AI for COVID-19 - Q42020 update
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With half of the world’s population lacking access to healthcare services, and 30% of the adult population in the US having inadequate health insurance coverage to get even basic access to services, it should have been clear that a pandemic like COVID-19 would strain the global healthcare system way over its maximum capacity. In this context, many are trying to embrace and encourage the use of telehealth as a way to provide safe and convenient access to care. However, telehealth in itself can not scale to cover all our needs unless we improve scalability and efficiency through AI and automation. In this talk, we will describe how our work on combining latest AI advances with medical experts and online access has the potential to change the landscape in healthcare access and provide 24/7 quality healthcare. Combining areas such as NLP, vision, and automatic diagnosis we can augment and scale doctors. We will describe our work on combining expert systems with deep learning to build state-of-the-art medical diagnostic models that are also able to model the unknowns. We will also show our work on using language models for medical Q&A . More importantly, we will describe how those approaches have been used to address the urgent and immediate needs of the current pandemic.
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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
- 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?
- 7. Breakthroughs in AI & healthcare
- 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.
- 17. Our approach to COVID-19
- 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
- 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