PURPOSE: To enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases.
PURPOSE: Antibiotic prophylaxis is critical to ophthalmology and other surgical specialties. We performed natural language processing (NLP) of 743 838 operative notes recorded for 315 246 surgeries to ascertain two variables needed to study the compa...
PURPOSE: Medications with non-standard dosing and unstandardized units of measurement make the estimation of prescribed dose difficult from pharmacy dispensing data. A natural language processing tool named the SIG extractor was developed to identify...
PURPOSE: Outpatient infusions are commonly missing in Veterans Health Affairs (VHA) pharmacy dispensing data sets. Currently, Healthcare Common Procedure Coding System (HCPCS) codes are used to identify outpatient infusions, but concerns exist if the...
PURPOSE: The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that, we conducted a simulation study comparing disease risk scores (DRS) and propensity scores (PS) usi...
BACKGROUND: Residual confounding presents a persistent challenge in observational studies, particularly in high-dimensional settings. High-dimensional proxy adjustment methods, such as the high-dimensional propensity score (hdPS), are widely used to ...
Artificial intelligence (AI) and machine learning (ML) are important tools across many fields of health and medical research. Pharmacoepidemiologists can bring essential methodological rigor and study design expertise to the design and use of these t...