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Xavier Amatriain, Cofounder & CTO, Curai at MLconf SF 2017


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ML to Cure the World:
The practice of medicine involves diagnosis, treatment, and prevention of diseases. Recent technological breakthroughs have made little dent to the centuries-old system of practicing medicine: complex diagnostic decisions are still mostly dependent on “educated” work-ups of the doctors, and rely on somewhat outdated tools and incomplete data. All of this often leads to imperfect, biased, and, at times, incorrect diagnosis and treatment.

With a growing research community as well as tech companies working on AI advances to medicine, the hope for healthcare renaissance is definitely not lost. The emphasis of this talk will be on ML-driven medicine. We will discuss recent AI advancements for aiding medical decision including language understanding, medical knowledge base construction and diagnosis systems. We will discuss the importance of personalized medicine that takes into account not only the user, but also the context, and other metadata. We will also highlight challenges in designing ML-based medical systems that are accurate, but at the same time engaging and trustworthy for the user.

Bio: Xavier Amatriain is currently co-founder and CTO of Curai, a stealth startup trying to radically improve healthcare for patients by using AI. Previous to this, he was VP of Engineering at Quora, and Research/engineering Director at Netflix, where he led the team building the famous Netflix recommendation algorithms. Before going into leadership positions in industry, Xavier was a research scientist at Telefonica Research and a research director at UCSB. With over 50 publications (and 3k+ citations) in different fields, Xavier is best known for his work on machine learning in general and recommender systems in particular. He has lectured at different universities both in the US and Spain and is frequently invited as a speaker at conferences and companies.

Published in: Technology

Xavier Amatriain, Cofounder & CTO, Curai at MLconf SF 2017

  1. 1. Machine Learning to cure the World Xavier Amatriain Curai MLConf SF ‘17
  2. 2. Medicine is hard(er) ● Doctors have ~15 minutes to capture information* about a patient, diagnose, and recommend treatment ● *Information ○ Patient’s history ○ Patient’s symptoms ○ Medical knowledge ■ Learned years ago ■ Latest research findings ■ Different demographics ● Data is growing over time, so is complexity ● Very hard for doctors to “manually” personalize their “recommendations”
  3. 3. Medical Diagnosis ● Diagnosis (R.A. Miller 1990): ○ Mapping from patient’s data (history, examination, lab exams…) to a possible condition. ○ It depends on ability to: ■ Evoke history ■ Surface symptoms and findings ■ Generate hypotheses that suggest how to refine or pursue different hypothesis ○ In a compassionate, cost-effective manner
  4. 4. Cost of medical errors ● 400k deaths a year can be attributed to medical errors as well as 4M serious health events ○ This compares to 500k deaths from cancer or 40k from vehicle accidents ● Almost half of those events could be preventable
  5. 5. How to improve medical care? ● Automate processes through AI/ML ● Use of (big) data ● More/better personalization ● Improved user experience both for patients and doctors Does this sound familiar?
  6. 6. Medical Decision Support + Knowledge Bases Personalization NLP Multimodal input ML/AI Medical System
  7. 7. ML/AI Medical System Personalization NLP Multimodal input Medical Decision Support + Knowledge Bases
  8. 8. Medical Decision + Knowledge Bases Medical Knowledge Bases encode years of Doctor Expertise Doctor Expertise Medical Research
  9. 9. An example: Internist-1/QMR/Vddx ● Internist (1971) led by Jack Myers considered (one of) the best clinical diagnostic experts in the US ○ University of Pittsburgh, Chairman of the National Board of Medical Examiners, President of the American College of Physicians, and Chairman of the American Board of Internal Medicine ● Process for adding a disease requires 2-4 weeks of full-time effort and doctors reading 50 to 250 relevant publications
  10. 10. An example: Internist-1/QMR/Vddx
  11. 11. ML/AI Approaches to Diagnosis ● Early DDSS based on Bayesian reasoning (60s-70s) ● Bayesian networks (80s-90s) ● Neural networks (lately)
  12. 12. Health knowledge graphs
  13. 13. ML/AI Medical System Medical Decision Support + Knowledge Bases Personalization Multimodal input NLP
  14. 14. Ontologies ● Snomed Clinical Terms ○ Computer processable collection of medical terms used in clinical documentation and reporting. ○ Clinical findings, symptoms, diagnoses, procedures, body structures, organisms substances, pharmaceuticals, devices... ● ICD-10 ○ 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD) ○ Codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes ● UMLS ○ Compendium of many controlled vocabularies ○ Mapping structure among vocabularies ○ Allows to translate among the various terminology systems
  15. 15. NLP ● Understanding what doctors and patients say ● Extracting knowledge from medical texts ● ...
  16. 16. Electronic Health Records ● EHR/EMRs include digital information about patients encounters with doctors or the health system
  17. 17. NLP Methods and algorithms to extract meaning and knowledge from unstructured text Patient understanding The Language of Medicine Doctor’s Notes Medical research publications
  18. 18. ML/AI Medical System Clinical Decision Support + Medical Knowledge Bases Personalization NLP Multimodal input
  19. 19. Multimodal input We will include many different signals besides direct patient input Speech interfaces Image recognition Sensors/lab data
  20. 20. Inputs to DDSS ● Improve accuracy of signals input to diagnostic systems by using AI/ML techniques
  21. 21. ML/AI Medical System Clinical Decision Support + Medical Knowledge Bases NLP Multimodal input Personalization
  22. 22. Precision medicine ● Precision medicine (NIH): "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." ● Term is relatively new, but concept has been around for many years. ○ E.g. blood transfusion is not given from a randomly selected donor
  23. 23. Personalization The best and most relevant information “for you” Patient profile & medical history
  24. 24. Personalization The best and most relevant information “for you” Patient profile & medical history Biological markers & other lab data
  25. 25. Lessons learned from Recsys
  26. 26. Clinical Decision Support + Medical Knowledge Bases ML/AI Medical System Personalization NLP Multimodal input
  27. 27. What is different from other domains? ● Cost of errors ● We care about causality ● Implicit user signals not enough ● Need of conversational approaches ○ Importance of eliciting information ○ Importance of communicating outcomes ● Complex interactions between diseases and symptoms, including temporal sequences
  28. 28. What are we doing? ● Building an awesome team (Netflix, Quora, Facebook, Google, Microsoft, Uber, Stanford…) ● Combining AI/ML and best product/UX practices to build a service that revolutionizes healthcare by empowering patients to make their own decisions ● Leveraging pre-existing resources and state-of-the-art approaches ● We are stealth, too soon to say too much about what we have
  29. 29. Challenges ● Algorithmic: e.g. combining expert rule-based and ML ● Data: quality, sparsity, and bias in data ● UX: trustworthiness and engagement of the system, incentives… ● Legal ● … It’s about time we overcome all of these.
  30. 30. References
  31. 31. ● “Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base” . Shwe et al. 1991. ● “Computer-assisted diagnostic decision support: history, challenges, and possible paths forward” Miller. 2009. ● “Mining Biomedical Ontologies and Data Using RDF Hypergraphs” Liu et al. 2013. ● “Health Recommender Systems: Concepts, Requirements, Technical Basics & Challenges”, Wiesner & Pfeifer, 2014. ● “A ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care” Longhurst et al. 2014. ● “Building the graph of medicine from millions of clinical narratives” Finlayson et al. 2014. ● “Comparison of Physician and Computer Diagnostic Accuracy” Semigran et al. 2016. ● “Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization”. Joshi et al. 2016. ● “Clinical Tagging with Joint Probabilistic Models” . Halpern et al. 2016. ● “Deep Patient: An Unsupervised Representation to Predict the Future of Patients from EHR”. Miotto et al. 2016. ● “Learning a Health Knowledge Graph from Electronic Medical Records” Rotmensch et al. 2017. ● “Clustering Patients with Tensor Decomposition”. Ruffini et al. 2017. ● “Patient Similarity Using Population Statistics and Multiple Kernel Learning”. Conroy et al. 2017. ● “Diagnostic Inferencing via Clinical Concept Extraction with Deep Reinforcement Learning”. Ling et al. 2017. ● “Generating Multi-label Discrete Patient Records using Generative Adversarial Networks” Choi et al. 2017 ● Suresh, H., Szolovits, P., & Ghassemi, M. (2017, March 20). The Use of Autoencoders for Discovering Patient Phenotypes. References
  32. 32. Yes, we’re hiring!