AI for COVID-19 - Q42020 update

1. AI for COVID-19: An online virtual care approach (Q42020 update) Xavier Amatriain (@xamat) https://curai.com 11/06/2020

2. ToC ● ● ○ ● ○ ○ 2

3. ● >50% world with no access to essential health services ○ ~30% of US adults under-insured ● ~15 min. to capture information, diagnose, recommend treatment ● 30% of the medical errors causing ~400k deaths a year are due to misdiagnosis Healthcare access, quality, and scalability shortage of 120,000 physicians by 2030

4. Towards an AI powered learning health system ● Mobile-First Care, always on, accessible, affordable ● AI + human providers in the loop for quality care ● Always-Learning system ● AI to operate in-the-wild (EHR) FEEDBACK DATA MODEL AI-augmented medical conversations

5. How does this look like?

6. ToC ● ● ○ ● ○ ○ 6

7. Breakthroughs in AI & healthcare

8. Research at Curai

9. Research areas at Curai ● Medical Reasoning and Diagnosis ○ Learning from the Experts: combining expert systems and deep learning ● NLP ○ Medical dialogue summarization ○ Transfer Learning for Similar Questions ○ Medical entity recognition: An active learning approach ● Multimodal healthcare AI ○ Few-shot dermatology image classiﬁcation

10. General principles 1. Extensible AI a. Data feedback loops b. Incrementally/iteratively learn from “physician-in-the-loop” 2. Domain Priors + Data 3. AI in the wild a. Uncertainty in prediction b. Enables fall-back to “physician-in-the-loop”

11. ToC ● ● ○ ● ○ ○ 11 To appear at EMNLP-Findings 2020

12. Local to global summarization DOCTOR: what color is the phlegm? PATIENT: dark green dark green DOCTOR: do you have chest pain while coughing? PATIENT: yes and breathing . pretty much all the time DOCTOR: just clarifying. is your chest pain constant? or is it on & off ? PATIENT: pretty constant it is constant pain it does not turn off. Summaries dark green phlegm chest pain while coughing and breathing all the time constant chest pain ● Local structures in the conversation / Local summaries to global summaries

13. Designing the Model ● Hybrid model between abstractive and extractive summarization ○ Copy more (maintain integrity of domain) ○ Focus on medical concepts ○ Get negations and aﬃrmations right ● Extension of the Pointer Generator Network (Abigail See et al. 2017) ○ Loss Contributions: Penalized generation to focus on copying ○ Architecture Contributions: Negations and medical concepts in attention mechanism and ﬁnal probability distributions. ○ Formulated new evaluation criteria: Automated and doctor metrics

14. Results Copied from Doctor Copied from Patient Generated Proposed ModelFine-tuned pointer generator network

15. Using GPT-3 to Supplement Training Data Snippet Trained on Human Trained on GPT-3 Trained on Human + GPT-3 DR: Have you ever been tested for any underlying health conditions such as diabetes, hypothyroidism or polycystic ovarian syndrome? PT: No PT: I have been told I have prediabetes Has not been tested for any underlying health conditions. Hasn't tested for any underlying health conditions such as diabetes, hypothyroidism or polycystic ovarian syndrome Has not been tested for any underlying health conditions. Has been told has prediabetes. DR: Do you have pus appearing discharge from the site? PT: Yes. If the bubbles pop it leaks out a watery substance Has pus appearing from the sire. Pus appearing from the site Pus discharge from the site. If bubbles pop it leaks out a substance.

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17. Our approach to COVID-19

18. Personalized Diagnostic Assessment

19. AI + Providers in the loop + Daily follow up

20. Feedback data from COVID-assessment female middle-age cough headache nose discharge cigarette smoking hospital personnel Moderate COVID-19 risk female middle-age cough headache chest pain difficulty breathing nose discharge cigarette smoking hospital personnel foreign travel history High COVID-19 risk Positive examples for COVID-19

21. ToC ● ● ○ ● ○ ○ 21 To appear at NeurIPS’ 2020 ML4H workshop

22. ML + Expert systems for Dx models female middle aged fever cough Influenza 16.9 bacterial pneumonia 16.9 acute sinusitis 10.9 asthma 10.9 common cold 10.9 influenza 0.753 bacterial pneumonia 0.205 asthma 0.017 acute sinusitis 0.008 pulmonary tuberculosis 0.007 Inputs DDx with expert system DDx with ML model Expert system Clinical case simulator Clinical cases DDx ML model Common cold UTI Acute bronchitis Female Middle-aged Chronic cough Nasal congestion Other data (e.g. EHR)

23. COVID-aware modeling Expert system Clinical case simulator Clinical cases with DDx ML model Common cold UTI Acute bronchitis Female Middle-aged Chronic cough Nasal congestion COVID-19 assessment data COVID-19 COVID-19 female middle-age cough headache nose discharge cigarette smoking hospital personnel

24. Examples Inputs DDx before COVID DDx after COVID female middle aged fever cough healthcare worker influenza bacterial pneumonia asthma COVID-19 influenza bacterial pneumonia female middle aged fever cough nasal congestion influenza adenovirus infection bacterial pneumonia influenza COVID-19 adenovirus infection

25. ToC ● ● ○ ● ○ ○ 25 Proceedings of the 2020 ACM SIGKDD

26. Question similarity for COVID FAQs ● Transfer learning ● Double-ﬁnetuned BERT model ● Handle data sparsity ● Medical domain knowledge through an intermediate QA binary task Does A answer Q? Q A Pretrained BERT BERT Q1 similar to Q2? Q1 Q2 BERT

27. COVID-19: Automated FAQ Matching ● ● User Questions Matching Questions in our FAQ (Answer not shown here for brevity) When do COVID symptoms start after exposure? How long is it between when a person is exposed to the virus and when they start showing symptoms? Currently I’m experiencing a cough and slight chest pain. Should I just stay at home? At what point will I know I have to go to the ER? When should you go to the emergency room? 27

28. Product in action 28

29. Conclusions ● Healthcare needs to scale quickly, and this has become obvious in a global pandemic like the one we are facing ● The only way to scale healthcare while improving quality and accessibility is through technology and AI ● AI cannot be simply “dropped” in the middle of old workﬂows and processes ○ It needs to be integrated in end-to-end medical care beneﬁtting both patients and providers https://curai.com/work

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