Jan January 29 Wed 2020
Stanford Strong. Diversity & Inclusion Week 2020 #BoussardLab
Dec December 03 Tue 2019
We are hosting a monthly workshop for clinical informatics: DDAMES: Data Divas for AI in Medcine at Stanford. More information can be found here.
Sep September 17 Tue 2019
Dr. Hernandez-Boussard received a new R01 award from NLM, Advancing Knowledge Discovery for Postoperative Pain Management. This work will apply deep learning techniques to identify patients at high risk for adverse postoperative pain outcomes. Check out our recruiting page to join our team!
Sep September 04 Wed 2019
Dr. Hernandez-Boussard discusses the use of real world data for clinical assertions in a recent interview with Stanford Medicine based on findings from her recent JAMIA article. More details can be found here.
MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care.
The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present MINIMAR (MINimum Information for Medical AI Reporting), a proposal describing the minimum information necessary to understand intended predictions, target populations, and hidden biases in these emerging technologies. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias
Phenotyping severity of patient‐centered outcomes using clinical notes: A prostate cancer use case
A learning health system (LHS) must improve care in ways that are meaningful to patients, integrating patient‐centered outcomes (PCOs) into core infrastructure. PCOs are common following cancer treatment, such as urinary incontinence (UI) following prostatectomy. EHRs contain valuable information on PCOs and by using NLP methods, it is feasible to accurately and efficiently phenotype PCO severity. Phenotyping must extend beyond the identification of disease to provide classification of disease severity that can be used to guide treatment and inform shared decision‐making. Our methods demonstrate a path to a patient centered LHS that could advance precision medicine.