The Impact of Comorbid Sleep-Disordered Breathing on Hospitalization Risk Related to Diabetes and Atherosclerotic Disease: A Retrospective Cohort Analysis.

To determine the relationship between comorbid sleep-disordered breathing (SDB) and hospitalization rates related to diabetes mellitus (DM) and atherosclerotic disease (AD). This study used a retrospective cohort design from a large medical claims database with 5 years of data between 2018 and 2022. The presences of SDB, DM, and AD were identified using International Classification of Diseases (ICD-10) and relevant Current Procedural Terminology (CPT) codes.

Machine learning to identify chronic cough from administrative claims data.

Accurate identification of patient populations is an essential component of clinical research, especially for medical conditions such as chronic cough that are inconsistently defined and diagnosed. We aimed to develop and compare machine learning models to identify chronic cough from medical and pharmacy claims data. In this retrospective observational study, we compared 3 machine learning algorithms based on XG Boost, logistic regression, and neural network approaches using a large claims and electronic health record database.

A machine learning-based algorithm to identify U-500R insulin candidates among adults with type 2 diabetes mellitus in US retrospective databases.

Objective: To develop a machine learning-based predictive algorithm to identify patients with type 2 diabetes mellitus (T2DM) who are candidates for initiation of U-500R insulin (U-500R).

Methods: A retrospective cohort of patients with T2DM was used from a large US administrative claims and electronic health records (EHR) database affiliated with Optum. Predictor variables derived from the data were used to identify appropriate supervised machine learning models including least absolute shrinkage and selection operator (LASSO) and extreme gradient boosted (XGBoost) methods.

Development of Mobile Contingency Management for Cannabis Use Reduction.

Many interventions for cannabis use disorder (CUD) are associated with decreases in frequency and quantity of use but fail to increase overall rates of sustained abstinence. It is currently unknown whether reductions in use (in the absence of sustained abstinence) result in clinically significant improvements in functioning. The objective of this study was to refine a mobile contingency management approach to reduce cannabis use to ultimately evaluate whether reductions in frequency and quantity of cannabis are related to improvements in functional and mental health status.