Jason Alan Fries

Jason Alan Fries

Stanford University

H-index: 21

North America-United States

About Jason Alan Fries

Jason Alan Fries, With an exceptional h-index of 21 and a recent h-index of 20 (since 2020), a distinguished researcher at Stanford University, specializes in the field of Healthcare, Foundation Models, Weak Supervision, Data-Centric AI, NLP.

His recent articles reflect a diverse array of research interests and contributions to the field:

MedAlign: a clinician-generated dataset for instruction following with electronic medical records

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium

Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare

INSPECT: A Multimodal Dataset for Patient Outcome Prediction of Pulmonary Embolisms

Ehrshot: An ehr benchmark for few-shot evaluation of foundation models

Red Teaming Large Language Models in Medicine: Real-World Insights on Model Behavior

Exploring the Potential of Large Language Models in Neurology, Using Neurologic Localization as an Example

A Systematic Review of Testing and Evaluation of Healthcare Applications of Large Language Models (LLMs)

Jason Alan Fries Information

University

Position

___

Citations(all)

4241

Citations(since 2020)

4043

Cited By

750

hIndex(all)

21

hIndex(since 2020)

20

i10Index(all)

27

i10Index(since 2020)

26

Email

University Profile Page

Google Scholar

Jason Alan Fries Skills & Research Interests

Healthcare

Foundation Models

Weak Supervision

Data-Centric AI

NLP

Top articles of Jason Alan Fries

Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare

BMC Medical Informatics and Decision Making

2024/2/14

INSPECT: A Multimodal Dataset for Patient Outcome Prediction of Pulmonary Embolisms

Advances in Neural Information Processing Systems

2024/2/13

Ehrshot: An ehr benchmark for few-shot evaluation of foundation models

Advances in Neural Information Processing Systems

2024/2/13

Exploring the Potential of Large Language Models in Neurology, Using Neurologic Localization as an Example

2023/12/6

A Systematic Review of Testing and Evaluation of Healthcare Applications of Large Language Models (LLMs)

2024/4/16

Language models in the loop: Incorporating prompting into weak supervision

ACM/JMS Journal of Data Science

2024/4/8

Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study

JMIR Medical Informatics

2024/4/4

EHR foundation models improve robustness in the presence of temporal distribution shift

Scientific Reports

2023/3/7

MOTOR: A Time-To-Event Foundation Model For Structured Medical Records

arXiv preprint arXiv:2301.03150

2023/1/9

Scalable Approach to Medical Wearable Post-Market Surveillance

medRxiv

2023

Investigating Real-world Consequences of Biases in Commonly Used Clinical Calculators.

American Journal of Managed Care

2023/1/1

Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks

Journal of the American Medical Informatics Association

2023/12/1

A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records

arXiv preprint arXiv:2311.11483

2023/11/20

Weak Supervision Enables Scalable Post-Market Surveillance on Medical Wearables

Circulation

2023/11/7

The Stanford Medicine data science ecosystem for clinical and translational research

JAMIA open

2023/10/1

The shaky foundations of large language models and foundation models for electronic health records

2023/7/29

See List of Professors in Jason Alan Fries University(Stanford University)

Co-Authors

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