Time series as images: Vision transformer for irregularly sampled time series
Irregularly sampled time series are increasingly prevalent, particularly in medical domains.
While various specialized methods have been developed to handle these irregularities …
While various specialized methods have been developed to handle these irregularities …
Fusemoe: Mixture-of-experts transformers for fleximodal fusion
As machine learning models in critical fields increasingly grapple with multimodal data, they
face the dual challenges of handling a wide array of modalities, often incomplete due to …
face the dual challenges of handling a wide array of modalities, often incomplete due to …
REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models
The integration of multimodal Electronic Health Records (EHR) data has significantly
improved clinical predictive capabilities. Leveraging clinical notes and multivariate time …
improved clinical predictive capabilities. Leveraging clinical notes and multivariate time …
[HTML][HTML] Continuous patient state attention model for addressing irregularity in electronic health records
Background Irregular time series (ITS) are common in healthcare as patient data is recorded
in an electronic health record (EHR) system as per clinical guidelines/requirements but not …
in an electronic health record (EHR) system as per clinical guidelines/requirements but not …
A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs)
Electronic Health Records (EHRs) play an important role in the healthcare system. However,
their complexity and vast volume pose significant challenges to data interpretation and …
their complexity and vast volume pose significant challenges to data interpretation and …
Temporal Cross-Attention for Dynamic Embedding and Tokenization of Multimodal Electronic Health Records
The breadth, scale, and temporal granularity of modern electronic health records (EHR)
systems offers great potential for estimating personalized and contextual patient health …
systems offers great potential for estimating personalized and contextual patient health …
Automated fusion of multimodal electronic health records for better medical predictions
The widespread adoption of Electronic Health Record (EHR) systems in healthcare institutes
has generated vast amounts of medical data, offering significant opportunities for improving …
has generated vast amounts of medical data, offering significant opportunities for improving …
FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction
Multimodal electronic health record (EHR) data can offer a holistic assessment of a patient's
health status, supporting various predictive healthcare tasks. Recently, several studies have …
health status, supporting various predictive healthcare tasks. Recently, several studies have …
EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling
The integration of multimodal Electronic Health Records (EHR) data has notably advanced
clinical predictive capabilities. However, current models that utilize clinical notes and …
clinical predictive capabilities. However, current models that utilize clinical notes and …
[HTML][HTML] Research on Multimodal Fusion of Temporal Electronic Medical Records
M Ma, M Wang, B Gao, Y Li, J Huang, H Chen - Bioengineering, 2024 - mdpi.com
The surge in deep learning-driven EMR research has centered on harnessing diverse data
forms. Yet, the amalgamation of diverse modalities within time series data remains an …
forms. Yet, the amalgamation of diverse modalities within time series data remains an …