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To identify causal mechanisms driving variations in the impact of rehabilitation treatments on stroke survivors' independence improvement during rehabilitation inpatient stays.

A point-of-care resource is required to support Registered Dietitian Nutritionists (RDNs) in providing comprehensive culturally-tailored nutrition services. This study aims to describe the iterative development and testing of a comprehensive nutrition assessment tool as part of a novel web-based platform to Support Personalized and Inclusive Cuisines in Environments for Healthcare (SPICE-Healthcare).

To address algorithmic bias in clinical prediction models related to the timing of diagnosis, we evaluated the efficacy of mixture cure models that integrate time-to-event and binary classification frameworks to predict diagnoses.

Sharing clinical data for research that is both collaborative and privacy-preserving remains a challenge. Differential privacy (DP) offers a solution by introducing noise to query results. Using the PrivateSQL DP platform, this study assesses the resulting utility of differentially private data at different levels of aggregation through analyses of COVID-19 pandemic associations with new cancer diagnosis counts (NCCs).

Clinician perspectives on machine learning and artificial intelligence (ML/AI) vary with discipline. However, fewer studies describe patient perspectives and address medical situations that require rapid decisions with durable consequences for patient outcomes. This study characterized perspectives (qualitatively) and sentiment (quantitatively) regarding the use of ML/AI for patient management in neurological emergencies.

To present the Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform-unified access to diverse clinical datasets from the Mount Sinai Health System (MSHS), along with computational infrastructure for AI-driven research and demonstrate its utility with 3 research projects.

To evaluate large language models (LLMs) for automating the assessment of clinician adherence to ESC heart failure pharmacotherapy guidelines.

Different natural language processing (NLP) techniques have demonstrated promising results for data extraction from radiological reports. Both traditional rule-based methods like regular expressions (Regex) and modern large language models (LLMs) can extract structured information. However, comparison between these approaches for extraction of specific radiological data elements has not been widely conducted.

Systemic mastocytosis (SM) is a rare disorder with heterogeneous, multisystem symptoms that often lead to diagnostic delays. Real-world symptom documentation in unstructured clinical notes may hold untapped potential for earlier recognition, but scalable extraction methods are lacking.

Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative artificial intelligence (GenAI) pipeline using an open-source Large Language Model (LLM) with built-in guardrails and a retry mechanism to extract data from unstructured right heart catheterization (RHC) notes while minimizing errors, including hallucinations.