Temporal information extraction is the task of identifying temporal entities in a text and relating them to each other. In medicine, electronic health records (EHRs) contain text that documents the sequence of events during an encounter with a patient, and sometimes the events prior to the encounter (e.g., psychosocial environment and history). Temporality is especially important for the specialty of psychiatry. In this work, we describe the updates to the guidelines that allowed us to create a corpus of temporally-annotated psychiatric discharge summaries and progress notes in English. These updated guidelines were used to create a corpus of over 18,000 events, 2,200 time expressions, and 13,000 temporal relations. Temporal information extraction performance with a baseline system trained on non-psychiatric data obtains an F1 score of 0.152 on relation extraction, indicating the importance of this new dataset for making progress on temporal information extraction in the psychiatric domain.

Recent progress in large language models (LLMs) has enabled the automated processing of lengthy documents even without supervised training on a task-specific dataset.Yet, their zero-shot performance in complex tasks as opposed to straightforward information extraction tasks remains suboptimal.One feasible approach for tasks with lengthy, complex input is to first summarize the document and then apply supervised fine-tuning to the summary.However, the summarization process inevitably results in some loss of information.In this study we present a method for processing the summaries of long documents aimed to capture different important aspects of the original document.We hypothesize that LLM summaries generated with different aspect-oriented prompts contain different information signals, and we propose methods to measure these differences.We introduce approaches to effectively integrate signals from these different summaries for supervised training of transformer models.We validate our hypotheses on a high-impact task -30-day readmission prediction from a psychiatric discharge -using real-world data from four hospitals, and show that our proposed method increases the prediction performance for the complex task of predicting patient outcome.