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With the rapid growth of unstructured clinical narratives in electronic health records (EHRs), clinical named entity recognition (NER) has become a crucial technique for extracting structured medical information. However, traditional supervised models such as CRF and BioClinicalBERT rely on costly manual annotations. Although large language model (LLM)-based zero-shot NER reduces the dependency on labeled data, challenges remain in aligning example selection with task granularity and in effectively integrating prompt design [...]

Characterise longitudinal patterns of chiropractic visits for neck pain or low back pain by using machine learning (ML) methods and explainable models.

The "Projections In Multiple Sclerosis" (PRIMUS) project aims to develop a precision medicine platform enabling neurologists to support therapeutic decisions in multiple sclerosis by visualizing similar patient data in a reference database. We present a data integration method to combine randomized clinical trials (RCTs) and observational studies data and optimize the informativeness of the resulting database.

This study aims to develop and evaluate a trustworthy and ethical-by-design machine learning (ML) framework for predicting 5-year cancer survival across 10 Surveillance, Epidemiology, and End Results (SEER) cancer types. We assessed ML model(s) performance across localized, regional, and distant stages while examining ML fairness, ML explainability, and the added value of social determinants of health (SDOH) as features. The goal is to advance clinically interpretable and equity-centered survival prediction [...]

To develop a large-language-model (LLM)-centric workflow flow extraction and migration of clinician-documented colonoscopy recall recommendations from unstructured reports and letters during an enterprise-wide electronic health record (EHR) transition.

Electronic Health Record (EHR) data are increasingly used in cancer research, yet the fidelity of this data when exchanged between systems remains poorly quantified. This study investigated the agreement in essential biomarker data after they are passed from the EHR into the cancer registry and Fast Healthcare Interoperability Resources (FHIR) extracts.

In the absence of standardized measures, researchers have struggled to define meaningful use of telehealth (care received via video or telephone). We evaluated a novel Telehealth Engagement Measure (TEM) that quantifies an individual's telehealth use, including telephone (TEM-t) and video (TEM-v), as a percentage of total care received.

To use bibliometric methods to deeply analyze the application of WeChat in China's mHealth field, outline its research panorama, and clarify research frontiers.

Patient portals support health management, yet enrollment remains low. This study evaluated whether timely email nudges increase patient portal enrollment compared with usual system portal invitations, whether message framing influences enrollment, and whether patients nudged to enroll go on to engage with the portal at similar rates as those who enroll organically.

Implementing biomedical informatics (BMI) tools and applications into clinical and other settings remains a significant challenge. Unintended consequences remain common post-implementation. This paper addresses the above issue by integrating systems thinking and implementation science approaches to support health information technology (HIT) implementation.