Document text
Principal Investigator: Catherine Bielick
Organization: BETH ISRAEL DEACONESS MEDICAL CENTER
Fiscal Year: 2024
Award: $191,484
Funding agency: National Institute of Allergy and Infectious Diseases
PROJECT SUMMARY
People who present with Acquire Immunodeficiency Syndrome (AIDS) are at high risk of developing an
opportunistic infection and death. Opportunistic infections (OIs) are any infection that is more frequent or more
severe because of HIV-mediated immunosuppression. Different states in the United States (US) have varying
rates of deaths among people with HIV (PWH) and AIDS due to variable health insurance coverage rates,
demographic health disparities, and access to health care. Data science public health tools to predict disease
and poor outcomes are rapidly advancing, but have not yet been sufficiently applied to improve outcomes
among PWH. In this K08 Mentored Career Development Award, Dr. Catherine Bielick, a fellow physician in
Infectious Diseases at the University of Virginia and rising data scientist, proposes to use artificial intelligence
(AI) to 1) predict the change in OI hospitalization rates by simulating Medicaid expansion (ME) in the South
and identify associated health inequities, 2) predict the change in OI-related mortality rates by simulating ME
and identify associated health inequities, and 3) create a time-series machine learning model to predict poor
clinical outcomes for individual PWH. The first two aims will be accomplished using the State Inpatient
Database (SID), which is hospitalization-level data for over 97% of hospitals in the US. The South was chosen
based on preliminary data finding an association with OI hospitalizations, mortality, and being uninsured.
Demographics, diagnosis codes with presence on admission indicators, and hospital information will all be
used to simulate the effect of Medicaid expansion in each state and predict the change in OI hospitalization
and mortality rates for PWH. The measured impact of this simulated intervention will provide important
groundwork to inform progress on state-level social determinants of health (SDOH), the need for health
insurance for all PWH, and future cost-effectiveness analyses. The last aim will use multisite longitudinal
electronic medical record (EMR) data called the ADVANCE network consisting entirely of underrepresented
patient populations from whom this subset of PWH will be the focus. A time-series deep learning model will be
used to create a risk score for individual PWH which predicts loss of viral suppression, development of an OI,
or all-cause mortality in the following 6 months. Accomplishing this goal will produce a foundational tool on
which future machine learning models can optimize model generalizability, safety, privacy, and responsible
implementation in an EMR for real-time predictions made at an intervenable time. This proposal benefits from a
strong advisory team which includes leading experts in HIV health and policy, data analytics, machine learning,
biostatistics, PWH data use, and AI health care. Drawing from the mentorship, collaboration, and support from
the Division of Infectious Disease, Public Health Sciences, the McManus lab, and the UVA School of Data
Science, the University of Virginia is an ideal institution for this award and provide the resources and diverse
environment for Dr. Bielick to flourish as an independent investigator in this cutting-edge field.
Terms: <AI system><AIDS><AIDS Associated Opportunistic Infection><AIDS Virus><AIDS opportunistic infections><AIDS prevention><AIDS-Related Opportunistic Infections><Acquired Immune Deficiency><Acquired Immune Deficiency Syndrome><Acquired Immune Deficiency Syndrome Virus><Acquired Immunodeficiency Syndrome><Acquired Immunodeficiency Syndrome Virus><Admission><Admission activity><Advisory Committees><Age><Area><Artificial Intelligence><Award><Biometrics><Biometry><Biostatistics><COVID crisis><COVID epidemic><COVID pandemic><COVID-19 crisis><COVID-19 epidemic><COVID-19 era><COVID-19 global health crisis><COVID-19 global pandemic><COVID-19 health crisis><COVID-19 pandemic><COVID-19 period><COVID-19 public health crisis><COVID-19 years><Career Development Awards><Career Development Awards and Programs><Career Development Programs K-Series><Caring><Cause of Death><Centers for Disease Control><Centers for Disease Control and Prevention><Centers for Disease Control and Prevention (U.S.)><Cessation of life><Clinical><Code><Coding System><Collaborations><Communicable Diseases><Communities><Computer Reasoning><Computerized Medical Record><Cost Effectiveness Analysis><Data><Data Analytics><Data Bases><Data Science><Data Scientist><Databases><Death><Death Rate><Development><Diagnosis><Disadvantaged><Disease><Disorder><Disparities><Disparity><Electronic Medical Record><Environment><Ethnic Origin><Ethnicity><Failure><Foundations><Future><Gender Identity><Goals><HIV><HIV Prevention><HIV-Related Opportunistic Infections><HIV/AIDS prevention><Health><Health Inequity><Health Insurance><Health Sciences><Healthcare><Height><High Performance Computing><Hospital Admission><Hospital Mortality><Hospitalization><Hospitals><Human Immunodeficiency Viruses><Immunosuppression><Immunosuppression Effect><Immunosuppressive Effect><In-house Mortalities><Incidence><Individual><Inequalities in Health><Inequities in Health><Infection><Infectious Disease Pathway><Infectious Diseases><Infectious Disorder><Inhospital Mortality><Inpatients><Institution><Insurance Coverage><Insurance Status><Intervention><Intervention Strategies><Investigators><K-Awards><K-Series Research Career Programs><LAV-HTLV-III><Low income><Lymphadenopathy-Associated Virus><Machine Intelligence><Machine Learning><Measures><Mediating><Medicaid><Mentors><Mentorship><Methods><Modeling><Morbidity><Morbidity - disease rate><Opportunistic Infections><Outcome><Persons><Physicians><Policies><Population><Predicting Risk><Privacy><Public Health><Public Policy><Race><Races><Records><Reporting><Research><Research Career Program><Research Personnel><Research Resources><Researchers><Resources><Risk><Risk Factors><SARS-CoV-2 epidemic><SARS-CoV-2 global health crisis><SARS-CoV-2 global pandemic><SARS-CoV-2 pandemic><SARS-coronavirus-2 epidemic><SARS-coronavirus-2 pandemic><Safety><Sampling><Schools><Series><Severe Acute Respiratory Syndrome CoV 2 epidemic><Severe Acute Respiratory Syndrome CoV 2 pandemic><Severe acute respiratory syndrome coronavirus 2 epidemic><Severe acute respiratory syndrome coronavirus 2 pandemic><Site><Subgroup><Task Forces><Testing><Time><Time Series Analysis><Training><Translating><Underrepresented Groups><Underrepresented Populations><Uninsured><United States><United States Centers for Disease Control><United States Centers for Disease Control and Prevention><Universities><Viral><Viral Burden><Viral Load><Viral Load result><Virginia><Virus-HIV><Work><access to health care><access to healthcare><accessibility of health care><accessibility to health care><accessibility to healthcare><advisory team><ages><coronavirus disease 2019 crisis><coronavirus disease 2019 epidemic><coronavirus disease 2019 global health crisis><coronavirus disease 2019 global pandemic><coronavirus disease 2019 health crisis><coronavirus disease 2019 pandemic><coronavirus disease 2019 public health crisis><coronavirus disease crisis><coronavirus disease epidemic><coronavirus disease pandemic><coronavirus disease-19 global pandemic><coronavirus disease-19 pandemic><cost efficient analysis><cost-effective analysis><data base><data warehouse><deep learning><deep learning based model><deep learning method><deep learning model><deep learning strategy><demographics><developmental><disparity in health><forecasting risk><health care><health care access><health care availability><health care service access><health care service availability><health data><health disparity><health inequalities><health insurance plan><healthcare access><healthcare accessibility><healthcare availability><healthcare service access><healthcare service availability><high risk><high-end computing><hospitalization rates><immune suppression><immune suppressive activity><immune suppressive function><immunosuppressive activity><immunosuppressive function><immunosuppressive response><improved><improved outcome><interventional strategy><learning network><long short term memory><m-Health><mHealth><machine based learning><machine learning based method><machine learning based model><machine learning method><machine learning methodologies><machine learning model><malleable risk><mobile health><model generalizability><modifiable risk><mortality><mortality rate><mortality ratio><patient population><predict risk><predict risks><predicted risk><predicted risks><predicting risks><predictive risk><predicts risk><racial><racial background><racial origin><risk prediction><risk predictions><severe acute respiratory syndrome coronavirus 2 global health crisis><severe acute respiratory syndrome coronavirus 2 global pandemic><social health determinants><socio-demographics><sociodemographics><tool><under representation of groups><under represented groups><under represented people><under represented populations><underrepresentation of groups><underrepresented people>