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AI in Healthcare�(with a focus on using Knowledge-infused Neurosymbolic AI)

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AIISC portfolio in Core AI & Translational AI

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Neuro-symbolic

/Hybrid AI: Knowledge Infusion/Elicitation;

Compact + Custom

Knowledge Graph

Development

Deep Learning

Reinforcement Learning

NL Processing/�Understanding/�Generation

Computer Vision

Multimodal AI

(IoT/sensor, data streams, images, emoji)

Collaborative & Personal Assistants; coPilots, Multiagent Systems

Interpretability/�Explainability/Safety/

Trust/Ethics in AI

Medicine/Healthcare/Nursing

(Nutrition, Neurodevelopmental Disease, Asthma, Diabetes, Hypertension, Autism, Aphasia, Cognitive Disorders, Oncology,...)

Neuroscience

Brain Science

Epidemiology

Education

Social Good/Harm

(Disinformation, Harassment,Toxic Content, Deception, Extremism, Radicalization)

Public Health

(Mental Health, Addiction, COVID-19, Epidemics)

Smart Manufacturing

(Digital Twins, Factory of Future)

Disaster Management �(Response, Resilience)

Pharma: drug discovery, vigilance

Autonomous Systems (Vehicles)

Automated Planning

Gaming

Cognitive

Science

Science & Engg: �Radiation,

Astrophysics, Civil Infra & Transportation

Law

GenAI/LLMs:

HallucinationAI dectablilty, Toxicity, etc.

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My Students

Recent Alumnus

AIISC

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Outline

  • Using AI for Healthcare
    • Chemical Interaction Discovery
    • Asthma Management
    • Cognitive Decline from Speech
    • Brain Lesion Mapping
  • What is Semantics?
  • Symbolic AI
  • Neural AI
  • Knowledge-infused Neurosymbolic AI
  • Process Knowledge-infused Neurosymbolic AI
  • Examples from our Works
  • MTSS and Nourich

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Using AI for Healthcare - Chemical Interaction Discovery

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Using AI for Healthcare - Asthma Management

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Using AI for Healthcare - Cognitive Decline from Speech

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Comparing Pearson Correlation and Statistical Analysis

Identifying best combination modality, Atlas, Method

MRI Image Preprocessing

Atlas Selection

Modality Selection

Single Modality Experiments

Multimodal Experiments

r1 : rN - > PNT, AQ :Regression

r1: rN -> PNT, AQ : Autoencoder + Regression

Using AI for Healthcare - Brain Lesion Mapping

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What is Semantics?

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What is Semantics?

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Example Semantic Interpretations constructed either from manual effort (A, B, C), automatically (D, E), or semi-automatically (F).

(A) is empathi ontology designed to identify concepts in disaster scenarios (Gaur et al. 2019).

(B) Chem2Bio2RDF (Chen et al. 2010).

(C) ATOMIC (Sap et al. 2019).

(D) Education Knowledge Graph by Embibe (Faldu et al. 2020).

(E) Event Cascade Graph in WildFire (Jiang et al. 2019).

(F) Opioid Drug Knowledge Graph (Kamdar et al. 2019)

What is Semantics?

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Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

Downstream

Perform downstream task

—-----

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Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

Target: Return search results

Inference rule: If keyword match exists return page

Symbolic AI

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Keywords Obtained Using TF-IDF Vectorization, for example.

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Search Results

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Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

—-----

—-----

—------

Raw Big data + Minimal Target Demonstrations

Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

Target Prediction

with probability score

Visual Information processing

- object classification, object segmentation

Natural language processing

- part-of-speech tagging, constituency parsing

Long List of Successes!!

Neural AI

Downstream

Perform downstream task

Search Results

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Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

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Semantic Interpreter

Map raw data to useful features

Heterogeneous

Entities (nodes) and Relationships (directed edges)

Reasoner

Use rules of inference to infer targets from features

Target: Return search results

Inference rule: If path exists between nodes through certain intermediate nodes

Search Graph

Knowledge-infused Neurosymbolic AI

Downstream

Perform downstream task

Search Results

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Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

—-----

—-----

—------

Semantic Interpreter

Map raw data to useful features

Heterogeneous

Entities (nodes) and Relationships (directed edges)

Reasoner

Use rules of inference to infer targets from features

Target: Return search results

Inference rule: If path exists between nodes through certain intermediate nodes

Process Knowledge-infused Neurosymbolic AI

Downstream

Perform downstream task

Search Results

Search Graph

Process Trigger

If search non-toxic

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ALLEVIATE - A Chatbot for Mental Health Management in Individuals

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Examples from our Works

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Nourish Co-pilot: Custom, Compact and NeuroSymbolic Diet AI Model

MTSS AI Concierge- Custom, Compact and NeuroSymbolic AI Model

Please play on Youtube

Custom, Compact and Neurosymbolic AI Models

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  • Sheth, Amit, Manas Gaur, Kaushik Roy, and Keyur Faldu. "Knowledge-intensive Language Understanding for Explainable AI." IEEE Internet Computing 25, no. 5, 2021.

  • Gaur, Manas, et al. "" 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, 2018.

  • Kursuncu, Ugur, Manas Gaur, and Amit Sheth. "Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning." (2020), In AAAI Fall Symposium.

  • Gaur, Manas, Keyur Faldu, and Amit Sheth. "Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?." IEEE Internet Computing 25, no. 1, 2021.

  • Roy, K., Khandelwal, V., Goswami, R., Dolbir, N., Malekar, J., & Sheth, A. (2023, September). Demo alleviate: Demonstrating artificial intelligence enabled virtual assistance for telehealth: The mental health case. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 13, pp. 16479-16481).

Group webpage

Projects

webpage

References