author
Stanford University
Assistant Professor of Biomedical Data Science and of Medicine
I’m an Assistant Professor of Biomedical Data Science and of Medicine at Stanford University. My research focuses on training and evaluating foundation models for healthcare and is positioned at the intersection of computer science, medical informatics, and hospital systems. Much of my work explores using electronic health record (EHR) data to contextualize human health, leveraging longitudinal patient information to inform model development and evaluation. My work has appeared in NeurIPS, ICLR, AAAI, Nature Communications, and npj Digital Medicine.
The latest from Jason
BIGBIO: A Framework for Data-Centric Biomedical Natural Language Processing
BigBIO is a community library of biomedical NLP datasets that facilitates meta-dataset curation and enables zero-shot evaluation of biomedical prompts and multi-task learning.
Research Paper
BIGBIO: A Framework for Data-Centric Biomedical Natural Language Processing
BigBIO is a community library of biomedical NLP datasets that facilitates meta-dataset curation and enables zero-shot evaluation of biomedical prompts and multi-task learning.
Dataset Debt in Biomedical Language Modeling
This paper finds that only 13% of biomedical datasets are available via programmatic access and 30% lack documentation on licensing and permitted reuse, highlighting the dataset debt in biomedical NLP.
Research Paper
Dataset Debt in Biomedical Language Modeling
This paper finds that only 13% of biomedical datasets are available via programmatic access and 30% lack documentation on licensing and permitted reuse, highlighting the dataset debt in biomedical NLP.
Investigating Real-world Consequences of Biases in Commonly Used Clinical Calculators
Research Paper
Investigating Real-world Consequences of Biases in Commonly Used Clinical Calculators
Blog
Better not bigger: How to get GPT-3 quality at 0.1% the cost
We created Data-centric Foundation Model Development to bridge the gaps between foundation models and enterprise AI. New Snorkel Flow capabilities (Foundation Model Fine-tuning, Warm Start, and Prompt Builder) give data science and machine learning teams the tools they need to effectively put foundation models (FMs) to use for performance-critical enterprise use cases. The need is clear: despite undeniable excitement about…
Ontology-driven weak supervision for clinical entity classification in electronic health records
Presenting Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules.
Research Paper
Ontology-driven weak supervision for clinical entity classification in electronic health records
Presenting Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules.
Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine
Objective: The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts. Methods: Studies were included if they were fully published articles that used machine learning and implemented a procedure to mitigate the effects of temporal dataset shift in a clinical setting. We described how dataset shift was measured, the procedures used to preserve model performance, and their effects....
Research Paper
Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine
Objective: The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts. Methods: Studies were included if they were…
Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries
Are the data in a large US electronic health record (EHR) complete and accurate enough to track trends in implant use and to assess the durability of implants (hereafter referred to as implant survivorship)? In this cohort study, EHR records of patients who had total hip arthroplasty in all Veterans Health Administration hospitals since 2000 were automatically reviewed using novel software; 80% to 95% of hip replacement components used since 2014 were accurately identified, trends in implant use matched known national trends, and known poor implants were found to be negative outliers. This suggests that automated analysis of the EHR...
Research Paper
Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries
Are the data in a large US electronic health record (EHR) complete and accurate enough to track trends in implant use and to assess the durability of implants (hereafter referred to as implant survivorship)? In this cohort study, EHR records of patients who had total hip arthroplasty in all Veterans Health Administration hospitals since 2000 were automatically reviewed using novel…
Language models are an effective representation learning technique for electronic health record data
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. However, this process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant. Such patient representation schemes enable a 3.5% mean improvement in...
Research Paper
Language models are an effective representation learning technique for electronic health record data
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. However, this process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy…
Ontology-driven weak supervision for clinical entity classification in electronic health records
Presenting Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules.
Research Paper
Ontology-driven weak supervision for clinical entity classification in electronic health records
Presenting Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules.
