Machine Learning-Based Model to Predict Response to Induction in Acute Myeloid Leukemia

2025 Annual Symposium On Demand

Presentation Time: 09:45 AM - 10:00 AM


This study evaluates machine learning (ML) models predicting response to induction therapy in acute myeloid leukemia (AML) patients, integrating clinical and gene expression data. After preprocessing data from the Beat AML trial, scaling, feature selection, and hyperparameter tuning were conducted. Six ML models were tested, with the XGB Classifier achieving the highest performance (AUROC = 0.86, AUPRC = 0.92). Findings highlight the potential of ML models in predicting AML treatment response and identifying relevant features.

Speaker:
Judy Bai, High school student
Greenhills School

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Using Reasoning LLMs to Extract Social Determinants of Health from Clinical Notes

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2025 Annual Symposium On Demand

Presentation Time: 10:00 AM - 10:15 AM


Social Determinants of Health (SDOH) include environmental, behavioral, and social conditions and have a substantial effect on individual health outcomes. This data is primarily found in unstructured clinical notes which are not machine-readable. Current leading SDOH extraction methods require significant computing resources and have a high setup difficulty. This study examined the use of reasoning LLMs such as OpenAI’s o4-mini for SDOH extraction. With self-consistency and few-shot prompting, o4-mini matched other leading NLP strategies.

Speaker:
Ertan Dogan, High School
Winston Churchill High School

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Comparative Cardiovascular Outcomes of Degarelix vs Leuprolide in Prostate Cancer: A U.S. Real-world Cohort Study

2025 Annual Symposium On Demand

Presentation Time: 10:15 AM - 10:30 AM


Cardiotoxicity is a major concern in androgen deprivation therapy for prostate cancer. Using the TriNetX Network, we compared cardiovascular outcomes of degarelix versus leuprolide in prostate cancer, hypothesizing reduced toxicity with degarelix. Outcomes included major adverse cardiovascular events (MACE), myocardial infarction (MI), stroke, death, and angina, stratified by pre-existing cardiovascular disease. Contrary to our hypothesis, degarelix was associated with higher mortality and MACE in both strata, with increased MI, stroke, and angina confined to individual strata.

Speaker:
Leonis Su, None
Centennial High School

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Using Machine Learning and Electronic Health Record (EHR) Data for Early Differentiation of Dementia with Lewy Bodies and Parkinson’s Disease Dementia

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2025 Annual Symposium On Demand

Presentation Time: 10:30 AM - 10:45 AM


Dementia with Lewy Bodies (DLB) and Parkinson's Disease Dementia (PDD) are frequently misdiagnosed, leading to delayed and inappropriate treatment. Using Medical Information Mart for Intensive Care (MIMIC-IV) data, the study developed machine learning models combining structured and unstructured clinical data to identify patients at risk of PDD or DLB. Random Forest and AdaBoost models achieved the highest accuracy (0.89), highlighting the potential of EHR to enable earlier detection of commonly misdiagnosed dementias.

Speaker:
Sophia Yang, High School
Columbia University (Visiting Student Researcher)

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Characterizing a Digital Cohort of People with Parkinson’s Disease From r/Parkinsons

2025 Annual Symposium On Demand

Presentation Time: 10:45 AM - 11:00 AM


Parkinson’s disease is heterogeneous and poorly documented outside clinical visits. Social media offers longitudinal patient archives. We extracted disease status and age from r/Parkinsons Reddit posts using natural language processing pipelines. The best-performing models were the Ensemble classifier for disease status (F₁ = 0.94) and the fine-tuned BERTweet model for age extraction (F₁ = 0.86). Modern LLMs also performed well. Accurate, automated phenotype extraction from online cohorts is highly feasible, supporting more efficient digital research.

Speaker:
Aaditya Panchal, High School Student
William B. Travis High School

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