Honors Theses and Capstones

Date Completed

Spring 2026

Abstract

Systematic reviews and meta-analyses represent the gold standard for evidence synthesis in healthcare, yet their manual execution remains labor-intensive, time-consuming, and vulnerable to human bias. With the exponential growth of biomedical literature, traditional literature screening and analysis has become increasingly unstable and noncomprehensive. This thesis presents the development and validation of an automate literature gathering and review system that integrates multiple scientific databases through a unified desktop application. The platform combines APIs from PubMed (NCBI Entrez), ClinicalTrials.gov, bioRxiv and medRxiv to enable simultaneous, standardized searching across peerreviewed and preprint sources. Built in Python with a PySide6 graphical interface, this standalone tool BLENS (Biomedical Literature Extraction and Scoring System) facilitates efficient and more comprehensive data retrieval, tabular visualization, and export functionality while supporting real-time updates as new studies become available. A key innovation lies in addressing critical gaps in existing tools: unlike narrow postretrieval machine learning classifiers or clinical decision-making support systems, this application focuses on upstream literature discovery. Moreover, it provides researchers with a broad, multi-database exploration tool specifically designed to reduce selection bias, overcome data dispersion, and accelerate the early stages of systematic review and meta-analysis.

Document Type

Undergraduate Thesis

First Advisor

Marek Petrik

College or School

CEPS

Department or Program

Data Science

Degree Name

Bachelor of Science

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Data Science Commons

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