Biomedical AI Machine Learning: The Road Ahead and How to Avoid Pitfalls

2. BioSymetrics Overview
About Us: We build Machine Learning software to optimize innovation discovery
and productivity for biopharma, healthcare and life sciences.
Client Base: Pharmaceutical, precision medicine, technology, and genomics
companies (payers and health organizations).
What We Offer: Our technology platform, Augusta™, is deployable anywhere
and uses advanced pre-processing and integrated data analytics to provide a
seamless experience.
- Awarded SAP’s 2017 “AI and the Enterprise Most Innovative Solution”
- Fast processing of multiple types of biological, clinical, R&D, genomics,
metabolomic, and outcome data in combination.

3. Key Challenges in Biomedical Data Analysis
Data Variety/Heterogeneity
Different Data Types:
EHR/EMR, MRI/fMRI,
EEG, EKG, chemistry
Lack of Standards
No standards for processing
or interpreting medical data
Lack of Scalability
Challenges in pre-processing
and machine learning using
massive data
Single framework for
integrated analytics
Customized pre-processing
of multiple raw data types
Deploy anywhere, on any
architecture

4. High Level Guide to Machine Learning (ML)
Focus of Machine Learning
(in order of increasing complexity)
● Descriptive analytics
● Predictive analytics
● Prescriptive analytics
● Integrated analytics
Examples of ML Models and Algorithms:
● Regression models
● Random forests
● Neural networks
Source: McKinsey Analytics. An executive’s guide to AI.
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai

7. ML Algorithms: Key Driver of Market Disruption
Reshaping Competitive Landscapes in <7 Years

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14. AugustaTM
ML Platform Technology
Augusta™ can start with siloed, complex, and raw data
of multiple types:
• Accurate results and automated distribution
• Scales to massive data architectures
• End-to-end machine learning
• Post-analysis & data visualization
• Integration of diverse and large-scale data types
• Data of any type, size, and dimensionality
explored and modeled
• Biological, clinical, genomics, precision medicine,
metabolomic, lab testing, drug compound data

15. AugustaTM
ML Platform Architecture
Physical Servers
Client side
Local machines
BioSymetrics server
Cloud Servers
AWS
Microsoft Azure
Private cloud
Conventional DBs
mySQL
PostGRES
Oracle
Other DBs
HBase, Bigtable
HDFS
noSQL
Input/Data
Management
Pre-processing
Analytics
Output Actionable insight
for quality of care
Personalized
diagnostic models
Population-scale
health analysis
Standardized data
features

16. Use Cases: Integrating Data in Precision Medicine
● Processing bias (measurements and feature design)
● Confounding factors (comorbidity, therapeutics, demographics)
● Selection bias (improper randomization)
● Information bias (more non-causal data reduces performance)
● Augusta’s architecture allows the careful reduction of these biases.
Alzheimer’s Disease Neuroimaging Initiative (ADNI) data

17. Use Cases: Time Series Data
● ECG/EEG data can be processed in near real-time, with features extracted and combined with other
data sources.
● Deep Learning Based segmentation using the NVIDIA CUDA® Deep Neural Network library.
● NVIDIA Inception program for next-gen AI leaders.

18. Use Cases: Medical Image Processing
● Augusta provides methods for image Registration, Standardization, ROI prediction.
● Methods are agnostic and can be trained to find novel ROIs.
● Applications: feature engineering, classification, anomaly detection.
Raw MRI Standardization Region of Interest

19. Use Cases: Value Based Care Analysis
● GE Healthcare (US) major initiative in value based care
● Raw EHR data (HL7, FIHR, schemas) for millions of patients
● Design therapeutic policies for management of diabetes and coronary artery disease.
Medical Data Appointments and Billing

20. Use Cases: ADME Prediction and Structural Analysis
● Augusta provides methods for ADME (pharmacokinetics and pharmacology) prediction.
● Exploratory analysis and viability scores can be computed for millions of potential compounds.
● Generative models for novel compounds with specific activities or targets.
● Construct configurations for input into in-silico screening methods (MD, CGMD, path sampling).
Novel compound to fight superbugs (Methicillin-resistant Staphylococcus aureus)?

21. Gain Competitive Advantage Leveraging Augusta™
Flexible Architecture for Rapid Results Our architecture can meet your needs:
- Parallelized on a cluster of nodes,
- Parallelized on a single node, or Single
threaded
Seamless Deployment Infrastructure
for Data Management and Access
Control
Augusta can go to the data:
- Deploy on public cloud services
(Microsoft Azure, AWS);
- Private cloud; Private server; or local
computing infrastructure
Streamlined Installation and
Maintenance Process
Augusta Docker uses containerization
technology to simplify installation and
upgrades
Full documentation, user guides,
training, and customized installation
available
Technology walkthroughs offer step-by-step
guidance for typical data analysis pipelines
What we offer and how we deliver it:

22. Our Team
Anthony Iacavone - Co-Founder & Board Chairman
Multiple successful ventures as an entrepreneur
Founder and former CEO of AdTheorent
Years of experience in ML applications for big data
Gabriel Musso - Chief Scientific Officer
PhD in Computational Biology (Univ. of Toronto)
Research Fellowship at Harvard Medical School
Background in Genomics, Drug Discovery & ML
Babak Afshin-Pour - VP of Technology
PhD in Electrical Engineering (Univ. of Tehran)
Former Research Fellow at the Rotman RI
Extensive experience in medical image processing
William Hoiles - Data Scientist
PhD in Electrical & Computer Engineering (UBC)
Research fellowships at UBC & UCLA
Expertise in feature engineering & neural networks
Victoria Catterson - Principal Data Scientist
PhD in Machine Learning for Diagnostics (Strathclyde)
Led a team of AI researchers on industry projects
Over a decade of Data Science experience
Scott Russo & James Lawson - Board Members
Wendy Tsai - VP Business Development
MBA in Finance and Strategy (Kellogg)
Biopharma & health IT cross-functional leadership
Experience corporate development & alliances