Automating Early Disease Prediction Via Structured and Unstructured Clinical Data

This study presents a fully automated methodology for early prediction studies in clinical settings, leveraging information extracted from unstructured discharge reports. The proposed pipeline uses discharge reports to support the three main steps of early prediction: cohort selection, dataset generation, and outcome labeling. By processing discharge reports with natural language processing techniques, we can efficiently identify relevant patient cohorts, enrich structured datasets with additional clinical variables, and generate high-quality labels without manual intervention. This approach addresses the frequent issue of missing or incomplete data in codified electronic health records (EHR), capturing clinically relevant information that is often underrepresented. We evaluate the methodology in the context of predicting atrial fibrillation (AF) progression, showing that predictive models trained on datasets enriched with discharge report information achieve higher accuracy and correlation with true outcomes compared to models trained solely on structured EHR data, while also surpassing traditional clinical scores. These results demonstrate that automating the integration of unstructured clinical text can streamline early prediction studies, improve data quality, and enhance the reliability of predictive models for clinical decision-making.

ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality

This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text, by prompt or similarity ranking, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality.