body
33%
Trying to conceive
10%
PregnancyContraception
33%
Menopause
60 years20
5. Ava is a long-term companion for women,
giving them data-driven and scientifically
proven insights along all stages of their
reproductive lives.
OUR VISION
5
6. Ava is the world’s first artificial
intelligence (AI) platform in women’s
health
6
TODAY’S SOLUTION
Bracelet
App
Big data / AI
7. • Breathing rate
• Skin temperature
• Heat loss
• Pulse rate
• Various heart rate variability (HRV) components
• Movement
• Bioimpedance
• Sleep (duration, deep/light, REM/non-REM)
• Perfusion
Generating big data sets is the basis for
Ava’s AI approach
THE BRACELET
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The Ava bracelet is a Class 1
registered medical device
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1. AVA – The vision and solution
2. The basics - Why fertility tracking works
3. Algorithms – How the methods evolves with the data
4. Future – the challenges ahead
CONTENTS
PresentationforXXX|August10,2017|Strictlyconfidential
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HORMONAL CHANGES
Average woman:
Cycle length: 28 days
Luteal length: 13 days
Averages apply to groups
not individuals!
Ava’s measured signals correlates with progesterone and estradiol
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Five different algorithms operating at different phases of the cycle
ALGORITHMS FOR FERTILITY TRACKING
High Peak
High
High
Peak
I
II
III
IV
PeakV
Peak
Peak
Peak
PeakHigh
PeakHigh
Priors
Priors Signals
Priors Signals
Signals
Priors Signals
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OUR DATA
• Every night 3 million data
points are collected
• Too little reference labels
Too much data!
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Features: e.g. average temperature during the
night
Currently our algorithms are based on these
features
Calculate daily features to reduce the complexity of nightly data
OUR DATA
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1. AVA – The vision and solution
2. The basics - Why fertility tracking works
3. Algorithms – How the methods evolves with the data
4. Future – the challenges ahead
CONTENTS
PresentationforXXX|August10,2017|Strictlyconfidential
15. Clinical data
15
Before launch – clinical data only
EARLY ALGORITHMS
Signals
LiteratureSignal
statistics
Woman
specific
info
16. Clinical data
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New data from Ava users & clinical
study coming in every day!
TRANSITION TO AI
Signals
LiteratureSignal
statistics
Woman
specific
info
AI
Clinical & user data
Signals Woman
specific
info
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WORKFLOW – RELEASED ALGO
Machine learning with 1’000 cycles
Temperature
HR
Feature 1
Feature 2
Raw data Signal features Engineered features Classifier
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WORKFLOW – IN DEVELOPMENT
Machine learning with 10’000+ cycles
Temperature
HR
Feature 1
Feature 2
Raw data Signal features Engineered features Classifier
In theory
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Better performance
Easier to include information from many sources
Faster development
Capture more variations
The more data we collect – the better we become
WHY GO FOR AI
AI
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1. AVA – The vision and solution
2. The basics - Why fertility tracking works
3. Algorithms – How the methods evolves with the data
4. Future – the challenges ahead
CONTENTS
PresentationforXXX|August10,2017|Strictlyconfidential
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WORKFLOW – COMING SOON
End-to-end learning: Same data but better infrastructure needed
Temperature
HR
Feature 1
Feature 2
Raw data Signal features Engineered features Classifier
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Good algorithms isn’t everything
CHALLENGES: PRODUCT
User behavior
• Users wear the bracelet only a few days per
cycle out of personal curiosity
• Users wear the bracelet only during the fertile
window
• Users wear the bracelet diligently in general,
but not for the first week of a cycle
• Missing data due to other reasons
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How to be more than a hype
CRITICAL POINTS FOR FUTURE OF DIGITAL HEALTH
Solve real problems and bring a real value to the customer
Products need to be based on solid science and validated by clinical studies