PROJECT ABSTRACT
Pneumonia is the most common cause of death due to infection worldwide and in patients admitted to the
intensive care unit (ICU). Some patients are successfully cured with a short course of antibiotics, but over a third
are not readily cured despite appropriate therapy and have worse outcomes. Current ICU prediction models
focus on remote outcomes, such as hospital mortality, and are not built to predict short term outcomes of
pneumonia episodes. These episodes evolve over days, which are the timeframe during which clinical
interventions can shift patient trajectories toward favorable outcomes. I developed a machine learning approach
called CarpeDiem based on the practice of daily physician rounds to disentangle the contributions of unresolving
pneumonia to poor outcomes in patients with respiratory failure. These data suggest identifying patients likely to
fail pneumonia treatment early in their clinical course could impact ICU outcomes.
In this proposal, I will build upon my previous CarpeDiem methodology to develop and validate a machine
learning model to predict whether a pneumonia episode will be cured on day 7 using features from days 1-3. I
will leverage the data from the Successful Clinical Response in Pneumonia Therapy (SCRIPT) U19 Systems
Biology Center, a one-of-a-kind biorepository that includes over 700 patients and 1,400 bronchoalveolar lavages.
I will use both clinical electronic health record data and single-cell RNA sequencing transcriptomic data in my
models. My predictions and hypotheses are founded on causal biologic studies in murine models of lung injury
demonstrating the importance of regulatory T cells to promote pneumonia resolution. This proposal will give me
training in deep learning methods, deployment of machine learning models, and computational skills to analyze
high-dimensional single-cell transcriptomic data.
My project addresses critically important gaps in current modeling and predictors of pneumonia outcome and
will overcome current limitations in machine learning approaches to ICU data. I am supported by my mentors
Richard Wunderink, a world-renowned expert in pneumonia and critical care. My co-mentor Benjamin Singer
provides lung immunology expertise, and my co-mentor Yuan Luo provides expertise in machine learning. My
Research Advisory Committee includes experienced scientists in computational transcriptomics, electronic
health record modeling, database informatics, and lung immunology. My training plan leverages the unique
research environment at Northwestern University to grow my substantial track record while expanding my skills
in electronic health record and genomic data to support my academic development as a future R01 funded
investigator. Identifying early features of treatment failure provides targets for intervention, prompting additional
diagnostics, such as repeat bronchoalveolar lavage or imaging, or treatment modifications. Recognition of early
treatment failure offers the opportunity to divert a patient’s downward trajectory toward cure in critically ill patients.