Ehrshot: An ehr benchmark for few-shot evaluation of foundation models
M Wornow, R Thapa, E Steinberg… - Advances in Neural …, 2023 - proceedings.neurips.cc
While the general machine learning (ML) community has benefited from public datasets,
tasks, and models, the progress of ML in healthcare has been hampered by a lack of such …
tasks, and models, the progress of ML in healthcare has been hampered by a lack of such …
[HTML][HTML] The Impact of Multi-Institution Datasets on the Generalizability of Machine Learning Prediction Models in the ICU
Objectives: To evaluate the transferability of deep learning (DL) models for the early
detection of adverse events to previously unseen hospitals. Design: Retrospective …
detection of adverse events to previously unseen hospitals. Design: Retrospective …
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse
set of data modalities. While prior works have successfully leveraged multiple modalities in …
set of data modalities. While prior works have successfully leveraged multiple modalities in …
Optimizing Length of Stay Prediction After Intubation: An Advanced Machine Learning Model with Real-time Vital Sign Integration
In the critical care unit, bedside monitors track patients' vital signs to help physicians make
choices. With increased storage and analysis capability, huge datasets may be processed …
choices. With increased storage and analysis capability, huge datasets may be processed …
Medical Event Data Standard (MEDS): Facilitating Machine Learning for Health
We introduce the Medical Event Data Standard (MEDS), a lightweight schema for enabling
machine learning over electronic health record (EHR) data. Unlike common data models …
machine learning over electronic health record (EHR) data. Unlike common data models …