4 papers · 1 filter
Adaptation Interfaces for In-Context Tabular Foundation Models in Time-to-Event Prediction
Minh-Khoi Pham, Luca Cotugno, Dan Cernei +10
Tabular foundation models (TabFMs) achieve strong performance on structured data, particularly for standard classification and regression problems. Yet, extending them to censored…
Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation
Minh-Khoi Pham, Luca Cotugno, Alina Sirbu +3
Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis. While classical statistical and…
MIEO: encoding clinical data to enhance cardiovascular event prediction
Davide Borghini, Davide Marchi, Angelo Nardone +6
As clinical data are becoming increasingly available, machine learning methods have been employed to extract knowledge from them and predict clinical events. While promising, appro…
A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
Samuele Punzo, Silvia Giulia Galfrè, Francesco Massafra +3
We present a machine learning pipeline for biomarker discovery in Multiple Sclerosis (MS), integrating eight publicly available microarray datasets from Peripheral Blood Mononuclea…