Mike Conway — accepted AI-conference papers

2026

X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models

Jie Huang, Pengfei Yin, Zihan Xu, Daniel Capurro, Mike Conway, Ting Dang

IJCAI 2026

Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks. Despite their effectiveness, FEMRs remain black-box mode

Cited by 0 Scholar ↗

2024

Generating Mental Health Transcripts with SAPE (Spanish Adaptive Prompt Engineering)

Daniel Lozoya, Alejandro Berazaluce, Juan Perches, Eloy Lúa, Mike Conway, Simon D’Alfonso

NAACL 2024long

Large language models have become valuable tools for data augmentation in scenarios with limited data availability, as they can generate synthetic data resembling real-world data. However, their generative performance depends on the quality of the prompt used to instruct the model. Prompt engineerin…

Cited by 4 Source ↗PDF ↗Scholar ↗