HRSA Grant UR650342

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Contact Aize Cao, Ph.D. at acao@mmc.edu to get more information on the project.

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Cao receives $2.2 million grant to support maternal health care for underserved women with SUD

Aize Cao, Ph.D., receives 2.2 million grant to receives $2.2 million HRSA grant to develop a comprehensive data analytics infrastructure model to support maternal health care for underserved women with substance use disorders.

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Activities

Student Capstone Projects

Degree
Semester
  • Chris Brown

    M.S. Data Science

    Antiphospholipid Syndrome: Unraveling Adverse Outcomes in Pregnancy

    Antiphospholipid syndrome (APS) refers to the clinical association between antiphospholipid antibodies and a hypercoagulable state, which increases the risk of blood clot formation within blood vessels. APS is more prevalent in women than in men. Research shows that women with APS face an elevated risk of adverse pregnancy outcomes, particularly during the fetal period (ten or more weeks of gestation). These outcomes include preeclampsia, characterized by high blood pressure and proteinuria (excess protein in urine), recurrent early pregnancy loss, fetal demise, and intrauterine growth restriction. APS-related pregnancy losses tend to occur later in pregnancy compared to sporadic or recurrent miscarriages, which typically happen earlier in the pre-embryonic or embryonic period. Factors such as placental insufficiency, hypertensive disorders of pregnancy, thrombophilia, and underlying autoimmune conditions play a role. This research aims to study the complex interplay of these factors to improve outcomes for affected women. Notably, APS is more prevalent among underserved communities.

  • Kristen Oguno

    M.S. Data Science

    Building a Machine Learning Model to Evaluate Risk Factors Associated with Poly-cystic Ovarian Syndrome

    Poly-cystic Ovarian Syndrome (PCOS) is a common, yet often undiagnosed, health condition affecting 8-13% of women globally. Its effects are primarily centered around hormonal imbalances and metabolism causing problems with the ovaries. The exact cause remains unknown, but PCOS is associated with an increased risk of diabetes, heart disease, and other complications. Early diagnosis is crucial for effective management and prevention of these issues. Leveraging machine learning (ML) and data science, our study focuses on developing a robust diagnostic model for PCOS, excluding the need for ultrasonography. Statistical analysis models such as Recursive Feature Elimination (RFE), Logistic Regression, and Random Forest were used to identify key predictors of PCOS diagnosis. Notably, results revealed women with less than 5 cycle days per month were more likely to develop PCOS, contradicting the assumption that PCOS causes excessive bleeding. Cystic acne, skin discoloration, and excess hair growth were identified as notable precursors to PCOS. Anti-Müllerian hormone was a significant biomarker for PCOS development. To address disparities in access to diagnostic tools, we propose integrating Anti-Müllerian hormone testing into routine blood work for all women to enable earlier PCOS detection. Implementing these recommendations could revolutionize PCOS management by facilitating early intervention and mitigating downstream health complications. Further research is needed to fully understand the mechanisms underlying PCOS development.

  • Fuxue Xin

    M.S. Biomedical Data Science

    The Impact of Housing Condition on AMA among Pregnant and Postpartum Women with SUDs

    Leaving treatment against medical advice (AMA) among pregnant and postpartum women with substance use disorder (SUD) is influenced by various factors, including housing and other social determinants of health (age, insurance, SUDs and mental state). Housing instability can be a significant barrier for pregnant and postpartum women with SUD seeking treatment. Lack of stable housing can lead to difficulties in accessing care, completing treatment programs, and maintaining recovery. This study aims to explore the feasibility and effectiveness of utilizing natural language processing to extract housing information from clinical notes, validate model performance. The dataset is coming from a clinical chart for patients in the Rainbow/Mending Rainbow program at Elam Mental Health Center (EMHC) at Meharry.