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Full-Text Articles in Medicine and Health Sciences
Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando
Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando
Community & Environmental Health Faculty Publications
Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …
Utilizing Ai Integrated Neuroimaging Technology To Expand Upon Machine Learning In Positron Emission Tomography Technology With The Aim Of Detecting Amyloid Beta Biomarkers Early In The Onset Of Alzheimer's., Ethan S. Terman
Undergraduate Research Posters
Early intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our day-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI …
Comparing Large Language Models Accuracy In Following Interval Surveillance Colonoscopy Guidelines, Olufemi Osikoya, Gregory Brennan
Comparing Large Language Models Accuracy In Following Interval Surveillance Colonoscopy Guidelines, Olufemi Osikoya, Gregory Brennan
North Texas GME Research Forum 2024
Introduction: Providing pathology results and appropriate recommendations after resection of colon polyps is mandatory. Large language models (LLMs) such as ChatGPT and Google Bard, have shown promise in clinical workflows such as pathology results letters. We tested whether LLMs could provide appropriate surveillance recommendations based on current guidelines from the US multi-society task force for post-colonoscopy follow-up. Methods: Our aim was to compare the accuracy of ChatGPT 3.5, ChatGPT 4, and Google Bard in providing appropriate interval surveillance recommendations. An example prompt being “Write a patient pathology result letter after a colonoscopy with one tubular adenoma polyp (< 10mm) resected. Include recommendations for when the next surveillance colonoscopy should be completed.” Seventeen different post polypectomy surveillance queries and responses were analyzed (correct, partially correct, incorrect) compared to USMSTF guidelines. Results: When …