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Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The study titled 'Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction' explores the potential of large language models (LLMs) in processing lengthy documents for improved prediction of psychiatric patient outcomes. Despite the advancements in LLMs, their zero-shot performance in complex tasks remains suboptimal, leading to the hypothesis that different aspect-oriented prompts yield distinct information signals in summaries. The researchers validated their approach using real-world data from four hospitals, focusing on the critical task of predicting 30-day readmission rates. By integrating these varied signals into supervised training of transformer models, the study aims to enhance prediction accuracy, addressing the inherent information loss that occurs during summarization. This research not only contributes to the field of psychiatric care but also highlights the evolving capabilities of AI in handling complex healthcare data.
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