Integrating mechanistic insights and models with machine learning: Applications in addressing neurological disorders

Saboo, Krishnakant V.

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https://hdl.handle.net/2142/122094 Copy

Description

Title

Integrating mechanistic insights and models with machine learning: Applications in addressing neurological disorders

Author(s)

Saboo, Krishnakant V.

Issue Date

2023-09-08

Director of Research (if dissertation) or Advisor (if thesis)

Iyer, Ravishankar K

Doctoral Committee Chair(s)

Iyer, Ravishankar K

Committee Member(s)

• Srikant, Rayadurgam
• Koyejo, Oluwasanmi
• Worrell, Gregory A

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Machine learning
• reinforcement learning
• mechanistic models
• mechanistic insights
• brain disorders
• Alzheimer's disease
• epilepsy
• disease progression modeling
• cognition prediction
• seizure cluster prediction
• brain stimulation

Abstract

Neurological disorders currently affect over one billion people and are the leading cause of disability worldwide. This thesis develops a novel framework that integrates mechanistic models and insights with machine learning (ML) to address neurological disorders by diagnosing them early and accurately, developing treatments, and improving our understanding of their underlying biology. Mechanistic insights and models express known disease-related biological processes either implicitly or explicitly as mathematical relationships amongst pertinent variables. These implicit/explicit relationships mitigate the noise in multi-modal data and help ML models efficiently extract unknown relationships related to the disease. Thus, our approach provides a holistic view of dynamically evolving, multi-scale, complex diseases in the face of unknown biological relationships by overcoming data-related challenges such as partial observability, high dimensionality, noise, and limited data. Our approach led to novel ML-based techniques that (i) work well with limited data, (ii) express and learn complex relationships, and (iii) capture causal relationships, which enabled diagnosis and in silico exploration of treatments. This thesis exemplifies the proposed framework by addressing clinically important problems in (i) Alzheimer’s disease (AD) and (ii) epilepsy. (i) Integrating mechanistic models with reinforcement learning (RL) enabled early diagnosis of individuals at risk of future cognitive decline by predicting personalized 10-year AD progression. (ii) We developed a unique mechanistic state-space model of epilepsy that could forecast seizures several days in advance and demonstrated its utility for designing RL-based adaptive brain stimulation to alleviate seizures. Our approach also led to new insights on short-term memory, localization of epileptogenic tissue, individualized prediction of seizure clusters, and the discovery of brain structures that mitigate the effect of AD on cognition. The proposed framework can be extended to improve the diagnosis, treatment, and understanding of other diseases of the body.

Graduation Semester

2023-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/122094

Copyright and License Information

Copyright 2023 Krishnakant Saboo

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