The document discusses the future of medical data science and precision medicine. It outlines both the promise and challenges, including the need for a holistic understanding of patients, improved diagnostics, and better treatment. It also discusses the large market opportunity in machine learning for medicine. The author's company, Biosymetrics, provides an integrated analytics and machine learning platform called Augusta that can process and integrate different types of medical data, like images and genomics data, for applications like predicting autism and Alzheimer's disease.

1. The Future of Medical Data Science
Shiva Amiri, PhD
AI Conference, San Francisco 2 June 2017

2. Promise and Pitfalls of Precision
Medicine
 Holistic understanding of the individual’s
condition for:
 Prevention
 Improved Diagnostics
 Better drugs
 Better care
 Challenges
 Data Variety/Heterogeneity
 Lack of Standards
 Lack of Scalability

3. The Market Opportunity
 IDC Reports Big Data Analytics market at $130B in 2016
 Machine learning in medicine market projected to reach $6B by
2021 according to Frost and Sullivan
 According to one estimate, the value of the healthcare IoT will top
$163 billion by 2020, with compound annual growth of 38.1%
between 2015 and 2020
 Streaming data, real-time analytics and machine learning will
remain a significant challenge for multiple sectors

4. Competitive Advantage
 Integrated analytics/ML solution – incorporating large repositories of images, genomics
data, compounds, together
 Custom pre-processing for multiple types of raw biomedical data – unique ability to
carry out targeted preprocessing on multiple complex biomedical data types
 Model optimization based on proprietary parameter iteration method
 No limitations on data size - Outperforms Revolution R, Rapid Miner, Amazon ML,
Microsoft Azure, H2O ... in benchmarking tests

5. Our Solution: The Augusta Platform
Focused on pre-processing, feature optimization for multiple data types in
biomedicine

6. Integrating Datasets: Example 1
 Features extracted from medical images can be compared
using the results of genomic analysis (shown below for Autism
Spectrum Disorder)
Higher
in Var-
Group
Higher
in Var+
Group

7. Integrating Datasets – Example 2
 Combining MRI with genomic features allowed better prediction
performance than with either alone
 Adding metabolic features increased prediction performance
(shown using data from ADNI – Alzheimer’s Disease)

8. Detailed Documentation For AugustaTM
 Documentation available for all modules, including 11 code walkthroughs

9. Thank You
Shiva Amiri, PhD, CEO shiva@biosymetrics.com
Babak Afshin-Pour, PhD, VP Technology babak@biosymetrics.com
Gabe Musso, PhD, VP Life Sciences gabe@biosymetrics.com
Anatoly Likhatchev, VP FinTech tolik@biosymetrics.com
www.biosymetrics.com