MISTIC: a novel approach for metastasis classification in Italian electronic health records using transformers.

ABSTRACT:

Background

Analysis of Electronic Health Records (EHRs) is crucial in real-world evidence (RWE), especially in oncology, as it provides valuable insights into the complex nature of the disease. The implementation of advanced techniques for automated extraction of structured information from textual data potentially enables access to expert knowledge in highly specialized contexts. In this paper, we introduce MISTIC, a Natural Language Processing (NLP) approach to classify the presence or absence of metastasis in Italian EHRs, in the breast cancer domain.

Methods

Our approach consists of a transformer-based framework designed for few-shot learning, requiring a small labelled dataset and minimal computational resources for training. The pipeline includes text segmentation to i

SUBMITTER: Lilli L 

PROVIDER: S-EPMC11987267 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature