neural network approaches for reducing healthcare costs · 2019-06-21 · neural network approaches...
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Neural Network Approachesfor Reducing Healthcare Costs
Kimberly RobaskyTranslational Research Scientist, Renaissance Computing InstituteAdjunct Professor, Department of GeneticsAdjunct Professor, School of Information and Library ScienceCore Faculty, Carolina Health Informatics ProgramUniversity of North Carolina, Chapel Hillkrobasky@renci.org
Who is RENCI?
Carolina is at “the epicenter of computer science and data science” because of the work of the Renaissance Computing Institute
~ Executive Vice Chancellor and Provost Robert A. BlouinUNC University Gazette, June 2019
Key Partners
What is Translational Science?
https://www.nsf.gov/eng/iip/innovation.pdf
“Valley of Death”
Source: Haendel, et al
Often depicted as an iterative process from basic science and engineering research at the start to production and marketing at the end.(NSF, IBID)
Translational research may involve prototyping, proof-of-concept tests, or scale-up and implementation (NSF 2010b)
Overview
Promise of Precision Medicine
Challenge of Clinical Trials
Burdens and Blessings of Genomic Data
Reducing Healthcare Costs with Emerging Technologies
Precision Medicine: One Size does not fit all
Source: Quiagen
Clinical Trials In a Nutshell
Phase ApproximateSuccess Rate
1 70%
2 33%
3 25-30%
Phase1-3: 6-7%
• Most expensive phase of failure: Phase 4
• Most common source of PMA failure: hepatotoxicity [source: FDA]
Effect on body
Safety10+ subjects, weeks
Effectiveness
100+ subjects, months
Scale-up
1,000+ subjects, years
Long term safety
continuous
Phase 0
Phase 1
Phase 2
Phase 3
Phase 4
NDA: New Drug Approval Application Timeline
• Is it safe & effective? -trials
• Is the labelling appropriate? (prescriptive)
• Are the manufacturing practices adequate?-GMP
How Can We 1. Reduce Clinical Trial Costs and 2. Improve Outcomes?
Genomic Biomarkers Impact Survival
Cell 2015 161, 205-214DOI: (10.1016/j.cell.2015.03.030)
Copyright © 2015 Elsevier Inc. Terms and Conditions
Therapy Response Rate
Toxicity Long term survival
“Standard” chemotherapy
Lower Higher Lower
“Targeted” therapy
Higher Lower Moderate
Clinical Panels
Solid and circulating tumor gene panels
Diagnostics for dermatology, cardiology, immunology, and more
Neonatal carrier screening panel
Clinical exomes, Oncology, neurology, others
Oncomine
Cancer panels, exomes, transcriptomes
In Home Kits
Color: cancer risk
Celmatix: fertility
More In Home Kits
Is Genome Interpretation a Solved Problem?
Genomic variant prioritization, reporting incidental findings: not a solved problem
The American Journal of Human Genetics 2016 98, 1067-
1076DOI: (10.1016/j.ajhg.2016.03.024)
Why Is Genome Interpretation So Challenging?
Genomic Data ExplosionGenomic Data Explosion
Genomic Data ExplosionGenomic Data Explosion
Genomic Data ExplosionGenomic Data Explosion
Genomic Data ExplosionGenomic Data Explosion
Genomic Data ExplosionGenomic Data Explosion
Genomic Data ExplosionGenomic Data Explosion
Genomic Data Explosion
Source: xcode
Genomic Data Explosion
So Much Data!
90% of all data existing today was created in the last two years and…people create 2.5 quintillion bytes of data per day. (To visualize this number, according to a post from Yappn Corp, imagine covering the surface of the Earth with pennies — five times.)
~ Stan Ahalt, RENCI DirectorUNC University GazetteJune 2019
What Can Help Overcome the Challenges of Genome Interpretation?
Standard Bioinformatics Visualizations
https://www.r-bloggers.com/7-interactive-bioinformatics-plots-made-in-python-and-r/
Bill Gates to Francis Collins at ASHG 2017:
Good thing we're in a world now with Infinite compute and storage; geneticists are one of the few people in the world can fill it.
Neural Network “Perceptron”, a.k.a, “Node”
Simplified Neural Network: Many Nodes
f
f
f
f
f
f
f
f
Sharp teeth
Furry
Woof
Is Dog
Sharp teeth
Furry Woof Is Dog
1 1 1 1
1 0 1 1
0 1 0 0
Hidden Layer 1
Hidden Layer 2
Output Layer
Input Layer
Features Target
Training Data
Features
Neural Network: Training a Discriminator
f
f
f
f
f
f
f
f
Pixel1
Pixel2
Pixel3
Is Fake
Pixel1 Pixel2 Pixel3 Is Fake
1 1 1 1
1 0 1 1
0 1 0 0
Hidden Layer 1
Hidden Layer 2
Output Layer
Input Layer
Features Target
Training Data
Generative Adversarial Networks
Real Data
Discriminator (D)
Generator
NoiseGeneratedData
Legend
Neural Net
Was D fooled?
Generative Adversarial Networks
Real Data
Discriminator (D)
Generator
NoiseGeneratedData
Legend
Neural Net
Was D fooled?
ForgerGoal: Minimize Discriminator’s accuracy
DetectiveGoal: Distinguish fake from real
Can a NN learn how to make realistic data without labels?
ThisPersonDoesNotExist.com
WhichFaceIsReal.com
WhichFaceIsReal.com
Preliminary Simulated RNASeq Data
Manuscript In Preparation
Designing DNA with GANs
GAN (left) can generate “designs” that lay well over the known data, but expand the space of what is known with knew DNA sequence candidates (right)
Killoran, Lee, Delong, Duvenaud, Frey, 2017
Can a Neural Net teach us what it learned?
Example of Self-Explaining NN: DCell
Ma, Nature Methods, 2018
Dcell’s Neural Network architecture mimics the known hierarchical organization of the data and the perceptronshelp to explain the results.
Review
Promise of Precision Medicine
Challenge of Clinical Trials
Burdens and Blessings of Genomic Data
Reducing Healthcare Costs with Emerging Technologies
Take-aways
Promise of Precision Medicine
Right drug, right patient, right timeChallenge of Clinical Trials:
Costly, time consuming, failures – need biomarkersBurdens and Blessings of Genomic Data
New biomarkers, but so much data!Reducing Healthcare Costs with Emerging Technologies:
GANs can learn genomic data distributions,
Make them report back findings
Next steps: How can you help?
1. Learn oneDevelop knowledge about Analytical and Visualization tools
GANS original source: Goodfellow, 2014Neural Networks: Coursera Applied Bioinformatics: SILS 890-259 (contact me for pre-requisites)
Practice the languagePython/R courses: Solo Learn, Data Camp
2. Do oneInternship/Practicum
3. Teach oneFor RENCI, Contact me – be patient, but be persistent!krobasky@renci.org
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