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One state's journey with a reflective supervision professional development series: Development, implementation, and adaptation.
Infant Ment Health J
January 2025
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Reflective supervision (RS) has been viewed as best practice and is therefore incorporated-and often mandated-as a key feature of many relationship-based infant and early childhood serving programs. To promote the implementation of high-quality RS for infant and early childhood professionals, it is critical that a focus is placed on how infant and early childhood professionals are trained to build RS capacities. To this end, we describe Rhode Island, United States's journey developing, implementing, and iteratively adapting an RS professional development series.
View Article and Find Full Text PDFCorrection to: Cone photoreceptor phosphodiesterase PDE6H inhibition regulates cancer cell growth and metabolism, replicating the dark retina response.
Cancer Metab
January 2025
Molecular Oncology Laboratories, Department of Medical Oncology, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
Fast and interpretable mortality risk scores for critical care patients.
J Am Med Inform Assoc
January 2025
Department of Computer Science, Duke University, Durham, NC 27708, United States.
Objective: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.
Material And Methods: We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them.
From circuits to lifespan: translating mouse and human timelines with neuroimaging based tractography.
J Neurosci
January 2025
Department of Anatomy, Physiology, and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, USA.
Animal models are commonly used to investigate developmental processes and disease risk, but humans and model systems (e.g., mice) differ substantially in the pace of development and aging.
View Article and Find Full Text PDFAI for all: bridging data gaps in machine learning and health.
Transl Behav Med
January 2025
Slone Epidemiology Center at Boston University, 72 E Concord St, Boston, MA, USA.
Artificial intelligence (AI) and its subset, machine learning, have tremendous potential to transform health care, medicine, and population health through improved diagnoses, treatments, and patient care. However, the effectiveness of these technologies hinges on the quality and diversity of the data used to train them. Many datasets currently used in machine learning are inherently biased and lack diversity, leading to inaccurate predictions that may perpetuate existing health disparities.
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