@inproceedings{bc63fefa1fa44a42b9388e3a255d5cdc,
title = "Attention-Based MIL for Medical Malpractice Prediction",
abstract = "Medical malpractice prediction is challenging due to the weakly labeled, heterogeneous, and multi-instance structure of claims data. We introduce Deep Attention MIL (DAMIL), an attention-based Multiple Instance Learning model that learns to identify the most informative instances within each claim. By optimizing attention weights end-to-end, DAMIL improves both prediction and interpretability. We evaluate DAMIL on two datasets: (1) a synthetic benchmark with controlled risk patterns, and (2) a real-world dataset from the Col·legi de Metges de Barcelona. DAMIL outperforms traditional MIL and a Bag-of-Words baseline, reaching AUCs of 0.715 (synthetic) and 0.714 (real). Instance-level attention provides interpretable insights into risk-relevant claim components.",
keywords = "Applied Artificial Intelligence, Claims, Decision Support Systems, Legal Medicine, Liability, Machine Learning, Malpractice",
author = "\{Bueno Tricas\}, Arnau and \{Rodr{\'i}guez Serrano\}, \{Jose A.\} and Jennifer Nguyen",
note = "Publisher Copyright: {\textcopyright} 2025 The Authors.; 27th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2025 ; Conference date: 15-10-2025 Through 17-10-2025",
year = "2025",
month = sep,
day = "22",
doi = "10.3233/FAIA250606",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "284--288",
editor = "Karla Trejo and Isabel Aguilo and Riera, \{Juan Vicente\} and Jordi Pascual",
booktitle = "Artificial Intelligence Research and Development - Proceedings of the 27th International Conference of the Catalan Association for Artificial Intelligence",
address = "Netherlands",
}