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
Cardiac Imaging of Aortic Valve Area From 34 287 UK Biobank Participants Reveals Novel Genetic Associations and Shared Genetic Comorbidity With Multiple Disease Phenotypes
Background: The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. Methods: From a sample of 34 287 white British ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac magnetic resonance imaging sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by polygenic risk scoring and phenome-wide screening, to identify genetic comorbidities. Results: A genome-wide association study of aortic valve area in these UK Biobank participants showed 3 significant associations, indexed by rs71190365 (chr13:50764607, DLEU1, P=1.8×10−9), rs35991305 (chr12:94191968, CRADD, P=3.4×10−8), and chr17:45013271:C:T...
Research Paper
Cardiac Imaging of Aortic Valve Area From 34 287 UK Biobank Participants Reveals Novel Genetic Associations and Shared Genetic Comorbidity With Multiple Disease Phenotypes
Background: The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. Methods: From a sample of 34 287 white British ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac magnetic resonance imaging sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by…
Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening
Objective: Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order. Materials and Methods: 16,103 SARS-CoV-2 RT-PCR tests were performed on 15,807 patients at Stanford facilities between March 2 and April 11, 2020. We analyzed the fraction of tested patients that were confirmed positive for COVID-19, the fraction of those needing hospitalization, and the fraction requiring ICU admission over the 40 days between March 2nd and April 11th 2020. Results: We find...
Research Paper
Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening
Objective: Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order. Materials and Methods: 16,103 SARS-CoV-2 RT-PCR tests were performed on 15,807 patients at Stanford facilities between March 2 and April 11,…
Estimating the efficacy of symptom-based screening for COVID-19
There is substantial interest in using presenting symptoms to prioritize testing for COVID-19 and establish symptom-based surveillance. However, little is currently known about the specificity of COVID-19 symptoms. To assess the feasibility of symptom-based screening for COVID-19, we used data from tests for common respiratory viruses and SARS-CoV-2 in our health system to measure the ability to correctly classify virus test results based on presenting symptoms. Based on these results, symptom-based screening may not be an effective strategy to identify individuals who should be tested for SARS-CoV-2 infection or to obtain a leading indicator of new COVID-19 cases.
Research Paper
Estimating the efficacy of symptom-based screening for COVID-19
There is substantial interest in using presenting symptoms to prioritize testing for COVID-19 and establish symptom-based surveillance. However, little is currently known about the specificity of COVID-19 symptoms. To assess the feasibility of symptom-based screening for COVID-19, we used data from tests for common respiratory viruses and SARS-CoV-2 in our health system to measure the ability to correctly classify virus…
Assessing the accuracy of automatic speech recognition for psychotherapy
Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring. Here we show that automatic speech recognition is feasible in psychotherapy, but further improvements in accuracy are needed before widespread use. Our HIPAA-compliant automatic speech recognition system demonstrated a transcription word error rate of 25%. For depression-related utterances, sensitivity was 80%...
Research Paper
Assessing the accuracy of automatic speech recognition for psychotherapy
Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring. Here…
The accuracy vs. coverage trade-off in patient-facing diagnosis models
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis models similar to clinical decision support systems, with the primary difference being the coverage of symptoms and diagnoses. To be useful to patients and physicians, these models must have high accuracy while covering a meaningful space of symptoms and diagnoses. To the best of our knowledge, this paper is the first in studying the trade-off between the coverage of the model and its performance for diagnosis. To this end, we learn...
Research Paper
The accuracy vs. coverage trade-off in patient-facing diagnosis models
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis models similar to clinical decision support systems, with the primary difference being the coverage of symptoms and diagnoses. To be useful to patients and physicians, these models must have high accuracy…
Weakly Supervised Classification of Aortic Valve Malformations Using Unlabeled Cardiac MRI Sequences
This work formalizes a deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.
Research Paper
Weakly Supervised Classification of Aortic Valve Malformations Using Unlabeled Cardiac MRI Sequences
This work formalizes a deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.
Multi-Resolution Weak Supervision for Sequential Data
Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to individual elements or collections of elements in a sequence. A key challenge in weak supervision is estimating the unknown accuracies and correlations of these sources without using labeled data. Multi-resolution sources exacerbate this challenge due to complex correlations and sample complexity that scales in the length of the sequence. We propose Dugong, the first framework...
Research Paper
Multi-Resolution Weak Supervision for Sequential Data
Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to individual elements or collections of elements in a sequence. A key challenge in weak supervision…
Medical Device Surveillance With Electronic Health Records
Showcasing state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data.
Research Paper
Medical Device Surveillance With Electronic Health Records
Showcasing state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data.
Swellshark: A Generative Model for Biomedical Named Entity Recognition Without Labeled Data
Introducing SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly.
Research Paper
Swellshark: A Generative Model for Biomedical Named Entity Recognition Without Labeled Data
Introducing SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly.
