dbt mimic omop is a free, open-source resource that converts the MIMIC-IV dataset to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) format on consumer level hardware. CDM approaches are increasingly adopted in both industry and academia due to the need for interoperability and reproducibility, including in clinical NLP tasks such as cohort selection, information extraction, and retrieval-augmented generation. The MIMIC-IV database is among the most widely used critical care research datasets, yet existing pipelines to transform it to OMOP depend on enterprise database infrastructure and complex orchestration, limiting accessibility for practitioners and resource-constrained researchers. We further integrate free-text clinical notes (195.6M clinical annotations) and chest radiographs into the OMOP note nlp and imaging extension tables, making all MIMIC-IV modalities (structured data, free-text, and imaging) accessible through a common data model. This resource generates a more comprehensive dataset than existing alternatives and is intended to be used to aid in system development, testing, and evaluation.

MedCAT is an open-source framework for clinical named entity recognition and linking (NER+L) widely used in research and healthcare settings. We present MedCAT v2, a re-engineered version designed to improve modularity, extensibility, and maintainability while preserving the core functionality and performance of previous releases. The new architecture introduces a registry-based component system and a flexible pipeline that enables easy substitution of components, integration of alternative methods, and future expansion, including support for pre-trained components across the full NER+L and contextualisation workflow. This enables systematic exploration of clinical NER+L design trade-offs by evaluating different components in the pipeline. Evaluation across multiple public datasets shows equivalent or improved performance compared to earlier versions, with reduced integration overhead and improved runtime flexibility. The framework also supports optional extensions such as meta-annotation, relation extraction, providing a unified and reproducible environment for clinical NLP in real-world settings.

Transformer-based Large Language Models (LLMs) have achieved remarkable success across various domains, including clinical language processing, where they enable state-of-the-art performance in numerous tasks. Like all deep learning models, LLMs are susceptible to inference attacks that exploit sensitive attributes seen during training. AnonCAT, a RoBERTa-based masked language model, has been fine-tuned to de-identify sensitive clinical textual data. The community has a responsibility to explore the privacy risks of these models. This work proposes an attack method to infer sensitive named entities used in the training of AnonCAT models. We perform three experiments; the privacy implications of generating multiple names, the impact of white-box and black-box on attack inference performance, and the privacy-enhancing effects of Differential Privacy (DP) when applied to AnonCAT. By providing real textual predictions and privacy leakage metrics, this research contributes to understanding and mitigating the potential risks associated with exposing LLMs in sensitive domains like healthcare.

In this work we use consumed text to infer Big-5 personality inventories using data we have collected from the social media platform Reddit. We test our model on two datasets, sampled from participants who consumed either fiction content (N = 913) or news content (N = 213). We show that state-of-the-art models from a similar task using authored text do not translate well to this task, with average correlations of r=.06 between the model’s predictions and ground-truth personality inventory dimensions. We propose an alternate method of generating average personality labels for each piece of text consumed, under which our model achieves correlations as high as r=.34 when predicting personality from the text being read.