10 citations · 11 across the 4 of their papers we have counts for
4 papers
FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records
Hojjat Karami, David Atienza, Jean-Philippe Thiran +1
Feature engineering for Electronic Health Records (EHR) is complicated by irregular observation intervals, variable measurement frequencies, and structural sparsity inherent to cli…
SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers
Hojjat Karami, David Atienza, Anisoara Ionescu
Generating synthetic Electronic Health Records (EHRs) offers significant potential for data augmentation, privacy-preserving data sharing, and improving machine learning model trai…
TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Records
Hojjat Karami, David Atienza, Anisoara Ionescu
Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing dat…
TimEHR: Image-based Time Series Generation for Electronic Health Records
Hojjat Karami, Mary-Anne Hartley, David Atienza +1
Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality. In this paper,…