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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.

This research aimed to design and evaluate a clinician-facing report generated from the SYmptom iNput Clinical (SYNC) system, a digital questionnaire for patients to report Head and Neck Cancer (HNC) symptoms and uses this to calculate a risk score. The research explored how the SYNC system could be integrated into existing hospital workflows.

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.

To evaluate whether large language models (LLMs) can automate chart review to identify tumor necrosis factor inhibitor (TNFi) switching patterns and reasons for switching in a large real-world cohort.

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.

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

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.

Critically ill patients are managed with complex medication regimens that require medication management to optimize safety and efficacy. When performed by a critical care pharmacist (CCP), discrete medication management activities are termed medication interventions. The ability to define CCP workflow and intervention timeliness depends on the ability to predict the medication management needs of individual intensive care unit (ICU) patients. The purpose of this study was to develop prediction models [...]

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).