Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Preoperative risk prediction is an important component of perioperative medicine. Machine learning is a powerful tool that could lead to increasingly complex risk prediction models with improved predictive performance. Careful consideration is required to guide the machine learning approach to ensure appropriate decisions are made with regard to what we are trying to predict, when we are trying to predict it, and what we seek to do with the results.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.bja.2024.07.015 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Inferring tephritid fly pupal development stage using non-invasive near infrared imaging and machine learning classification.
Bull Entomol Res
September 2025
Instituto de Biotecnología y Ecología Aplicada, Universidad Veracruzana, Xalapa, Veracruz, México.
Insect pupae change morphologically (e.g., pigmentation of eyes, wings, setae and legs) during the intrapuparial period.
View Article and Find Full Text PDFProfiling the Chemical Exposomic Landscape of Esophageal Squamous Cell Carcinoma.
Environ Sci Technol
September 2025
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
While the cancer genome is well-studied, the nongenetic exposome of cancer remains elusive, particularly for regionally prevalent cancers with poor prognosis. Here, by employing a combined knowledge- and data-driven strategy, we profile the chemical exposome of plasma from 53 healthy controls, 14 esophagitis and 101 esophageal squamous cell carcinoma (ESCC) patients, and 46 esophageal tissues across 12 Chinese provinces, integrating inorganic, endogenous, and exogenous chemicals. We first show that components of the ESCC chemical exposome mediate the relationship between ESCC-related dietary/lifestyle factors and clinic health status indicators.
View Article and Find Full Text PDFSocial Participation and Depressive Symptoms Among Older Adults.
JAMA Netw Open
September 2025
Department of Social Epidemiology, Graduate School of Medicine and School of Public Health, Kyoto University, Kyoto, Japan.
Importance: Previous studies have suggested that social participation helps prevent depression among older adults. However, evidence is lacking about whether the preventive benefits vary among individuals and who would benefit most.
Objective: To examine the sociodemographic, behavioral, and health-related heterogeneity in the association between social participation and depressive symptoms among older adults and to identify the individual characteristics among older adults expected to benefit the most from social participation.
Artificial intelligence in nutrition science: Balancing innovation and ethical responsibility.
Nutr Health
September 2025
Independent researcher, Rome, Italy.
Artificial intelligence (AI) is increasingly applied in nutrition science to support clinical decision-making, prevent diet-related diseases such as obesity and type 2 diabetes, and improve nutrition care in both preventive and therapeutic settings. By analyzing diverse datasets, AI systems can support highly individualized nutritional guidance. We focus on machine learning applications and image recognition tools for dietary assessment and meal planning, highlighting their potential to enhance patient engagement and adherence through mobile apps and real-time feedback.
View Article and Find Full Text PDFFetalMLOps: operationalizing machine learning models for standard fetal ultrasound plane classification.
Med Biol Eng Comput
September 2025
Department of Computer Science, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging.
View Article and Find Full Text PDF