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How Artificial Intelligence is Reducing Costs and Improving Outcomes in Pharma R&D


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Artificial Intelligence in Pharma

Published in: Data & Analytics
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How Artificial Intelligence is Reducing Costs and Improving Outcomes in Pharma R&D

  1. 1. How Artificial Intelligence is Reducing Costs and Improving Outcomes in Pharma R&D
  2. 2. Executive Summary + Tomorrow’s challenge is to develop new medicines that can prevent or cure currently incurable diseases + Refresh the pharmaceutical drug pipeline, by taking better advantage of the available data with new algorithms and disruptive technologies + Artificial Intelligence (AI) in Pharma R&D will help to identify and validate new drug targets, support early identification of safety and efficacy issues, and improve patient stratification
  3. 3. Artificial Intelligence in Pharma R&D Use Cases
  4. 4. Human Intelligence Artificial Intelligence Average Intelligent Sub human Par human High human Super humanPerformance AI comparison with human performance Borderline performs better than all humans
  5. 5. Traditional Programming Machine Learning
  6. 6. Breast Cancer Diagnoses - 2017 Pathologist Performance A.I. Performance 73% 92% Doctors often use additional tests to find or diagnose breast cancer The pathologist ended up spending 30 hours on this task on 130 slides A closeup of a lymph node biopsy.
  7. 7. Skin Cancer Diagnoses - 2016 Pathologist Performance A.I. Performance 96,5% 97,1% If found early 95% of skin cancers can be treated successfully Pathologist + A.I. 99,5%
  8. 8. Early Diagnosis of Congestive Heart Failure A machine learning example from Georgia Tech demonstrated that machine-learning algorithms could look at many more factors in patients’ charts than doctors, and by adding additional features there was a substantial increase in the ability of the model to distinguish people who have CHF from people who don’t. Human performance A.I. performance
  9. 9. Predict Cardiac Failure Before It’s Diagnosed In quantitative evaluation, our proposed representation significantly improves the predictive modeling performance for onset of heart failure (HF), where classification methods achieve up to 23% improvement in area under the ROC curve (AUC) using this proposed representation.
  10. 10. AI results get better with
  11. 11. Machine Learning Problem Types
  12. 12. Distributed Machine Learning in Data Center Data Size Model Size Model parallelism Single machine Data center Data parallelism training very large models exploring several model architectures, hyper- parameter optimization, training several independent models speeds up the training
  13. 13. Machine Learning Workflow Collect data Data Preprocessing Search Analysis Model Training Re- simulation Reports Results Model Deployment Training data Model Testing Train Test Loop Test data Model Feedback Loop
  14. 14. Think Big Business Strategy Data Strategy Technology Strategy Agile Delivery Model Business Case Validation Prototypes, MVPs Data Exploration Data AcquisitionStart Small Value Proposition
  15. 15. thank you 15