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Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.
EClinicalMedicine
December 2024
Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).
View Article and Find Full Text PDFIntegrating machine learning with bioinformatics for predicting idiopathic pulmonary fibrosis prognosis: developing an individualized clinical prediction tool.
Exp Biol Med (Maywood)
December 2024
Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a poor prognosis. Its non-specific clinical symptoms make accurate prediction of disease progression challenging. This study aimed to develop molecular-level prognostic models to personalize treatment strategies for IPF patients.
View Article and Find Full Text PDFMetyrapone single administration, as a possible predictive tool of its dosage and timing in Cushing's syndrome.
Front Endocrinol (Lausanne)
December 2024
Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Hospital, Kobe, Japan.
Metyrapone is commonly used in the initial management of Cushing's syndrome to reduce hypercortisolemia, but its optimal dosage and timing can vary significantly between patients. Currently, there are limited guidelines on adjustment methods for its administration to individual needs. This study aimed to evaluate responsiveness of each patient to metyrapone and identify the patient characteristics associated with the indices of cortisol responsiveness following a low-dose metyrapone.
View Article and Find Full Text PDFBenchmarking lung cancer screening programmes with adaptive screening frequency against the optimal screening schedules derived from the ENGAGE framework: a comparative microsimulation study.
EClinicalMedicine
August 2024
Division of Cancer Prevention and Population Sciences, Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Background: Lung cancer screening recommendations employ annual frequency for eligible individuals, despite evidence that it may not be universally optimal. The impact of imposing a structure on the screening frequency remains unknown. The ENGAGE framework, a validated framework that offers fully dynamic, analytically optimal, personalised lung cancer screening recommendations, could be used to assess the impact of screening structure on the effectiveness and efficiency of lung cancer screening.
View Article and Find Full Text PDFThe integration of conventional omics data such as genomics and transcriptomics data into artificial intelligence models has advanced significantly in recent years; however, their low applicability in clinical contexts, due to the high complexity of models, has been limited in their direct use inpatients. We integrated classic omics, including DNA mutation and RNA gene expression, added a novel focus on promising omics methods based on A>I(G) RNA editing, and developed a drug response prediction model. We analyzed 104 patients from the Breast Cancer Genome-Guided Therapy Study (NCT02022202).
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