Mar March 2021
We are now hiring postdoctoral fellows and staff to work on a new medical informatics project that will include NLP and machine/deep learning predictive analytics. Contact if if you are interested!
Nov November 05 Thu 2020
Dr. Hernandez-Boussard discusses the application of machine learning and natural language processing (NLP) to Electronic Health Record (EHR) data at an NCI webinar. Check out the YouTube video here.
Jan January 29 Wed 2020
Stanford Strong. Diversity & Inclusion Week 2020 #BoussardLab
Call on FDA to Lead Standards for Safe and Effective AI Development
The FDA is considering the permanent exemption of premarket notification requirements for several Class I and II medical device products, including several artificial Intelligence (AI)–driven devices. The exemption is based on the need to rapidly more quickly disseminate devices to the public, estimated cost-savings, a lack of documented adverse events reported to the FDA’s database. However, this ignores emerging issues related to AI-based devices, including utility, reproducibility and bias that may not only affect an individual but entire populations. We urge the FDA to reinforce the messaging on safety and effectiveness regulations of AI-based Software as a Medical Device products to better promote fair AI-driven clinical decision tools and for preventing harm to the patients we serve. Read our Call to Action.
Learning from COVID-Like Patients to Predict Respiratory Failure
In the void created by a lack of clinical experience with COVID-19, AI may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. Here, we develop and test the feasibility of a “patients-like-me” framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. Our framework used COVID-19–like cohorts to design and train AI models that were then validated on the COVID-19 population. In total, 15 training cohorts were created using different combinations of the COVID-19–like cohorts. In the COVID-19–like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.