Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11020234 | PMC |
| http://dx.doi.org/10.1093/geroni/igae031 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
F-18 FDG PET/CT based Preoperative Machine Learning Prediction Models for Evaluating Regional Lymph Node Metastasis Status of Patients with Colon Cancer.
Asian Pac J Cancer Prev
January 2025
Department of Nuclear Medicine, Busan Paik Hospital, University of Inje College of Medicine, Busan, Republic of Korea.
Objective: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and clinical information for predicting regional lymph node metastasis in patients with colon cancer.
Methods: This retrospective study included 193 patients diagnosed with colon cancer between January 2014 and December 2017. All patients underwent F-18 FDG PET/CT and blood test before surgery.
The escape room: will we be able to build a team?
Intern Emerg Med
January 2025
Center for Advanced Simulation in Medicine, University Hospital Careggi, Lg. Brambilla 3, 50134, Florence, Italy.
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.
Age-invariant genes: multi-tissue identification and characterization of murine reference genes.
Aging (Albany NY)
January 2025
Department of Pathology, Yale University School of Medicine, New Haven, CT 06519, USA.
Studies of the aging transcriptome focus on genes that change with age. But what can we learn from age-invariant genes-those that remain unchanged throughout the aging process? These genes also have a practical application: they can serve as reference genes in expression studies. Reference genes have mostly been identified and validated in young organisms, and no systematic investigation has been done across the lifespan.
View Article and Find Full Text PDFLeadership development programs in healthcare organizations: What they are and how to assemble them.
Health Serv Manage Res
February 2025
LSU Shreveport, Shreveport, LA, USA.
In the highly competitive, perennially-changing healthcare industry, management prowess is vitally needed by those in positions of authority, but in isolation it is insufficient for delivering optimal results. Instead, management proficiencies must be complemented by leadership skills which amplify managerial efforts and outcomes. One avenue for building leadership acumen in healthcare organizations involves the establishment of leadership development programs.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!