LAIR Projects

ProjectDescriptionManagerE-mailTeam Email
AMMIThe Analytics & Machine-Learning for Maternal Health Interventions is a project that aims to tackle the elevated risk of maternal mortality in African American women in the United States by building machine learning applications using data from the social determinants of health and clinical factors.Karl
Bangladesh – AMMIEvaluating machine-learning approach for maternal health risk assessment and create digital health capacity among health care providers in
AMMI-PRAMSPregnancy Risk Assessment Monitoring System - The Analytics & Machine-Learning for Maternal Health Interventions Mohammad Golam
TDEThe project is to build an evidence base for using machine learning technology to improve maternal and infant health outcomes. The project focuses on digitization of a previously created paper-based patient questionnaire and integrating it with the EHR system and
Cancer & Health Informatics ResearchCancer & Health Informatics
HDXHealth Data Exchange (HDX) is a project with a focus to develop a database system with all the health interoperability standards along with case studies for understanding the processes and requirements behind the implementation and acceptance of a Health standards
iPICSThe Interactive Prostate Cancer Information, Communication, and Support program (iPICS) is to improve shared decision-making and quality of life (QOL) for patients with newly diagnosed low to intermediate-risk localized prostate cancer. The study is being funded by the US Department of Defense.Karl
MIDASMachine Interpretable Document Analytics ServicesVibhor
Methods in Medical InformaticsThe Methods in Medical Informatics is a project aimed to gather and analyze Biomedical use cases in healthcare using Python. We will be refining the scripts in the current Methods in Medical Informatics textbook and adding new use cases.
Accurate Benign Breast Disease (BBD) ClassificationBBD classification is complex, which makes it very easy to be under diagnosed. Classifying BBD into right categories is significant in recognizing potential breast cancer development risk. Pathology reports, which is the most important resource for breast disease diagnosis, also leads to the under-diagnosis due to its own limitation. To overcome these limitations of pathology reports in BBD classification, we are going to create a pipeline to turn the unstructured information in pathology reports to a structured or computable format, and eventually enable the data out of this pipeline ready to be combined with other structured information from different resources.Cheng
OLLIEOnline Library for Learning, Implementation, and Education (OLLIE) is a collaboration between Carolina Health Informatics Program and the Department of Maternal & Child Health at UNC Gillings School of Global Public
Clinical Decision Support and Experienced-based Medicine (CDS &ExBM)A rapid dynamic system for converting EHR data into actionable experienced-based information to improve treatment efficacy by modeling and forecasting disease course and outcomes from real-world patients’ EHR
  • Methods in Medical Informatics-Ashley VictorMay-09-2023Apr-11-2023
  • Accurate Benign Breast Disease-Cheng FengFeb-17-2023Feb-03-2023Feb-03-2023
  • OLLIE-Mohammad KibriaJan-26-2023Nov-07-2022
  • AMMI-PRAMS-Mohammad KibriaApr-26-2023Apr-25-2023
  • Bangladesh – Analytics & Machine-Learning for Maternal Health Interventions (B-AMMI)-Mohammad KibriaMay-24-2023May-24-2023
  • Cancer & Health Informatics Research-Saianand BaluOct-25-2022Sep-23-2022
  • TDE-Sruthi SrinivasanMay-24-2023May-08-2023Feb-13-2023
  • AMMI-Subha BalasubramanianMay-10-2023May-05-2023May-05-2023
  • MIDAS-Vibhor GuptaApr-06-2023Nov-01-2022
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