J. Fries

发表

E. Halilaj, M. Fiterau, Chris Ré, 2016 .

Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine

Alistair E. W. Johnson, S. Pfohl, A. Johnson, 2021, Scientific Reports.

Cardiac imaging of aortic valve area from 26,142 UK Biobank participants reveal novel genetic associations and shared genetic comorbidity with multiple disease phenotypes

M. Rivas, E. Ashley, Yosuke Tanigawa, 2020, medRxiv.

Cardiac Imaging of Aortic Valve Area From 34 287 UK Biobank Participants Reveals Novel Genetic Associations and Shared Genetic Comorbidity With Multiple Disease Phenotypes

M. Rivas, Christopher Ré, E. Ashley, 2020, Circulation. Genomic and precision medicine.

Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study

Alistair E. W. Johnson, J. Fries, C. Aftandilian, 2022, JMIR medical informatics.

Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening

N. Shah, S. Kashyap, S. Gombar, 2020, medRxiv.

Language models are an effective representation learning technique for electronic health record data

Nigam H Shah, Ethan Steinberg, Ken Jung, 2020, J. Biomed. Informatics.

Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine

S. Pfohl, J. Fries, C. Aftandilian, 2022, Methods of Information in Medicine.

Self-Supervised Time-to-Event Modeling with Structured Medical Records

Jason Alan Fries, E. Steinberg, J. Fries, 2023, ArXiv.

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

S. Pfohl, E. Steinberg, J. Fries, 2022, Scientific Reports.

Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine

S. Pfohl, J. Fries, C. Aftandilian, 2021, Applied Clinical Informatics.

Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences

Euan A. Ashley, Christopher Ré, Jared Dunnmon, 2018, bioRxiv.

Multi-Resolution Weak Supervision for Sequential Data

Frederic Sala, Christopher Ré, Kayvon Fatahalian, 2019, NeurIPS.

EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models

E. Steinberg, J. Fries, N. Shah, 2023, ArXiv.

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

E. Steinberg, J. Fries, J. Posada, 2023, medRxiv.

Efficient Diagnosis Assignment Using Unstructured Clinical Notes

Robert Tinn, J. Fries, N. Shah, 2023, ACL.

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

Birju S. Patel, E. Steinberg, J. Fries, 2023, npj Digit. Medicine.

Self-Supervised Time-to-Event Modeling with Structured Medical Records

J. Fries, N. Shah, E. Steinberg, 2023, ArXiv.

The Stanford Medicine data science ecosystem for clinical and translational research

A. Callahan, Somalee Datta, T. Ferris, 2023, JAMIA open.

EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models

E. Steinberg, J. Fries, N. Shah, 2023, ArXiv.

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

S. Pfohl, E. Steinberg, J. Fries, 2023, J. Am. Medical Informatics Assoc..

Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine

S. Pfohl, J. Fries, N. Shah, 2022, Methods of Information in Medicine.

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

J. Fries, N. Shah, Yizhe Xu, 2023, 2301.03150.

Weak Supervision for Time Series: Wearable Sensor Classification with Limited Labeled Data

Christopher Ré, Scott L. Delp, Jason A. Fries, 2019 .

Language models are an effective representation learning technique for electronic health record data

Jason Alan Fries, Conor K. Corbin, K. Jung, 2020, J. Biomed. Informatics.