Browsing by Subject "data science"

Now showing 1 - 3 of 3
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    Machine Learning Techniques for Time Series Regression in Unmonitored Environmental Systems
    (2023-04) Willard, Jared
    This thesis provides a computer science audience with a review of machine learningtechniques for modeling time series in unmonitored environmental systems with no available target data that have been published in recent years, and further includes three distinct research efforts applying these methods to real-world water resources prediction scenarios. Additionally, we identify several open questions for time series prediction in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding, and explainable AI techniques in modern machine learning frameworks. This is motivated by the current state of environmental time series modeling seeing a vast increase in applications of various machine learning models, in particular deep learning models built using the growing availability of high performance computing resources. It remains difficult to predict environmental variables for which observations are concentrated in a minority of locations and most locations remain unmonitored, and although many machine learning-based approaches have been developed, there is often a lack of comparison between them. The increased attention to environmental prediction topics such as disaster response, water resources management, and climate change reveal a need to compare these approaches, and understand when and where they should be applied in unmonitored environmental prediction scenarios.
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    Predicting Therapy Adherence : A Machine Learning Approach
    (2021-12) Lima Diniz Araujo, Matheus
    Ensuring adherence to medical therapy has been an open problem in health care practices since the Hippocrates Oath (400 BC) to modern medicine. In an ideal world, people would follow their doctor's recommendations. They would stick to their diet to lose weight, take their medication on time, and use their electronic health devices as recommended by the doctors. But the planned routine is rarely followed, causing a financial burden in the order of billions of dollars for the national healthcare system and many billions of dollars worldwide. A key mechanism to revert a tendency of non-adherence is early personalized intervention, targeting the key factors of undesired behavior, but this task is not trivial. After starting their therapy, individuals have an unpredictable series of life events that may impact their willingness to keep with the necessary therapy routine. Only recently, we achieved the ability to passively collect individual-level therapy data as patients progress in their treatments using digital devices. In this thesis, we proposes various machine-learning strategies that aim to leverage the data collected at the early stages of medical therapies to predict future adherence and recommend early accurate interventions that align with each individual's desired outcomes. We narrow most of the analysis in two sleep apnea therapies, Continuous Positive Airway Pressure (CPAP) and Upper-Airway Stimulation (UAS). But to reinforce the generalization of our methods we also show how they can be applied for the growth-hormone therapy management.
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    Talking in Code: Code Review as a Form of Communication
    (2023) Lisinker, Regina
    As coding and computation increasingly permeate statistics and data science courses, it is important for students to not only learn coding syntax, but also how to communicate their work. The process of code review enhances team communication by implementing a consistent feedback loop between coder and reviewer(s). While code review is commonplace in industry, it is not often implemented in data science classrooms. For this study, teams of undergraduate data science majors partnered with local community organizations to work on a data-focused problem. Students were given code review resources to utilize during the latter half of their projects. Data was collected through surveying students and interviewing their faculty advisors after project completion. This thesis presents results from these data to inform how students utilized the materials, their code review processes, and how they communicate via code review.