First Advisor
Suresh Singh
Date of Award
Spring 6-2026
Document Type
Thesis
Degree Name
Bachelor of Science (B.S.) in Computer Science and University Honors
Department
Computer Science
Language
English
Subjects
machine learning, bioinformatics, explanatory gap, health informatics
DOI
10.15760/honors.1866
Abstract
The explanatory gap is a widely discussed concept in scientific and philosophical literature. In neuroscience, the solution to the explanatory gap is highly sought out, but the general consensus is that it is unsolvable. Numerous articles discuss the explanatory gap alongside computational tools and how these tools could aid neuroscientists in uncovering the mental health explanatory gap. However, significant developments in machine learning have been made since 2020, coinciding with the rise in Large Language Models (LLMs). This thesis is a literature review on computational methods, tools, and devices developed and utilized by researchers to improve how mental health disorders are diagnosed and treated, with a focus on anxiety and depression. Overall, it will delve into problems clinicians and neuroscientists face when diagnosing and treating depression and anxiety, discuss prior methods scientists have used to counter these issues, connect them with newer developments, and conclude on current progress and whether "potential" exists. "Potential" will refer to multiple qualities, including the advantages and use cases of specific tools, particularly machine learning algorithms, discussing developments of particular tools in the past 20 years, and using machine learning metrics to standardize results.
Persistent Identifier
https://archives.pdx.edu/ds/psu/44800
Recommended Citation
Vyas, Ambika, "Applications of Prior and Novel Computational Tools in Mental Health Treatment, and Their Potential to Uncover the Explanatory Gap" (2026). University Honors Theses. Paper 1829.
https://doi.org/10.15760/honors.1866