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Knowledge-infused NLU for Addiction and Mental Health Research (Keynote at MAISon-IJCAI2021 & ASONAM 2021)

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Knowledge-infused NLU for Addiction and Mental Health Research (Keynote at MAISon-IJCAI2021 & ASONAM 2021)

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https://maison-workshop.com/prof-amit-sheth/
Video: https://youtu.be/pRUXTuxm3as
Keynote at the 7th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2021) – Special Edition on Responsible, August 21, 2021 and is co-located with the 15th International Joint Conference on Artificial Intelligence (IJCAI 2021). Also ASONAM 2021.

With the increasing legalization of medical and recreational use of substances, more research is needed to understand the association between mental health and user behavior related to drug consumption. Specifically, drug overdose and substance use-related mental health issues have become two major topics that have been widely discussed on social media platforms. Big social media data has the potential to provide deeper insights about these associations to public health analysts for making policy decisions. Multiple national population surveys have found that about half of those who experience a mental health illness during their lives will also experience a substance use disorder and vice versa. The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context. Moreover, dark web vendors have been using social media as a new marketplace for drugs. Social media users also discuss the novel drugs emerging in dark web marketplaces and associated side effects/health conditions. These communications get complex when researchers try to annotate them or link them to a specific mental health entity. Considering the significant sensitivity of such communications and to protect user privacy on social media, a potential solution requires reliable algorithms for modeling such communications. We demonstrate the value of incorporating domain-specific knowledge in natural language understanding to identify the relationship between mental health and drug addiction. We discuss end-to-end knowledge-infused deep learning frameworks that leverage the pre-trained language representation model and domain-specific declarative knowledge source to extract entities and their relationships jointly. Our model is further tailored to focus on the entities mentioned in the sentence where ontology is used to locate the target entity’s position. We also demonstrate the capabilities of inclusion of the knowledge-aware representation in association with language models that can extract the Drug-Mental health condition associations.

Acknowledgments: Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Jyotishman Pathak, and collaborators on NIDA/NIH and NSF funded projects on Addiction and Mental Health.
http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression

https://maison-workshop.com/prof-amit-sheth/
Video: https://youtu.be/pRUXTuxm3as
Keynote at the 7th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2021) – Special Edition on Responsible, August 21, 2021 and is co-located with the 15th International Joint Conference on Artificial Intelligence (IJCAI 2021). Also ASONAM 2021.

With the increasing legalization of medical and recreational use of substances, more research is needed to understand the association between mental health and user behavior related to drug consumption. Specifically, drug overdose and substance use-related mental health issues have become two major topics that have been widely discussed on social media platforms. Big social media data has the potential to provide deeper insights about these associations to public health analysts for making policy decisions. Multiple national population surveys have found that about half of those who experience a mental health illness during their lives will also experience a substance use disorder and vice versa. The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context. Moreover, dark web vendors have been using social media as a new marketplace for drugs. Social media users also discuss the novel drugs emerging in dark web marketplaces and associated side effects/health conditions. These communications get complex when researchers try to annotate them or link them to a specific mental health entity. Considering the significant sensitivity of such communications and to protect user privacy on social media, a potential solution requires reliable algorithms for modeling such communications. We demonstrate the value of incorporating domain-specific knowledge in natural language understanding to identify the relationship between mental health and drug addiction. We discuss end-to-end knowledge-infused deep learning frameworks that leverage the pre-trained language representation model and domain-specific declarative knowledge source to extract entities and their relationships jointly. Our model is further tailored to focus on the entities mentioned in the sentence where ontology is used to locate the target entity’s position. We also demonstrate the capabilities of inclusion of the knowledge-aware representation in association with language models that can extract the Drug-Mental health condition associations.

Acknowledgments: Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Jyotishman Pathak, and collaborators on NIDA/NIH and NSF funded projects on Addiction and Mental Health.
http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression

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Knowledge-infused NLU for Addiction and Mental Health Research (Keynote at MAISon-IJCAI2021 & ASONAM 2021)

  1. 1. Video at: https://youtu.be/pRUXTuxm3as Knowledge-infused NLU for Addiction and Mental Health Research AI Institute public health, epidemiology, substance use Invited Talk at MAISoN 2021 in conjunction with IJCAI2021 Amit Sheth, Founding Director, AI Institute at UofSC #AIISC Technical Team: Usha Lokala, Manas Gaur, Kaushik Roy Experts/Collaborators: Raminta Daniulaityte, Francois Lamy, Jyotishman Pathak Collaborations: School of Medicine at UofSC, Wright State University, Boonshoft School of Medicine and Weill Cornell Medicine. Funding: NIDA/NIH and NSF funded projects on Addiction and Mental Health.
  2. 2. Outline of the talk ❏ Why NLP is not enough, why we need NLU? Challenges in Deep Learning (DL) ❏ Duality of Data and Knowledge & Adding Knowledge to DL ❏ What is Knowledge Graph? ❏ Towards Knowledge Infused Learning (K-IL) ❏ Shades of K-IL ❏ Use-cases ❏ Addiction X Epidemiology ❏ Mental Health While we will focus on social media data, the ideas and methods are adaptable to clinical, conversational and other types of data. 2
  3. 3. NLP is not enough! Data alone is not enough! Why we need NLU? Why knowledge is indispensable? 3 NLU: Natural Language Understanding
  4. 4. Deep Learning: Deeper you go, Darker it gets. 4 Performance Interpretability Rule Based Learning Linear/ Logistic Regression Decision Trees k-NN Generalized Additive Models Bayesian Models Support Vector Machines Ensembles Deep Learning Statistical Symbolic Solution: Hybrid systems.
  5. 5. Duality of Data and Knowledge Layered hybrid system using neural networks and deep learning for perception with knowledge-based reasoning system for decision making (one of several architectures for neuro-symbolic AI) Sheth, A., & Thirunarayan, K. (2021). The duality of data and knowledge across the three waves of AI. IT Professional, 23(3), 35–45. Decisions/Actions Symbolic Reasoning Linking Data to Knowledge Neural Network & Deep Learning Data Sensing & Collection Feature Engineering, Recommendation Deduction, Abduction, Non- monotonic and Probabilistic Inferencing, Planning Construction and/or Instantiation Upper Level (Second Stage) Lower Level (First Stage)
  6. 6. Example: NLP vs NLU
  7. 7. Limitations of NLP Presence of Long Tail Context Personalization Knowledge infused Learning (explicit knowledge + statistical AI) NLU for Decisions/Actions/Wisdom supported by XAI Abstraction NLP Limitations Approach Real Impact Reasoning A. Sheth, M. Gaur, K. Roy, K. Faldu, "Knowledge-intensive Language Understanding for Explainable AI." arXiv e-prints (2021): arXiv-2108. [ In print: IEEE Internet Computing, September/October, 2021.]
  8. 8. Context: Opioids are not legalized for recreation and medical purposes. Pain relievers are used legally with prescription. Synthetic Opioids like Fentanyl are being used for similar purposes illegally as consumers are addicted to it and often use them without prescription, contributing to Opioid overdoses. Q: Does Fentanyl cause Opioid Overdose? 8 Opioids are a class of drugs that include the illegal drug heroin, synthetic opioids such as fentanyl. Pain relievers available legally by prescription, such as oxycodone, morphine Misuse of and addiction to opioids—including prescription pain relievers causes Opioid Overdoses. NLU - Learning the Long Tail Domain Specific Low Resource Symbolic Knowledge Multi Hop Procedural Process of learning longtail which would be difficult to support with statistical AI alone.
  9. 9. Language Model Prediction Vs Symbolic Knowledge 9 “Who was the 44th President of the United States of America?” Barack Obama. Explain Why? See Info box or equivalently see KG . Who Succeeded? Who Preceded? Term? With Knowledge and limited data Without Knowledge GPT-3, ~175 Billion Parameters Barack Obama (71%), Donald Trump (18%), .. Explain Why? ….. ● Probabilistic vs assertional ● Named relationships
  10. 10. ConceptNet Common sense knowledge helps us identify commonly related context for the concepts in the text Here we use a single knowledge graph such as ConceptNet to contextualize Task: Respond to user appropriately if they are absent minded Contextualization Faldu, Keyur, Amit Sheth, Prashant Kikani, and Hemang Akabari. "KI-BERT: Infusing Knowledge Context for Better Language and Domain Understanding." arXiv preprint arXiv:2104.08145 (2021)
  11. 11. Sentence 1: How does benzodiazepine withdrawal happen by increasing stimulating chemicals? Sentence 2: Does heightened stimulating chemical production such as serotonin cause benzodiazepine withdrawals? ConceptNet Neurotransmitter isa chemical isa relatedTo withdrawals Are these sentences related to withdrawal from benzodiazepine? Abstraction
  12. 12. I’ve had a long day at work and I would like to go grab a drink Sure, there is a bar at […..] that’s got great reviews Alright, thank you. I’ve had a long day at work and I would like to go grab a drink Your depression typically gets worse with alcohol, are you sure? You’re right. Thanks Without personalization With a personalization Adam Alcoholism Suffers from PKG: Personaling knowledge graph (PKG) support the wisdom to prevent Adam from going out to a bar, which may be a norm with extenuating circumstances Personalization
  13. 13. 15 Really struggling with my bisexuality which is causing chaos in my relationship with a girl. Being a fan of LGBTQ community, I am equal to worthless for her. I’m now starting to get drunk because I can’t cope with the obsessive, intrusive thoughts, and need to get it out of my head. Is mental health related ? Yes: 0.71 , No: 0.29 Which Mental Health condition? Predicted: Depression (False) True: Obsessive Compulsive Disorder Reasoning over Model: Why model predicted Depression? Unknown Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." CIKM 2018. [CIKM 2018] Reasoning
  14. 14. 16 Really struggling with my [health-related behavior] which is causing [health-related behavior] with a girl. Being a fan of [XXX] community, I am equal to [level of mood] for her. I’m now starting to [drinking] because I can’t cope with the [obsessive compulsive personality disorder] [disturbance in thinking], and [disturbance in thinking]. Is mental health related ? Yes: 0.96 , No: 0.04 Which Mental Health condition? Predicted: Obsessive Compulsive Disorder(True) True: Obsessive Compulsive Disorder DSM-5 Knowledge Graph DSM-5 and Post Correlation Matrix Reasoning over Model: Why model predicted Obsessive Compulsive Disorder ? known Interpretable and Explainable Learning Reasoning
  15. 15. “ Adding knowledge to data- intensive learning for interpretability and explainability 17
  16. 16. What is Knowledge Graph Knowledge Graph (KG) is a structural representation of entities (entity and entity type) and relationships in various forms (e.g. labeled property graphs, RDFs) create to facilitate reasoning over the outcome. Commonsense Reasoning Graph Event Ontology Crisis Ontology 18
  17. 17. 19 Shades of Knowledge-infused Learning (K- IL) of knowledge graphs to improve the semantic and conceptual processing of data. Semi-Deep Infusion Deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques. Deep Infusion (Part of Future KG Strategy) combines a stratified representation of knowledge representing abstractions levels to be transferred in different layers of a deep learning model. Shallow Infusion Sheth, Gaur, Kursuncu, Wickramarachchi: Shades of Knowledge-Infused Learning for Enhancing Deep Learning
  18. 18. Dataset enrich Deep Learning Model Tacit Knowledge Hypothesis testing or similarity-based verification Shallow Infusion Tacit Knowledge Self-aware or External Knowledge Self-aware or External Knowledge Similarity based verification Semi-Deep Infusion Dataset Deep Learning Model Knowledge-infused (Deep) Learning Kursuncu, Ugur, Manas Gaur, and Amit Sheth. "Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning." AAAI Fall Symposium (2020).
  19. 19. Tacit Knowledge Similarity based verification Deep Infusion Deep Learning Model Stratified layer 1 Stratified layer 2 Stratified Knowledge 1 Stratified Knowledge 2 Knowledge-infused (Deep) Learning Kursuncu, Ugur, Manas Gaur, and Amit Sheth. "Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning." AAAI Fall Symposium (2020).
  20. 20. Knowledge Infused Language Understanding (KILU) A.Sheth, M. Gaur, K. Roy, K.Faldu. "Knowledge-intensive Language Understanding for Explainable AI," IEEE Internet Computing, September/October 2021.
  21. 21. Addiction (Opioid, Cannabis, Synthetic Cannabinoid, Prescription Drug Abuse) X Epidemiology Visit Project page: http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
  22. 22. Motivation The opioid epidemic is entrenched in USA ● Study the prevalence of opioid and its impact on the well-being of individuals and the society. ○ 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? Approach Monitoring the prevalence of opioid and its impact on mental health and suicide utilizing a scalable knowledge and data driven BIGDATA (BD) approach via social media. Opioid and Substance Use
  23. 23. 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 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 Understanding (Ki- NLU) Semantic Mapping Semantic Proximity Topic Model Language Model DAO DSM-5 Dashboard Visualizations (Online) Offline Analysis & Visualizations Opioid and Substance Use
  24. 24. Social Media Analysis to understand Cannabis and Synthetic Cannabinoid use Visit Project page: http://wiki.aiisc.ai/index.php/EDrugTrends
  25. 25. Study of Cannabis use on social media Semi-automated platform to identify emerging trends in cannabis and synthetic cannabinoid use in the U.S. ◎ To analyze characteristics of marijuana concentrate users, describe patterns and reasons of use. ◎ To identify factors associated with daily use of concentrates among U.S.-based cannabis users recruited via a Twitter-based online survey ◎ Identify and compare trends in knowledge, attitudes, and behaviors related to cannabis and synthetic cannabinoid use across U.S. regions with different cannabis legalization policies using Twitter and Web forum data. ◎ Analyze social network characteristics and identify key influencers (opinions leaders) in cannabis and synthetic cannabinoid-related discussions on Twitter 28
  26. 26. DAO Cannabis Study on social Media The DAO was expanded further to include more comprehensive representation of emerging cannabis products, synthetic cannabinoid products, health-related consequences and mental health conditions. NLU http://wiki.aiisc.ai/index.php/EDrugTrends
  27. 27. Drug Abuse Ontology as knowledge source for analyzing web-based data and use the extracted wisdom to inform public health surveillance as insights and actionable items. Ontology as a Knowledge Source to understand Addiction and Mental Health Roy, Kaushik, et al. "" Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision." arXiv preprint arXiv:2102.01222 (2021).
  28. 28. Knowledge-infused via DAO Ontology for solving relation between Cannabis and Depression Yadav, Shweta, et al. "“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction." PloS one 16.3 (2021): e0248299.
  29. 29. Monitoring Cryptomarkets to Identify Emerging Trends of Illicit Synthetic Opioids Use Visit Project page: http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
  30. 30. Why study cryptomarkets To monitor Cryptomarkets to Identify Emerging Trends of Illicit Synthetic Opioids Use Semi automated platform to monitor illicit online transactions of several illicit synthetic opioids in dark web. To design effective and responsive prevention and policies for public health professionals Epidemiological surveillance by providing timely data regarding emerging substances and product form To monitor Darknet supply and marketing trends over time. Enhancing the capacities of early warning systems like NDEWS 33
  31. 31. Wisdom Knowledge Wisdom as emerging trend alert to NDEWS Task: to harness cryptomarket data on illicit fentanyl and other novel synthetic opioid over time and identify new substances NLU
  32. 32. Actionable insight to National Drug Early Warning System (ndews.org) Reports about Novel Synthetic Opioids being sold on cryptomarkets. Early identification of emerging trends. Fentanyl Analogs (Ex: Carfentanil, acetyl fentanyl, furanyl fentanyl) , Novel Synthetic Opioids, variation in potency and other pharmacological features.
  33. 33. Shallow knowledge infusion to identify unique vendors in cryptomarkets - Sybil Account Detection Kumar, Ramnath, et al. "edarkfind: Unsupervised multi-view learning for sybil account detection." Proceedings of The Web Conference 2020. 2020.
  34. 34. Knowledge Infusion with DAO in DL model to detect trends in cryptomarkets - Reflection on social Media Table: Sample properties derived from cryptomarket with DAO Lokala, Usha, et al. "eDarkTrends: Harnessing Social Media Trends in Substance Use Disorders for Opioid Listings on Cryptomarket." ICLR AI for Public Health Workshop 2021.
  35. 35. “ Mental Health Disorders Wisdom as insights, emerging alerts and actionable items Drug Discovery Emerging Trends Enabling explainability Public Health surveillance User Roles and Behaviors Knowledge Sources: DAO and ODKG Takeaway - Epidemiology Real impact Cannabis study- Social Media Problems addressed Fentanyl- Cryptomarkets Web forums, BlueLight, Reddit, Twitter, Cryptomarkets How does Knowledge help in Epidemiology domain? Public Health Addictions Wiki Page: http://wiki.aiisc.ai/index.php/Public_Health_Addictions_Research_at_AIISC
  36. 36. Online Mental Health Support Social Media: Reddit Visit Project page: Mental Health
  37. 37. Interest We are interested in Matching Support Seekers -SSs (left) with Support Providers - SPs (right) Current State Currently, moderators (center) do this matching 41 Proposal SS-SP matcher will replace/assist the moderators that use medical knowledge, information about the user, extracted from the posts to perform this matching
  38. 38. 42 Example of matching on Reddit - SS-SP on Reddit Opiate addiction and recovery SS-SP matching Anxiety and depression SS-SP matching
  39. 39. Using knowledge and lexicons with deep learning for matching users on Reddit Event-Specific Filtering Business Closure School Closure Lockdown Shelter-in-place Hospitalization Domain-Specific Filtering Anxiety Depression First Person Pronouns Subordinate Conjunction Max Height Max Verb Phrase Length Syntactic Features Sentiment and Emotions Focus Future Cognitive Features Biological Features Psycholinguistic Gaur 2018, Gkotsis 2017 Linguistic Inquiry and Word Count http://liwc.wpengine.com/
  40. 40. Mental Health Dialogue 44
  41. 41. Construction of Personalized Knowledge Graph
  42. 42. 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.
  43. 43. APH Self Management: Reinforcement Learning
  44. 44. Conclusion 48
  45. 45. Knowledge Integration: ● Variety of knowledge is available , ● therefore depending on use- case/application it can be combined/integrated This will enable support for the right kind of wisdom depending on the task Integrate Integrated KG Stratified knowledge types with one example knowledge that can be directly used or extracted from, relevant to NLU Sheth, A., & Thirunarayan, K. (2021). The duality of data and knowledge across the three waves of AI. IT Professional, 23(3), 35–45. Language Syntactic Structure and Grammar
  46. 46. Data Sources ◎ Social Media (Reddit, Twitter,Web Forums) ◎ Conversations: Patient- Clinician, Virtual Health Assistant-Patient 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
  47. 47. “ 51 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
  48. 48. Addiction and Mental Health Projects at AIISC ➢ 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) ➢ BD Spoke: Opioid and Substance Use in Ohio ➢ more ...
  49. 49. 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. Acknowledgement Usha Lokala, Raminta Daniulaityte, Francois Lamy, Manas Gaur, Jyotishman Pathak, and collaborators on NIDA/NIH and NSF funded projects on Addiction and Mental Health.

Editor's Notes

  • Deep learning can do pattern recognition from data (NLP) (helps to classify class A vs class B, clustering etc.).

    Synthesizing, understanding and interpreting these patterns requires NLU, understanding of content that utilizes relevant knowledge and use that Knowledge for Personalization, Contextualization and Abstraction.
    ..
    (using domain knowledge, personalized understanding of the end-user)into concepts and guidelines that enable decisions requires NLU

    Explicit knowledge is indispensable for this process.
  • Connec, the lower-level (first stage) can sense and collect datating data to knowledge (semantic annotation and linking)
    Planning -> Symbolic reasoning

    Layered hybrid system using neural networks and deep learning for perception with knowledge-based reasoning system for decision making
    The lower-level (first stage) can sense and collect data


  • Add planning to SR
    Linking d to K
    Feature Enginnering, Recommendation

  • NLU is achieved using the following components: (1) the Drug Abuse Ontology (DAO);(2) an entity identification component; (3) a relationship extraction component; (4) a triple extraction component.
  • Long tail : entities or knowledge which are sparse, model will struggle in capturing the patterns to be explainable and interpretable. ---- zipf law shows this.
    Low resources --- limited in data
    Such problems requires knowledge, that is when are able in inject domain-specific concept and relationships

    Though this is a complex example, other applications: Crisis Response, Clinical notes in EHRs, are low resource in entities.

  • GPT-3 consumes large amount of data and parameters to answer a simple general knowledge question and cant explain why
    1) probabilistic vs assertional
    2) named relationships
  • Contextualization is interpreting a concept with reference to relevant application. Domain experts contextualize the problem within the domain of a particular disease, for example, depression with its common symptoms and medications. This enables better decision making such as more accurate treatments.
  • The task of mapping low-level features to higher-level human-understandable abstract concepts is known as abstraction. Humans often speak in terms of higher-level abstract concepts when explaining their decision/ideas to others. AI systems also need to explain decisions to the end users using abstract domain-relevant concepts constructed from low-level features and external knowledge in a KG.
  • Event specific filters and domain specific filters
  • Personalization, Contextualization, Wisdom (center), safety, explainability -> better, higher quality decisions for applications.
    Identifying data point-specific information and integrating it with external knowledge to construct a personalized knowledge source is known as personalization. For example, a person’s depressive disorder can be due to family issues, relationship issues, and clinical factors. All of these affect the context specific to the individual and consequently affect his symptoms and medications differently than that for another person.
  • KiL-Explainability as the ability of the framework to compute the difference between learned concepts and actual concepts (conceptual information loss)
    KiL-interpretability as the ability of the framework to proportionately propagate the conceptual information loss among the hidden layers by modulating the hidden representation with/without backpropagation.


    When u set up a problem in supervised learning, ask people to label
    Challenge -- people who are doing labeling, add bias or follow expert-specific schema, that brings diversity,
    On the other hand, concepts and relationship in knowledge graphs is very well in consensus and can be straight-away in statistical learning algorithm

    Specifically, a problem that requires contextualization and abstraction


  • In Semi-Deep Infusion, the knowledge is used to modify the parameters of the deep learning model depending on the identified context contained in the KG for relevant entities/tokens/phrases in the input.

    In semi ideep infusion, external knowledge is involved through attention mechanism or learnable knowledge constraints acting as a sentinel to guide model learning. we articulate the value of incorporating knowledge at different levels of abstractions in the latent layers of neural networks.
  • Stratified knowledge at different levels of abstraction is aligned with the relevant deep network layer (deeper layers, higher abstraction) and this knowledge is propagated during model learning

  • Here is the fragrance of Paradise. Here is the field of Jihad. Here is the land of #Islam. Here is the land of the Paradise.
  • General Language Understanding Evaluation (GLUE)

  • Drug abuse ontology work proved effective in adding knowledge to our deep learning models. Some of the applications of drug abuse ontology helped in determining user knowledge, attitudes, and behaviors related to non-medical use of buprenorphine and other illicit opioids through analysis of web forum data; 2) understanding patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the U.S and 3) gleaning trends in the availability of novel synthetic opioids through analysis of crypto market data.
  • Cannabis - Weed, Hemp , Ganja, CBD Oil

    Encoding position sequence relative to entities.

    The communications related to addiction and mental health are complex to process and understand given their language and contextual characteristics. Surface-level data analysis alone is not sufficient to understand the complex nature of relationships among the addiction and mental health context.

  • Challenges: Sparse-entity Recognition in Epidemiology, Implicit entity recognition from Slang Terms, Relationship extraction from Cryptomarket Web,

    Connected data Preparation using Domain Specific Knowledge over heterogeneous sources

    User Roles and User Behaviors in Social Epidemiology and Trends in Drug Discovery
  • How is the bot initialized from the initial information. PKG continues to be updated with basic patient data, discharge summary, continuous patient interactions.
  • How does a high level recommendation translate to dialogue and subsequent PKG updates
  • Lexical knowledge and common sense knowledge
  • Slide 3: Inner circle : talks about our research areas and strength
  • ×