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Principal Investigator: Neeraj Chhabra
Organization: UNIVERSITY OF ILLINOIS AT CHICAGO
Fiscal Year: 2024
Award: $199,260
Funding agency: National Institute on Drug Abuse
Project Summary/Abstract
Patients with opioid misuse disproportionately utilize emergency health services and are at increased risk for
premature death. The timely and accurate identification of patients with opioid misuse in the Emergency
Department (ED) is critical to provide evidence-based interventions to decrease mortality. Challenges to opioid
misuse detection in the ED include provider time constraints, inconsistent screening approaches, and patient
barriers to self-reporting. Advanced analytic techniques such as machine learning and cluster analyses offer
promise in efficiently characterizing and identifying patients with opioid misuse during their ED encounter by
leveraging data within the electronic health record (EHR) and the prescription drug monitoring program (PDMP).
The role of machine learning approaches utilizing multiple data sources to identify ED patients with opioid misuse
has yet to be fully explored. In aim 1, multiple machine learning algorithms using ED encounter data will be
developed for the identification of opioid misuse. Models will be systematically assessed for social biases and
mitigation strategies implemented to ensure equity in model performance. In aim 2, the inclusion of longitudinal
PDMP data for the identification of ED patients with opioid misuse will be evaluated by building models from both
data sources utilizing ensemble stacking methods. Finally, in aim 3, an unsupervised latent class analysis model
will be built to identify clinically relevant subphenotypes of ED patients with opioid misuse, describe their
characteristics, and determine patient-oriented outcomes. An innovative approach to the detection of ED patients
with opioid misuse will be pursued by rigorously testing machine learning models utilizing multiple data sources,
conducting social bias assessments prior to clinical deployment, and characterizing latent groups of patients with
opioid misuse. The candidate for this Mentored Patient-Oriented Career Development Award (Dr. Neeraj
Chhabra) possesses a strong foundation in emergency care, medical toxicology, substance use research, and
biostatistics. Through this K23, he will further develop skills in data science to build comprehensive and scalable
models spanning multiple data domains for the identification of patients with opioid misuse. The multidisciplinary
mentorship team led by his primary mentor (Dr. Niranjan Karnik) and co-mentors (Dr. Majid Afshar, Dr. Harold
Pollack, and Dr. Gail D’Onofrio) consists of nationally renowned experts in the fields of substance use research,
machine learning, natural language processing, and clinical ethics. Through an integrated program of formal
coursework, ethics training, mentorship, and research, Dr. Chhabra will develop the skillset necessary to
complete these aims and transition to independent investigation. His proposal takes full advantage of the
combined resources provided by the affiliated institutions of Cook County Health and Rush University Medical
Center. Dr. Chhabra’s long-term goal is to utilize machine learning techniques to focus treatments and resources
towards patients with opioid misuse within the ED setting. This K23 award provides the necessary foundation to
pursue this goal and will form the basis for future R01 proposals evaluating the clinical impact of these models.
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