Introduction: Data collection often relies on time-consuming manual inputs, with a vast amount of information embedded in unstructured texts such as patients' medical records and clinical notes. Our study aims to develop a pipeline that combines active learning (AL) and NLP techniques to enhance data extraction in an acute ischemic stroke cohort.
Materials And Methods: Consecutive acute ischemic stroke patients who received reperfusion therapies at IRCCS Humanitas Research Hospital were included.
Purpose: We undertook a trial to test the efficacy of a technology-assisted health coaching intervention for weight management, called Goals for Eating and Moving (GEM), within primary care.
Methods: This cluster-randomized controlled trial enrolled 19 primary care teams with 63 clinicians; 9 teams were randomized to GEM and 10 to enhanced usual care (EUC). The GEM intervention included 1 in-person and up to 12 telephone-delivered coaching sessions.
Background: COVID-19 clinical course is highly variable and secondary infections contribute to COVID-19 complexity. Early detection of secondary infections is clinically relevant for patient outcome. Procalcitonin (PCT) and C-reactive protein (CRP) are the most used biomarkers of infections.
View Article and Find Full Text PDFIntroduction: Intensive weight management programs are effective but often have low enrollment and high attrition. Lack of motivation is a key psychological barrier to enrollment, engagement, and weight loss. Mental Contrasting with Implementation Intentions (MCII) is a unique imagery technique that increases motivation for behavior change.
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