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AI & Healthcare @ AIISC: May 2021 Snapshot

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May 2021 snapshot of some of the Research and Collaborations in dHealth/personalized health, public health, epidemiology, biomedicine at the AI Institute of the University of South Carolina [AIISC]

May 2021 snapshot of some of the Research and Collaborations in dHealth/personalized health, public health, epidemiology, biomedicine at the AI Institute of the University of South Carolina [AIISC]

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AI & Healthcare @ AIISC: May 2021 Snapshot

  1. 1. Healthcare Research @ AI Institute: dHealth, public health, epidemiology, biomedicine Overview Presentation to the MUSC’s AI Hub and others May 2021 Amit Sheth, Founding Director http://aiisc.ai
  2. 2. © 2021 NAVER. All rights reserved.
  3. 3. “ 3 AIISC in core AI areas, and interdisciplinary AI/AI applications >> 25 researchers including 4 faculty (6 in Fall 2021), 2-3 postdocs, ~20 PhD students, >10 MS/BS and several interns/associates
  4. 4. © 2021 NAVER. All rights reserved.
  5. 5. D-Health, health informatics, public health, epidemiology – sample collaborations (Pending & PLANNED Submissions only) •College of Medicine/Prisma/Prisma-Upstate •Mental health [M Natarajan], Addiction [A. Litwin], Asthma [R. Dawson], Diabetes & Obesity [L. Knight], Hypertension - Diet & Nutrition [S. Donevant], Neutropenia [S. Craemer] •College of Pharmacy: EOCRC [P. Backhaults, L. Hofseth, et al] •College of Nursing: COVID-19 mobile app [R. Hughes, S. Donevant], mental health chatbot [R. Hughes, S. Donevant, P. Raynor] •Arnold School of Public Health: Mental Health [S. Qiao], Healthcare Big Data - Education & Training [X. Li, et. al.] •USCAND, Inst of Mind & Brain: Neuroscience [R. Desai] and neurodevelopmental diseases [J. Bradshaw, J. Roberts] •CEC- IIT, BME, CSE: Health IT/Smart Health [E. Regan], UI/UX [D. Wu]; Health mApp [N. Boltin], (several in CSE).
  6. 6. Projects ➢ KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care (NICHD) ➢ mHealth to Improve Carbohydrate Counting Accuracy in Pediatric Type 1 Diabetes ➢ Improving mental health of COVID-19 patients with an Artificial Intelligence-based chatbot ➢ Personalized Virtual Health Assistant Enabled by Knowledge-infused Reinforcement Learning for Adaptive Mental Health Self-management ➢ Characterizing and supporting help seekers on social media using expert-in-the-loop learning ➢ Modeling Social Behavior for Healthcare Utilization in Depression (NIMH) ➢ eDarkTrends : Monitoring Cryptomarkets to Identify Emerging Trends of Illicit Synthetic Opioids Use (NIDA) ➢ Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use (NIDA) ➢ Innovative NIDA National Early Warning System Network (iN3) (NIDA) ➢ PREDOSE: PREscription Drug abuse Online Surveillance and Epidemiology (NIDA) ➢ Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest (NSF) ➢ Discrepancies in Diagnosis and Treatment of Cardiovascular Disease Based on Sex and Gender to Improve Women’s Health (NHLBI) ➢ Early Onset of Colorectal Cancer ➢ Digestive Inflation Index ➢ more ...
  7. 7. Types of Healthcare Data ◎ EMR ◎ Social Media (Reddit, Twitter,Web Forums) ◎ Conversations: Patient- Clinician, Virtual Health Assistant-Patient ◎ Patient Generated: Wearable/sensor data, mApp data ◎ Images: food, fMRI AI Techniques and Technologies ◎ Knowledge Graphs/Ontologies (contextualization, personalization, abstraction) ◎ NLP/NLU ◎ Machine Learning/Deep Learning (RL, GAN, CNN, LSTM,....) ◎ Conversational AI, Q/A ◎ mApp, Virtual Health Assistants (Chatbots) ◎ Health sensors/IoTs/mobile devices
  8. 8. Medical Conditions/Healthcare Challenges addressed ◎ Asthma ◎ Mental Health ◎ Addiction ◎ COVID-19 ◎ Cardiovascular Disease ◎ Type 1 Diabetes in Children ◎ Adult Diabetes - Hypertension ◎ Neutropenia ◎ Sleep Disorders ◎ Gender and Race Disparity ◎ Demographics ◎ SDOH ◎ Drug Design Partners: Weill Cornell, UCSF Medical, Prisma-Health, Addiction Research Center, UofSC Medical/Pharma/Public Health/Nursing; Wright State Physicians, ….
  9. 9. Unique Value Propositions and Strengths [for Health Apps] ◎ Development of Knowledge Graphs ◎ Knowledge-infused (Deep) Learning and Knowledge- infused NLP: Explainable AI ◎ Conversational AI/collaborative agents ◎ Augmented Personalized Health
  10. 10. 10 Health Knowledge Graph Drug Abuse Ontology Lokala U, Daniulaityte R, Lamy F, Gaur M, Thirunarayan K, Kursuncu U, Sheth. A. (2020). DAO: An ontology for substance use epidemiology on social media and dark web. JMIR. https://doi.org/10.2196/preprints.24938 https://scholarcommons.sc.edu/aii_fac_pub/356/ [Shah and Sheth US patent 2015]
  11. 11. Knowledge-Infused Learning with Semantic, Cognitive, Perceptual Computing Framework 11 Overarching Theory Knowledge Domain (Ontology) Personalized KG Multisensory Sensing & Multimodal Data Interactions Images Text Speech Videos IoTs Natural Language Processing, Machine with Deep Learning AUGMENTED PERSONALIZED HEALTH (APH) Modeling broader disease context, and personalized user behavior Reasoning & decision- making framework Minimize data overload, assist in making choices, appraisal, recommendations TEDx talk: Augmented Health with Personalized Data and AI
  12. 12. 12 Contextualization and Personalization kBOT initiates greeting conversation. Understands the patient’s health condition (allergic reaction to high ragweed pollen level) via the personalized patient’s knowledge graph generated from EMR, PGHD, and prior interactions with the kBot. Generates predictions or recommended course of actions. Inference based on patient’s historical records and background health knowledge graph containing contextualized (domain-specific) knowledge. Figure: Example kBot conversation which utilizes background health knowledge graph and patient’s knowledge graph to infer and generate recommendation to patients. ★ Conversing only information relevant to the patient Context enabled by relevant healthcare knowledge including clinical protocols.
  13. 13. Why Knowledge Infused Learning (K-IL)? By changing the inputs, it can enrich the representation (E.g. Radicalization on Social Media) By changing parameters, we can control the learned patterns/correlations to adhere to the knowledge. Deep Infusion would allow us finer grained control over learned patterns to ensure adherence to knowledge at every step of the hierarchy Explanations easy to derive from the KG used 13 Contextual Modeling to analyze Radicalization on Social Media (Hate)
  14. 14. Health-e Gamecock COVID-19 daily status check (on Apple Store)
  15. 15. Health-e Gamecock 15
  16. 16. What? Comprehensive, Customizable, Adaptive ● App/chatbot for individuals ○ Cohort 1: College of Nursing (testing, research) ○ Cohort 2: Students, Staff, Faculty ● Dashboards for different uses: general administration, health services, research ○ Support for custom protocol Gamecock look-n-feel, easy to use and engaging, customizable, secure, privacy disclosure/management, HIPAA compliance, accessibility (ADA compliance), scalable, well tested, extensible
  17. 17. Why? Proactively keep Campus healthy ● Informing and Educating the Community, Regularly Check Health Status: ○ Stay informed and educated, make better individual decisions, feel safer, reduce adverse outcomes ○ In case of concern, connect with Student Health Services, manage isolation protocols ● Providing Campus-wide View: tools needed to make campus-wide decisions ○ Insurance against possible adverse outcome from low-risk approach (CDC’s Considerations for Institutes of Higher Education (May 21), proactive implementation of protocols ● Research: COVID-19, Mental Health
  18. 18. How? ● Comprehensive campus-wide involvement and coordination: requirements, development, testing, operations ○ College of Nursing: lead evaluation ○ Student Health Services ○ School of Public Health ○ Division of Information Technology ○ CEC; of course, the AI Institute ● Development team with extensive experience ● Much more functional, forward looking, and cheaper than vendors
  19. 19. Demo: Health-e Gamecock COVID-19 App (WebApp, IOS, Android) Note: development is now complete, app is evaluated and we plan to use it for a major study.
  20. 20. Health-e Gamecock COVID-19 daily status check (on Apple Store)
  21. 21. Augmented Personalized Health Check out the TEDx talk and the original article 22
  22. 22. 24 Use Case: kHealth Asthma Many Sources of Highly Diverse Data (& collection methods: Active + Passive): Up to 1852 data points/ patient /day http://bit.ly/kHealth-Asthma kBot with screen interface for conversation Images Text Speech ★ Episodic to Continuous Monitoring ★ Clinician-centric to Patient-centric ★ Clinician controlled to Patient-empowered ★ Disease Focused to Wellness-focused ★ Sparse data to Multimodal Big Data *(Asthma-Obesity)
  23. 23. Self Monitoring with kHealthDash: Knowledge enabled personalized DASHboard for Asthma Management Video link - https://youtu.be/yUgXCPwc55M
  24. 24. Digital Phenotype Score vs Asthma Control Test Score Digital Phenotype Score = Symptom Score + Rescue Score + Activity Score + Awakening Score
  25. 25. 27 Utkarshani Jaimini, Krishnaprasad Thirunarayan, Maninder Kalra, Revathy Venkataramanan, Dipesh Kadariya, Amit Sheth, “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma”, JMIR Pediatr Parent 2018;1(2):e11988, DOI: 10.2196/11988.
  26. 26. Self Appraisal with Digital Phenotype Score Jaimini U, Thirunarayan K, Kalra M, Venkataraman R, Kadariya D, Sheth A, “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma, JMIR Pediatric Parent 2018;1(2):e11988 https://medium.com/leoilab/digital-phenotyping-turning-our-smartphones-inward-141a75b2f2a3 ● Digital Phenotype Score (DPS) is defined as the score to quantify the digital phenotypes collected from the social media, smartphones, wearables, and sensors streams. ● DPS acts as a cumulative measure for the abstraction of knowledge and information from the raw digital phenotypic data. ● The integration of the DPS can enable personalized interventions in real time which are directly responsive to the healthcare need of a patient.
  27. 27. Using Knowledge Graphs to construct a contextualized and personalized profile for each patient that can drive insights and personalized care strategies
  28. 28. ● Published in ISWC 2018 Contextualized Knowledge Graph Workshop, 2018. Amelie Gyrard, Manas Gaur, Saeedeh Shekarpour, Krishnaprasad Thirunarayan, Amit Sheth 2018, ISWC. ● Sheth, A., Jaimini, U., & Yip, H. Y. (2018). How Will the Internet of Things Enable Augmented Personalized Health? IEEE Intelligent Systems, 33(1), 89–97. https://doi.org/10.1109/MIS.2018.012001556
  29. 29. Determining Personalized Asthma Triggers: Seasonal Dependency Venkataramanan R, Thirunarayan K, Jaimini U, Kadariya D, Yip HY, Kalra M, Sheth A. Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study. JMIR Pediatr Parent 2019;2(1):e14300. doi: 10.2196/14300 PMID: 31518318 PMCID: 6716491
  30. 30. © 2021 NAVER. All rights reserved.
  31. 31. Evidence based Path to Personalization Patient-A was monitored for 13 weeks encompassing winter to spring 2018. Type: Severe, low medication compliance. Absence of Pollen First 6 weeks Presence of Pollen Rest of the 7 weeks Pre (observe) 4 weeks Post (validate) 2 weeks Pre 4 weeks Post 3 weeks Pollen 0 Pollen 0 days Pollen 17 days Pollen 3 days PM2.5 20 days PM2.5 5 days PM2.5 14 days PM2.5 2 days Ozone 1 day Ozone 0 Ozone 0 Ozone 1 day Asthma Episodes* 21 days Asthma Episodes 5 days Asthma Episodes 17 days Asthma Episodes 3 days ● Absence of Pollen - PM2.5 is the trigger ● Presence of Pollen - Pollen and PM2.5. Severe symptoms occurred in this period. Presence of both PM2.5 and Pollen increased the intensity of asthma episodes. Venkataramanan R, Thirunarayan K, Jaimini U, Kadariya D, Yip HY, Kalra M, Sheth A. Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study. JMIR Pediatr Parent 2019;2(1):e14300. doi: 10.2196/14300 PMID: 31518318 PMCID: 6716491
  32. 32. Interventional Strategy Actionable insights using Digital Phenotype Score and Controller Compliance Score
  33. 33. Clinical Interviews 35
  34. 34. 36 Q1: Do you feel restless in sleep? A1: Two-three times a week Q2: Do you wake early? How often does it happen? A2: Too early, happens two times a week. Q3: Do you persistently feel sad? A3: Yes. A sense of loneliness Q4: How often you stay alone? A4: I am divorced. Q5: Have you been diagnosed with PTSD? A5: No Q6: Have you seen an MHP for your anxiety disorder? A6: hmm recently. Participant was asked do they feel restless in sleep, then participant said hmm,two three times week Participant was asked, do they persistently feel sad, participant said divorce loneliness Participant was asked, have they been diagnosed with PTSD, participant said hmm recently. Diagnostic Interview Summaries Contextualized Diagnosis of patient conversations Correct diagnosis: Post Traumatic Stress Disorder
  35. 35. Online Mental Health Support Social Media: Reddit 41
  36. 36. Interest We are interested in Matching Support Seekers -SSs (left) with Support Providers - SPs (right) Current State Currently, moderators (center) do this matching 42 Proposal Our AI system will replace/assist the moderators that use medical knowledge, information about the user, extracted from the posts to perform this matching
  37. 37. 43 Example of matching on Reddit
  38. 38. Using knowledge and lexicons with deep learning for matching users on Reddit
  39. 39. Addiction (Opioid, Cannabis, Synthetic Cannabinoid, Prescription Drug Abuse) X Epidemiology 46
  40. 40. Knowledge Graph for better Information Extraction: Application in Epidemiology 48 Cameron, Delroy, Gary A. Smith, Raminta Daniulaityte, Amit P. Sheth, Drashti Dave, Lu Chen, Gaurish Anand, Robert Carlson, Kera Z. Watkins, and Russel Falck. "PREDOSE: a semantic web platform for drug abuse epidemiology using social media." Journal of biomedical informatics 46, no. 6 (2013): 985-997.
  41. 41. Knowledge-infused via DAO Ontology for solving relation between Cannabis and Depression
  42. 42. K-IL: Shallow Infusion with DAO in DL model to detect trends in cryptomarkets Table: Sample properties derived from cryptomarket with DAO
  43. 43. Motivation ● The opioid epidemic entrenched in Ohio and the Midwest of the US. ● The prevalence of opioid and its impact on the well-being of individuals and the society in Ohio. ○ Mental Health & Suicide Risk Questions 1. How can we use social media to measure mental health impact of opioid prevalence? 1. Are there association between opioid and mental health/suicide risk based on social media data? Approach Monitoring the prevalence of opioid and its impact on mental health and suicide in Ohio, utilizing a scalable knowledge and data driven BIGDATA (BD) approach via social media. BD Spoke: Opioid and Substance Use in Ohio
  44. 44. Score Calculation Opioid Mental Health Depression Addiction Suicide Risk Ideation, Behavior Attempt Correlations ● Sheth, Amit, and Pavan Kapanipathi. "Semantic filtering for social data." IEEE Internet Computing 20, no. 4 (2016): 74-78. ● Hussein S. Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, and Amit Sheth. 2018. Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models. In Proceedings of the 27th International Conference on Computational Linguistics (COLING2018), pages 1986-1997. Association for Computational Linguistics ● Gaur, M., Kursuncu, U., Alambo, A., Sheth, A., Daniulaityte, R., Thirunarayan, K., & Pathak, J. (2018, October). " Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 753- 762). ● Gaur, M., Alambo, A., Sain, J. P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., ... & Pathak, J. (2019, May). Knowledge-aware assessment of severity of suicide risk for early intervention. In The World Wide Web Conference (pp. 514-525). ● Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., ... & Sheth, A. (2017, July). Semi-supervised approach to monitoring clinical depressive symptoms in social media. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 1191-1198). ● Daniulaityte, R., Nahhas, R. W., Wijeratne, S., Carlson, R. G., Lamy, F. R., Martins, S. S., ... & Sheth, A. (2015). “Time for dabs”: Analyzing Twitter data on marijuana concentrates across News Articles Twitter Data Domain Knowledg e Content Enrichment DAO DSM-5 Location Extraction Keyphrase Extraction Age-based Clustering Semantic Filtering Entity Extraction NLM Training f(.) Knowledge Infused Natural Language Processing (Ki-NLP) Semantic Mapping Semantic Proximity Topic Model Language Model DAO DSM-5 Dashboard Visualizations (Online) Offline Analysis & Visualizations BD Spoke: Opioid and Substance Use in Ohio
  45. 45. ● Substance use addictive disorder linked to opioid with higher correlation. ● Gender dysphoria, Dissociative and OCD disorders are correlating moderately. Opioid Prevalence in Ohio vs. Mental Health & Suicide ● Suicide ideation (initial stage) with highest correlation. ● Mild severity level of suicide risk linked to higher correlation. ● Weak correlation for suicide indication (before initial) p N counties p N counties
  46. 46. with a Social Quality Index Insights from semantic analysis of Social Media Big Data Psychidemic: Measuring the Spatio-Temporal Psychological Impact of Novel Coronavirus with a Social Quality Index Insights from semantic analysis of Social Media Big Data ○ Mental Health: Depression, Anxiety ○ Addiction: Substance use/abuse ○ Gender-based/Domestic Violence ○ Mental Health: Depression, Anxiety ○ Addiction: Substance use/abuse ○ Gender-based/Domestic Violence
  47. 47. Capability Demonstration 2 ● Spatio-temporal analysis of big data (1billion tweets, 700,000 articles) ● Use of domain knowledge graphs (mental health, addiction) ● Complex language understanding (slang, specialized domain terms) ○ Can do people/demographic, network, sentiment, emotion, intent analysis ● Scenario: Understand the implication of policy choices (e.g., school/business closing) and real- world events during COVID-19 55
  48. 48. A calculated Social Quality Index (SQI) aggregates mental health components (Depression, Anxiety), Addiction and Substance Use Disorders. Social Quality Index (SQI) vecteezy.com ● Change in SQI informs comparisons between states. ● Raw transformed SQI into relative state rankings changing over time.
  49. 49. e.g., IN, NH, OH, OR, WA, WY are worsening. Results: Relative State Rankings Reveal Patterns SQI Ranking April 4 - 10 SQI Ranking March 14 - 20 SQI Ranking March 21 - 27 SQI Ranking March 23-April 3 Darker: Better Social Quality
  50. 50. Results: Cluster --Improving SQI Ranking SQI bad SQI better SQI better SQI better Frequency Depression: 125037 Addiction: 92897 Anxiety: 81891 Total: 299825 Frequency Depression: 113830 Addiction: 81810 Anxiety: 74080 Total: 269720 Frequency Depression: 81463 Addiction: 60166 Anxiety: 45998 Total: 187627 Frequency Depression: 59088 Addiction: 49086 Anxiety: 46887 Total: 155061 IL, NY, MD, AZ, NM, MA. March 14-20 March 21-27 March 28-April 3 April 4-10
  51. 51. External Events ● Stay at home order ● Extension non-essential closure ● Closing parks ● State of emergency ● School Closure ● Mental Health Alarm ● Extension of small business closure ● Bill payment deadline extended ● 41k new job openings ● Child-care assistance for essential workers ● Spike in number of cases ● Stay at home order extended ● Extension School Closure ● State of emergency Extension ● Unemployment Increase(>800%) ● Tax deadline extended ● SNAP Benefits ● Death Benefits ● Domestic Violence Alarm ● Spike in number of cases ● Stay at home order extended ● Extension School Closure ● State of emergency Extension ● Closure barber shops and related businesses ● Number of deaths cross 50000 ● Unemployment Increase(>2.5k%) ● Tax deadline extended ● Phases reopening ● Limited indoor seating or gathering ● CARES Act Change in SQI relative to first week
  52. 52. Mental Health Dialogue 61
  53. 53. Construction of Personalized Knowledge Graph:
  54. 54. Knowledge-infused Reinforcement Learning ● The input to the agent is sequential through many steps, it gets an input and a reward at every step and learns the right output gradually through reinforcement.
  55. 55. APH Self Management: Reinforcement Learning
  56. 56. NOURICH A system to monitor diet, recommend meals and promote healthy eating habits: Current application: Type 1 diabetes in Children Contact: Revathy Venkataramanan Acknowledgement: Thanks to my collaborators Hong Yung Yip and Thilini Wijayasriwartane for the slides NOURICH Know What you Eat
  57. 57. Overweight Obesity Hospitalization Prevent overweight moving to obesity Prevent obesity leading to hospitalization Bridging the gap “Focusing on reducing excess and impulsive calorie consumption and making an informed decision about food choices and physical activity can help one attain a healthier weight and minimize the risk of chronic illness” The Dietary Guidelines of Americans 2010
  58. 58. ● Real-time food recognition ● Tensorflow Mobile net model 20 Food Categories 700 Images/Category Image Data Source Image Source: https://commons.wikimedia.org/wiki/File:Google_%22G%22_Logo.svg, https://freepik.com Nutrition Management System NOURICH GOAL: A system to monitor diet, recommend meals and promote healthy eating habits.
  59. 59. Architecture for application to Type 1 Diabetes in Children Data sources - User specific (food allergies, comorbidities, lab reports including genetic profiles and etc) Personalized Knowledge Base - Meal name - Nutrition - Ingredients - Cooking style Data collected DATA STORE Data collected are stored along with domain knowledge Image Voice Text Inputs Processing engine Image, voice,text to keyword DASHBOARD - Carbohydrate count
  60. 60. Trained on Food Images Cheesecake NOURICH Bitmap Conversion ByteArray Conversion 3 frames per second Python Script Data Cleaning 1) Removing png 2) Removing non jpeg Shell Script Image Annotation Training Model: Mobile-net model Accuracy: 83% Graph protobuf file Data preprocessing and training layer Crawled Images Conversion to TFLite Image Recognition layer Recognized image is displayed to the user APPLICATION ARCHITECTURE User Display nutrition info for the food Food Logs Sources: 1) User Icon by Gregor Cresnar from the Noun Project, 2)Food Images from Google Images, 3)TensorflowLite,Nutritionix,Bash, Instagram, Google - logos are from original vendors
  61. 61. Matching Support Seeker (SS) with Support Provider (SP) Online: Current application - matching SS and SP on mental health related subReddit https://scholarcommons.sc.edu/aii_fac_pub/516/
  62. 62. 72
  63. 63. 73 Modeling Exogenous Information into Epidemiological Models (Exo-SIR) Curve of Exogenous Information in Tamil Nadu Infected Curve (yellow) shift left because of Exogenous Information (Exo-SIR) Architecture of Exo-SIR model Simple SIR Model Infected Curve for Tamil Nadu State ● The curve representing time to infection shifts left (28% early) when we introduce exogenous events such as large gatherings (eg. Tablighi Jamaat religious gathering) or labor migration due to lockdown. ● Evaluated/validated for the impact of exogenous events on three states in India (Rajasthan, Tamil Nadu, Kerala). (Accepted at AI for COVID track in ACM KDD 2020)
  64. 64. There is a lot more: http://aiisc.ai, http://wiki.aiisc.ai https://scholarcommons.sc.edu/aii_fac_pub/ Many thanks to our sponsors, esp. ~10 NIH grants (four R01s, three R21s, R56, etc) from NIMH, NIDA, NICHD, and other institutes.

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