Boussad lab has established a network study (PORPOISE) on the OHDSI community to identify patients at risk of postoperative prolonged opioid use. PORPOISE aims to develop and validate machine learning models in a diverse, multisite cohort by evaluating their generalizability, discrimination, and calibration in various CDM databases. The research protocol and materials can be found in the GitHub repository for the OHDSI Study.
Check out this MedCity News article highlighting our study on opioid dependency in opioid-naive Medicaid patients. Highlighted publication: Presription quantity and duration predict progression from acute to chronic opioid use in opioid-naive Medicaid patients.
Check out Dr. Hernandez-Boussard's article in VentureBeat where she discusses how artificial intelligence is transforming medicine and how we make sure it works for everyone.
Congratulations to Benjamin Jacobson and Vaibhavi Shah on the funding of their MedScholars project and Gabriela Escobar on the funding of her Major Grant Project! We are very excited to have them join our team this summer!
Picture a data scientist: A call to action for increasing diversity, equity, and inclusion in the age of AI
The lack of diversity, equity, and inclusion continues to hamper the artificial intelligence (AI) field and is especially problematic for healthcare applications. In this article, we expand on the need for diversity, equity, and inclusion, specifically focusing on the composition of AI teams. We call to action leaders at all levels to make team inclusivity and diversity the centerpieces of AI development, not the afterthought.
Prescription quantity and duration predict progression from acute to chronic opioid use in opioid-naïve Medicaid patients
Medicaid recipients are particularly vulnerable to opioid misuse. Opiates used for acute pain are an established risk factor for chronic opioid use (COU). Patient characteristics contribute to progression from acute opioid use to COU, but most are not clinically modifiable. To develop and validate machine-learning algorithms that use claims data to predict progression from acute to COU in the Medicaid population, adult opioid naïve Medicaid patients from 6 anonymized states who received an opioid prescription between 2015 and 2019 were included. Five machine learning (ML) Models were developed, and model performance assessed by area under the receiver operating characteristic curve (auROC), precision and recall. Read our paper here.
Using deep learning-based natural language processing to identify reasons for statin nonuse in patients with atherosclerotic cardiovascular disease
Statins conclusively decrease mortality in atherosclerotic cardiovascular disease (ASCVD), the leading cause of death worldwide, and are strongly recommended by guidelines. However, real-world statin utilization and persistence are low, resulting in excess mortality. Identifying reasons for statin nonuse at scale across health systems is crucial to developing targeted interventions to improve statin use. Read our paper here.