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Machine Learning
to cure the World
Xavier Amatriain
Curai
MLConf SF ‘17
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”
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
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
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?
Medical Decision
Support +
Knowledge Bases
Personalization
NLP
Multimodal input
ML/AI
Medical System
ML/AI
Medical
System
Personalization
NLP
Multimodal input
Medical Decision Support +
Knowledge Bases
Medical Decision +
Knowledge Bases
Medical Knowledge Bases encode
years of Doctor Expertise
Doctor Expertise
Medical Research
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
An example: Internist-1/QMR/Vddx
ML/AI Approaches to Diagnosis
● Early DDSS based on Bayesian reasoning (60s-70s)
● Bayesian networks (80s-90s)
● Neural networks (lately)
Health knowledge graphs
ML/AI
Medical
System
Medical Decision
Support +
Knowledge Bases
Personalization Multimodal input
NLP
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
NLP
● Understanding what doctors and patients say
● Extracting knowledge from medical texts
● ...
Electronic Health Records
● EHR/EMRs include digital information
about patients encounters with doctors
or the health system
NLP
Methods and algorithms to
extract meaning and knowledge
from unstructured text
Patient
understanding
The Language
of Medicine
Doctor’s
Notes
Medical research
publications
ML/AI
Medical
System
Clinical Decision
Support +
Medical Knowledge
Bases
Personalization
NLP
Multimodal input
Multimodal input
We will include many different
signals besides direct patient
input
Speech interfaces
Image recognition
Sensors/lab data
Inputs to DDSS
● Improve accuracy of signals input to diagnostic systems by using AI/ML
techniques
ML/AI
Medical
System
Clinical Decision
Support +
Medical Knowledge
Bases
NLP
Multimodal input
Personalization
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
Personalization
The best and most relevant
information “for you”
Patient profile &
medical history
Personalization
The best and most relevant
information “for you”
Patient profile &
medical history
Biological markers &
other lab data
Lessons learned from Recsys
Clinical Decision
Support +
Medical Knowledge
Bases
ML/AI
Medical
System
Personalization
NLP
Multimodal input
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
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
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.
References
● “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. arXiv.org.
References
Yes, we’re hiring!

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ML to cure the world

  • 1. Machine Learning to cure the World Xavier Amatriain Curai MLConf SF ‘17
  • 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. 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. 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. 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. Medical Decision Support + Knowledge Bases Personalization NLP Multimodal input ML/AI Medical System
  • 8. Medical Decision + Knowledge Bases Medical Knowledge Bases encode years of Doctor Expertise Doctor Expertise Medical Research
  • 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
  • 11. ML/AI Approaches to Diagnosis ● Early DDSS based on Bayesian reasoning (60s-70s) ● Bayesian networks (80s-90s) ● Neural networks (lately)
  • 13. ML/AI Medical System Medical Decision Support + Knowledge Bases Personalization Multimodal input NLP
  • 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. NLP ● Understanding what doctors and patients say ● Extracting knowledge from medical texts ● ...
  • 16. Electronic Health Records ● EHR/EMRs include digital information about patients encounters with doctors or the health system
  • 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. ML/AI Medical System Clinical Decision Support + Medical Knowledge Bases Personalization NLP Multimodal input
  • 19. Multimodal input We will include many different signals besides direct patient input Speech interfaces Image recognition Sensors/lab data
  • 20. Inputs to DDSS ● Improve accuracy of signals input to diagnostic systems by using AI/ML techniques
  • 21. ML/AI Medical System Clinical Decision Support + Medical Knowledge Bases NLP Multimodal input Personalization
  • 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. Personalization The best and most relevant information “for you” Patient profile & medical history
  • 24. Personalization The best and most relevant information “for you” Patient profile & medical history Biological markers & other lab data
  • 26. Clinical Decision Support + Medical Knowledge Bases ML/AI Medical System Personalization NLP Multimodal input
  • 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.
  • 29. 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
  • 30. 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.
  • 32. ● “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. arXiv.org. References