Browsing by Subject "predictive model"

Now showing 1 - 2 of 2
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    Analysis of AI Models for Student Admissions: A Case Study
    (ACM, 2023-03) Van Basum, Kelly; Fang, Shaiofen; Computer and Information Science, School of Science
    This research uses machine learning-based AI models to predict admissions decisions at a large urban research university. Admissions data spanning five years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios. During this time, submission of standardized test scores as part of a student's application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We first developed AI models and analyzed these models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his family to attend college.
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    External Validation of a Predictive Model for Acute Pancreatitis Risk in Patients With Severe Hypertriglyceridemia
    (AACE, 2019-08) Morkos, Michael; Amblee, Ambika; Henriquez, Andres; Basu, Sanjib; Fogelfeld, Leon; Medicine, School of Medicine
    Objective: We previously developed a predictive model to assess the risk of developing acute pancreatitis (AP) in patients with severe hypertriglyceridemia (HTG). In this study, we aimed to externally validate this model. Methods: The validation cohort included cross-sectional data between 2013 and 2017. Adult patients (≥18 years old) with triglyceride levels ≥1,000 mg/dL were identified. Based on our previous 4-factor predictive model (age, triglyceride [TG], excessive alcohol use, and gallstone disease), we estimated the probability of developing AP. Model performance was assessed using area under receiver operating characteristic curve (AUROC). Results: In comparison to the original cohort, patients in the validation cohort had more prevalent acute pancreatitis (16.2% versus 9.2%; P<.001) and gallstone disease (7.5% versus 2.1%; P<.001). Other characteristics were comparable and not statistically significant. The AUROCs were almost identical: 0.8337 versus 0.8336 in the validation and the original cohorts, respectively. In univariable analyses, the highest increase in odds of AP was associated with HTG, followed by gallstones, excessive alcohol use, and younger age. Conclusion: This study externally validates the 4-factor predictive model to estimate the risk of AP in adult patients with severe HTG (TG ≥1,000 mg/dL). Younger age was confirmed to place patients at high risk of AP. The clinical risk categories suggested in this study may be useful to guide treatment options.