REU Computing for Health and Well-being

May 22 - July 28, 2023

Department of Computer Science

University of Iowa

Project for Summer 2023

Modeling Problems Related to Spread of Hospital Acquired Infections

Mentor: Dr. Alberto Maria Segre
Domain Expert: Dr. Philip Polgreen

Students will work on modeling problems related to the spread of hospital acquired infections as part of the Computational Epidemiology research group. Team method is data driven, based on fine-grained data obtained from the University of Iowa Hospitals and Clinics and other similar healthcare facilities. Our approach generally involves running simulations to measure the effectiveness of changes to hospital architecture, hospital operations, healthcare worker behaviors (e.g., hand hygiene), and patient room assignments. Thus, depending on student interests, research topics might include, for example:
1. Constructing generative statistical models of, e.g., healthcare worker behavior from large, fine-grained, healthcare worker movement data sets;
2. Constructing simulations of different hospital operation policies (e.g., room assignments, cleaning practices, etc.);
3. Extracting spatial models of healthcare facilities from CAD files; or
4. Devising statistical models of healthcare acquired disease transmission parameters (e.g., shedding rates, healthcare worker/patient contact parameters, etc.).

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Computational Supports for Children’s Development of Executive Functions

Mentor: Dr. Juan Pablo Hourcade

We are developing interactive technology to lower barriers to a specific set of evidence-based activities intended to enhance preschool children's executive functions through high-quality social play. Our interactive web application, called StoryCarnival, includes interactive stories to motivate play, a play-planning tool, and voice agents controlled by adult facilitators to keep children engaged in play. We will expand our current research on StoryCarnival in multiple ways that we expect to contribute to the field of human-computer interaction. First, we are researching advanced user interfaces, including smart recommendations, for adult facilitators to control tangible voice agents with which children interact as part of StoryCarnival activities. Second, we will expand our focus from preschool children to neurodiverse children (e.g., autistic children, children with Down Syndrome).

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Measuring the exploitation of data voids

Mentor: Dr. Rishab Nithyanand

Data voids are obscure terms or queries that result in few results on search engines associated with online platforms such as Google, YouTube, Facebook, and Reddit. There has been recent evidence to suggest that these data voids are exploited by malicious actors in order to promote problematic content such as disinformation and propaganda. One common form of exploitation involves the production of problematic content to populate the search results associated with data voids, promoing the term/query in discourse that occurs on mainstream platforms (e.g., Twitter, Reddit, YouTube, etc.), and therefore directing unsuspecting users searching for these terms to the problematic content. The terms "migrant caravan" and "crisis actor" were believed to have been promoted into mainstream discourse with malintent in this way. In this REU project, students will work in the SPARTA lab to build a platform that measures this form of data void exploitation. The end goal is to produce a tool that enables the identification of such manipulation and provides resources for journalists and common citizens to recognize when they are being manipulated by these exploited data voids.

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Modeling equity in vaccine allocation

Mentor: Dr. Sriram Pemmaraju
Domain Expert: Dr. Philip Polgreen

The (still!) ongoing COVID-19 pandemic has highlighted the importance of fairness and equity in the allocation of vaccines. Defining and modeling fairness in this context is complicated. Should "demographic equity" be the main goal? Should we focus on equity of allocation or equity of outcomes? Should vaccines target the most vulnerable or should they target segments of the population that provide greatest societal utility? Furthermore, increasing fairness of allocation may reduce overall efficiency of vaccine allocation and so both fairness and overall efficiency are distinct and important goals. This project will focus on mathematical and computational modeling of fairness and equity of vaccine allocation. We will view this as an optimization problem, design efficient algorithms to solve this problem, and evaluate our solutions on realistic contact networks and disease-spread models.

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Individual CDI Risk Forecasting

Mentors: Dr. Bijaya Adhikari
Domain Expert: Dr. Philip Polgreen

Clostridioides difficile infection (CDI) is a bacterial hospital acquired infection (HAI). HAIs like CDI are a major burden on both patients and healthcare professionals (HCPs). The CDC estimates that there are roughly 4.5 HAI cases for every 100 hospital admissions with an annual cost of between 28 and 45 billion USD. Timely forecasts of HAI risk can help inform the deployment of effective counter measures, as well as improve resource allocation planning by forecasting future ICU bed requirements.
In this project, we will develop AI-driven approach to forecast daily CDI infection risk for each patient. Our proposed approach will combine both CDI exposure and patient demographic information to estimate the patient’s current risk for CDI infection.

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The Practical Implications of Diagnostic Embeddings

Mentors: Dr. Kishlay Jha

Our goal in this project is to develop an autonomous medical diagnostic system that can assist medical professionals in diagnosing medical conditions accurately and efficiently, while also reducing the risk of misdiagnosis and improving patient outcomes. To achieve this, we will train a machine learning model on data sources such as electronic health records (EHRs) and medical literature to generate meaningful feature representations. These representations or embeddings can then be used to cluster similar medical conditions and to generate diagnoses for new patient symptoms. The system can also be extended to include additional features such as patient demographics, medical history, and laboratory results to improve the accuracy of the diagnosis.

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