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Bharath Sudharsan, ArmadaHealth - NLP in Aid of Critical Health Decisions - H2O World San Francisco


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This session was recorded in San Francisco on February 5th, 2019 and can be viewed here:

Bio: Bharath Sudharsan is the Director of Data Science and Innovation at ArmadaHealth. He leads a team of data analysts who develop and implement AI tools that are at the heart of objective and data-driven specialty care referral process synonymous with ArmadaHealth. Bharath has also held positions at Fractal Analytics and Quanttus, Inc. and WellDoc, Inc. He is also the founder of Geetha, LLC, a provider of best in class healthcare analytics consultation including implementation of NLP and AI.

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Bharath Sudharsan, ArmadaHealth - NLP in Aid of Critical Health Decisions - H2O World San Francisco

  1. 1. Emotional AI Name Bharath Sudharsan Title Emotional AI Company ArmadaHealth #EmotionalAI #H2OWORLD
  2. 2. PreviousNext 2 Hello from ArmadaHealth and H20 Bharath Sudharsan Director of Data Science ArmadaHealth Sudalai Raj Kumar NLP Lead H20 Ryan Kosiba Data Scientist ArmadaHealth Shwetank Sonal Data Scientist ArmadaHealth Shruti Padmanabhan Data Scientist ArmadaHealth Andrew Corson Software Engineer ArmadaHealth
  3. 3. PreviousNext 3 What if AI could understand human emotions? And why? 1 It is no longer a science fiction 2 It is often associated with facial and speech-based approaches 3 Text-based approach is more mainstream, given we spend most time typing than talking Emotion AI AI Understanding Human Emotions could have real and measurable impact in Healthcare.
  4. 4. PreviousNext 4 Every Technology needs a compelling and meaningf ul vehicle t o reach t ipping point
  5. 5. PreviousNext 5 What’s the problem? Consumers are not equipped to navigate the complex and confusing healthcare system. Finding a good physician solves major problems.. CAN SAVE LIVES Patient is here The right doctor for patient is here. Specialty care access problem 30,000+ diseases 900,000 specialists 100s of subspecial es Varying quality of training & outcomes No transparency to the pa ent or referring physician Huge health systems: Who’s in-network? 3rdleading cause of death: medical errors5 50%of consumers will receive incorrect treatment in their lifetime4 Adult patients receive an incorrect diagnosis each year2 12m 34%of consumers self-refer to specialists3 $29b Lost each year in preventable waste1
  6. 6. PreviousNext 6 People used to ask only other people for help People started asking technology for help Technology can help understand other’s experiences and bring in the right help  Not objective enough  Not convenient enough  Not trustworthy  Not descriptive/experiential enough  Objective, trustworthy and descriptive When we need help… Patient Reported Experience Measures (PREMs)
  7. 7. PreviousNext 7 What’s the Solution? Objective approach to leverage wisdom of experts, AI and people Wisdom-based Approach
  8. 8. PreviousNext 8 What’s the problem with current approaches? 100s of Reviews to sift through Keyword-based match is highly inaccurate Ratings are highly subjective Doesn’t answer consumer’s key questions
  9. 9. PreviousNext 9 Key Questions consumers have while reading reviews….. Do other patients get better?#01 Outcomes, including ease of getting back to normal quality of life, after a major surgery, is a key focus. Positive Negative Treatment Outcome Is the physician and office staff friendly?#02 Patients and their family undergo a lot of pain, anxiety and stress during an episode. It is important for their care team to be friendly and understanding. Positive Negative Attitude Do they communicate treatment options clearly and involve the patient in decision making? #03 Shared decision making is key to better outcomes, and patient empowerment. Positive Negative Communication Patient Reported Experience Measures (PREMs)
  10. 10. PreviousNext 10 It’s a NLP approach that gives a general idea about the positive, neutral, and negative sentiment of texts 01. It is important to understand the context of each sentence 02. There are multiple layers of meaning: Rhetorical devices like sarcasm, irony, and implied meaning can mislead 03. It is a hard challenge for language technologies, and achieving good results is much more difficult Sentiment Analysis
  11. 11. PreviousNext 11Example Don't go to this doctor at all!! Uncaring, does not explain to the patient. God knows if he really did see what needed to be done on my knee. Did not say anything before and after the operation. I had to go through multiple surgeries to get back to being normal. Treatment Outcome Communication Positive Negative Vs.
  12. 12. PreviousNext 12 Key Takeaways 01. Context matters a lot 02. Need to look beyond word-based approaches 03. Aspects are important, to move beyond overall sentiment scores
  13. 13. PreviousNext 13 Our Initial Model – Treatment Positive/Negative 1500 labeled rows 14,000 unlabeled reviews Used Driverless AI NLP features and word2Vec approach
  14. 14. PreviousNext 14 Our Other Models Was unstable due to class imbalance Used Driverless AI NLP features and word2Vec approach Physician communication Model Office Staff Attitude Model
  15. 15. PreviousNext 15 Language Models What is a language model? • Language model is a model to calculate probability distribution over sequences of tokens in a (natural) language • Make use of distributed representations, that is, low-dimension vector representations of tokens that can mitigate the curse of dimension How can it help? • Because they can empower a very efficient way of learning, called transfer learning. Transfer learning helps you to train, often unsupervised on a lot of data, then to apply the pre-trained model to efficiently learn downstream tasks. What are some examples? • Word2vec is one example of transfer learning, a universal one. • Researchers since then have been looking for even better way of transfer learning, the current state-=of-the-art ones are ELMo, ULMFit, OpenAI Transformer, and BERT.
  16. 16. PreviousNext 16 Language Model – Architectures and Approaches Left to right Right to left Shallow Bidirectional And deep Architectures – LSTM Vs. Transformer Attention Approach
  17. 17. PreviousNext 17 Comparison
  18. 18. PreviousNext 18 BERT-based Model - Supervised 4000 labeled rows 14,000 unlabeled reviews Models Results Treatment Outcome 77% (vs DAI – 73%) PhysicianAttitude 89.4% Physician Communication 89% Office Staff Attitude 83% Office Staff Communication 84% 4000 labeled rows 1M+ Unlabeled domain-specific rows Subjectivity Detection Fake Reviews using Linguistic patterns and models
  19. 19. PreviousNext 19 Next Frontier – Personality and Emotion-centric analysis Pleasantness Attention Sensitivity Aptitude Ecstasy Vigilance Rage Admiration Joy Anticipation Anger Trust Serenity Interest Annoyance Acceptance Pensiveness Distraction Apprehension Boredom Sadness Surprise Fear Disgust Grief Amazement Terror Loathing A truly consumer-centric empathy-based AI Understanding the person behind the patient
  20. 20. PreviousNext 20 A truly consumer-centric empathy-based Healthcare experience using AI Understanding the person behind the patient Next Frontier in Healthcare
  21. 21. PreviousNext 21 Summary Summary • Physician reviews is a compelling and impactful use case for sentiment analysis • Reviews need a both objective and descriptive approach – Expert-based and AI-based • Context is key to sentiment analysis • Language models are better at understanding and preserving context • BERT model, given its bidirectional approach, outperforms other models and looks very promising • Understands context better • Aspect-based approaches add more meaning • Emotion detection via text is the next major frontier for Patient Reported Experience Measures (PREMs) and is key to a truly value-based and wisdom- based care
  22. 22. PreviousNext 22