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complex diagnostic decisions are still mostly dependent on “educated” work-ups of the doctors, and rely on somewhat outdated tools and incomplete data.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.

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

7. ML/AI
Medical
System
Personalization
NLP
Multimodal input
Medical Decision Support +
Knowledge Bases

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

10. An example: Internist-1/QMR/Vddx

11. ML/AI Approaches to Diagnosis
● Early DDSS based on Bayesian reasoning (60s-70s)
● Bayesian networks (80s-90s)
● Neural networks (lately)

12. Health knowledge graphs

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

25. Lessons learned from Recsys

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

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.

31. References

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

33. Yes, we’re hiring!