Machine Learning For Personalized Fertility Predictions
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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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Machine Learning For Personalized Fertility Predictions
- 1. Ava Women’s health Lisa Falco, Director of Data Science Geek Girl’s Carrot, 2018.10.27
- 2. 2 • 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
- 3. 3 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
- 4. 4 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 7 The Ava bracelet is a Class 1 registered medical device
- 9. 9 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
- 10. 10 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
- 11. 11 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
- 12. 12 OUR DATA • Every night 3 million data points are collected • Too little reference labels Too much data!
- 13. 13 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
- 14. 14 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 16 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
- 17. 17 WORKFLOW – RELEASED ALGO Machine learning with 1’000 cycles Temperature HR Feature 1 Feature 2 Raw data Signal features Engineered features Classifier
- 18. 18 WORKFLOW – IN DEVELOPMENT Machine learning with 10’000+ cycles Temperature HR Feature 1 Feature 2 Raw data Signal features Engineered features Classifier In theory
- 19. 19 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
- 20. 20 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
- 21. 21 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
- 22. 22 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
- 23. 23 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
- 24. THANK YOU! 24