Resources

Surveillance of invasive fungal infection (IFI) in clinical settings is a laborious process requiring a detailed review of patient medical history. One of the key sources of clinical information is imaging reports: radiologist-produced free-text rep…

Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented within clinical notes. As large language models (LLMs) are becoming more …

Surveillance of invasive fungal infection (IFI) in clinical settings is a laborious process requiring a detailed review of patient medical history. One of the key sources of clinical information is cytology and histopathology reports: pathologist-pr…

The Contextualized Race and Ethnicity Annotations for Clinical Text (C-REACT) dataset is a large publicly available corpus of sentences from clinical notes manually annotated for information related to race and ethnicity (RE). The corpus presented h…

This study presents MIMIC-III-Ext-SBDH, a manually reviewed and consolidated annotation dataset of social and behavioral determinants of health (SBDH) derived from clinical notes in the MIMIC-III database. We previously developed SBDH-Reader, a larg…

Electronic Health Records (EHRs) are integral for storing comprehensive patient medical records, combining structured data (e.g., medications) with detailed clinical notes (e.g., physician notes). These elements are essential for straightforward dat…

Large-language models (LLMs) show promise for extracting information from clinical notes. Deploying these models at scale can be challenging due to high computational costs, regulatory constraints, and privacy concerns. To address these challenges, …

Large language models (LLMs) have recently demonstrated remarkable capabilities across a broad spectrum of tasks and applications, including the medical field. Models like GPT-4 excel in medical question answering but encounter challenges in interpr…

Large-language models (LLMs) show promise for extracting information from clinical notes. Deploying these models at scale can be challenging due to high computational costs, regulatory constraints, and privacy concerns. To address these challenges, …

State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training…