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Current Research Projects

  • Apol1 and risk for Preeclampsia
    To quantify the relationship between fetal and maternal APOL1 status and preeclampsia and to address whether APOL1 HR status is associated with preeclampsia onset or severity, incident vs recurrent preeclampsia, and fetal growth restriction (SGA), and preterm birth, or low birth weight

  • Predicting Rapid Kidney Function Decline in Sickle Cell Disease
    To predict and identify risk factors for rapid decline in kidney function in patients withsevere sickle cell disease (SCD) genotypes (HbSS/HbSβ0 thalassemia) using machine learning.

  • Artificial Intelligence Assisted Prediction of Late Onset Cardiomyopathy among Childhood Cancer Survivors
    Identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling earlier treatment.  We are implementing deep learning and signal processing methods using Children’s Oncology Group (COG) guideline-recommended baseline electrocardiography (ECG) to predict future cardiomyopathy.

  • Parkinson’s disease
    Parkinson’s disease (PD) is a progressive disabling neurodegenerative disorder that is understood to have three developmental phases, referred to as preclinical, premotor (or prodromal), and motor phases [1]–[3]. This project will discover novel noninvasive electrocardiogram (EKG) features of PD onset that can be processed within an artificial intelligence framework to identify patients at the prodromal PD phase. 

  • Education and Clinical Outcomes in Youth with Sickle Cell in Tennessee
    This project looks at educational outcomes among youth with Sickle Cell Disease in Tennessee, and how those outcomes are related to preventive care, including use of hydroxyurea.

  • CDC SCD Surveillance Project
    Sickle cell disease  is a genetic disorder affecting approximately 4.5 million people worldwide. Annually, more than 300,000 infants are born with SCD worldwide, and this number is expected to rise to 400,000 by 2050. In the United States (US) African Americans  are disproportionately affected by SCD. Tennessee has an overall population of approximately 6.8 million persons, with a racial distribution of 17% AA and 74% White, and 6% of people of Hispanic or Latino ethnicity. While TN has a diverse mix of rural and urban residents, racial minorities are disproportionally located in four urban areas: Memphis, Nashville, Knoxville and Chattanooga. Following one-year of capacity building in which we laid its foundation, we propose to now implement a statewide SCD surveillance program in TN.
  • Prediction of decompensation in COVID-19 patients using physiologic data analysis (Co-PI) IRB: 20-07294-XP

  • Machine learning predicts early onset acute organ failure in ICU patients with sickle cell disease (Research Support Staff) IRB:18-05881-XP

  • Physiomarkers from continuous physiological data streams predicts early onset of sepsis in critically ill adults(Research Support Staff) IRB: 16-04985-XP

  • Applying Artificial Intelligence for Early Identification of Parkinson’s Disease (Research Support Staff) IRB: 19-06751-XP

  • Predicting intracranial hypertension episodes in PICU patients (Research Support Staff) IRB:19-06976-XP
  • TweenVax: A comprehensive practice-, provider-, and parent/patient-level intervention to improve adolescent HPV vaccination. In collaboration with researchers at Emory and Johns Hopkins universities, we conduct a multi-phase study to utilize formative research to assess the efficacy of the TweenVax package to improve the provider recommendation and administration of HPV vaccine among pediatric and family physician practices in Georgia and Tennessee. This project is funded by ational Cancer Institute, National Institutes of Health, US Department of Health and Human Services.

  • SIEMA: The Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) is an analytic framework to help integrate dynamic surveillance data from multiple sources and health systems to support decision making for malaria control and elimination in Sub-Saharan Africa. We used ontologies and semantic web services to increase the interoperability of the various sources. We also designed a tool allowing researchers to detect and identify updates and changes in malaria data sources and to update the services so that the whole surveillance systems becomes more resilient. SIEMA has been funded by Melinda and Bill Gates Foundation, and Microsoft Research Inc.

  • SPACES: Semantic Platform for Adverse Childhood Experience (ACEs) Surveillance (SPACES) is a clinical mental health counseling and surveillance platform that facilitates the access to the relevant integrated information related to adverse childhood experiences, enables discovering the causality pathways and assists researchers, clinicians, public health practitioners, social workers, and health organization in studying the ACEs, identifying the trends, as well as planning and implementing preventive and therapeutic strategies. This project is supported by Memphis Research Consortium (MRC).

  • SODHS: Social Determinants of Health Surveillance (SODHS) provides a framework to integrate social, economic, and environmental determinants of health (Sociomarkers), and biological markers (Biomarkers) with multiple health related data sets to investigate the association between socio-economic, racial and ethnical disparities with multiple adverse health outcomes, such as Asthma, Mental Health, Sepsis, Adverse Surgical Outcome, Comorbidity of Chronic Diseases, Sickle Cell Disease (in collaboration with researchers from St. Jude Children's Research Hospital and University of Memphis), and COVID-19. The funding for this project is partially provided through grants from Memphis Research Consortium (MRC), and the Children's Foundation Research Institute (CFRI) at Le Bonheur Children’s Hospital.

  • UPHO: Memphis Urban Public Health Observatory (UPHO) uses advanced AI, Knowledge Representation and Semantic Web tools and techniques to assist epidemiologists, public health authorities and researchers collect data from several resources, foster the collection of surveillance data in a consistent and comparable way across jurisdictions to estimate the incidence and prevalence of different health conditions, as well as related risk factors. 

  • The PHL: Personal health libraries (PHL) are intended to help consumers manage their health information and play a more active role in their healthcare, health, and wellness. We propose implementing an integrated PHL solution that takes patients' perspectives, social and behavioral characteristics into account using a Hybrid Recommender Digital Librarian system (HRDL). The PHL system provides a single point of secure access to patients' digital health information and improves their health information seeking skills through personalized and tailored alerts and recommendations based on the unique attributes of an individual user or group.

  • POLE.VAULT: The POLicy EVAlUation & Logical Testing (POLE.VAULT) Framework assists different stakeholders and decision-makers in making informed decisions about different health-related interventions, programs and ultimately policies, based on the contextual knowledge and the best available evidence at both individual and aggregate levels. POLE.VAULT uses the theory of change (TOC) approach along with logic models that define the intervention under consideration to generate a causal diagram and an ontology-based inference model for causal description. The resulting causal diagram will then be compared to existing knowledge and data to determine whether the intervention is coherent, internally consistent and its goals are achievable in the allotted time with the resources provided. The contextual knowledge and semantics provided by the ontology will generate a more explainable, understandable, and trustworthy approach to compare and assess different interventions based on their shared goals.

Last Published: Jun 30, 2020