Jun June 2022
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!
Apr April 2022
Check out Dr. Hernandez-Boussard's interview in Ms Magazine here where she discusses data science as a path to inclusivity and diversity in medicine.
Mar March 07 Mon 2022
Dr Hernandez-Boussard moderated the Health Panel discussion at the annual Women in Science (WiDS) conference featuring outstanding women in the field.
Jun June 2021
Check out the interview of Dr. Hernandez-Boussard by Russ Altman for Stanford Engineering’s The Future of Everything podcast, here.
Deep Learning Approaches for Predicting Glaucoma Progression Using Electronic Health Records and Natural Language Processing
Advances in artificial intelligence have produced a few predictive models in glaucoma, including a logistic regression model predicting glaucoma progression to surgery. However, uncertainty exists regarding how to integrate the wealth of information in free-text clinical notes. The purpose of this study was to predict glaucoma progression requiring surgery using deep learning (DL) approaches on data from electronic health records (EHRs), including features from structured clinical data and from natural language processing of clinical free-text notes. Read our full paper here on Ophthalmology Science.
Analyzing real world data of blood transfusion adverse events: Opportunities and challenges
Blood transfusions are a vital component of modern healthcare, yet adverse reactions to blood product transfusions can cause morbidity, and rarely result in mortality. Therefore, accurate reporting of transfusion related adverse events (TRAEs) is paramount to improved transfusion practice. This study aims to investigate real-world data (RWD) on TRAEs by evaluating differences between ICD 9/10-based electronic health records (EHR) and blood bank-specific reporting. Read our full paper.
Opioid2MME: Standardizing opioid prescriptions to morphine milligram equivalents from electronic health records
The national increase in opioid use and misuse has become a public health crisis in the U.S. To tackle this crisis, the systematic evaluation and monitoring of opioid prescribing patterns is necessary. Thus, opioid prescriptions from electronic health records (EHRs) must be standardized to morphine milligram equivalent (MME) to facilitate monitoring and surveillance. While most studies report MMEs to describe opioid prescribing patterns, there is a lack of transparency regarding their data pre-processing and conversion processes for replication or comparison purposes. Read our full paper in International Journal of Medical Informatics.