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Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.
BMC Med Res Methodol
January 2025
Leeds Institute of Clinical Trials Research, University of Leeds, Clarendon Way, Leeds, LS2 9NL, UK.
Background: Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendations on how such models should be developed.
View Article and Find Full Text PDFChildhood BMI trajectories and sociodemographic factors in an Italian pediatric population.
Sci Rep
January 2025
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardio-Thoraco- Vascular Sciences and Public Health, University of Padua, Padua, Italy.
Childhood obesity is a growing global concern due to its long-term health consequences. Yet, more research relying on multiple time-point BMI measurements is warranted to gain further insight into obesity's temporal trends. We aimed to identify BMI trajectories in children aged 2-10 years and evaluate their association with sociodemographic factors.
View Article and Find Full Text PDFSpatiotemporal distribution of marine aerosols and gaseous species over the North Pacific Ocean.
Sci Total Environ
January 2025
Department of Environmental Science and Engineering, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Republic of Korea; Research Center for Strategic Solutions for Environmental Blindspots in the Interest of Society, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Republic of Korea. Electronic address:
Observational studies of marine aerosols are essential for understanding the global aerosol budget and its environmental impacts. This study presents simultaneous in-situ measurements of major ionic components (Cl, NO, SO, NH, K, Ca, Na, and Mg) in aerosols and gaseous species (HCl, HNO, HONO, SO, and NH) over the North Pacific Ocean from July 4 to 15 and September 19 to October 3, 2022. Using high temporal resolution instruments aboard the Republic of Korea's icebreaker research vessel Araon, this study aimed to (1) report the spatial and temporal distributions of aerosols and gaseous species, (2) estimate the source contributions of continental anthropogenic pollutants, and (3) assess the influence of aerosol chemical composition and gaseous species on aerosol acidity and water content.
View Article and Find Full Text PDFRetrospective cohort study of trends in the use of outpatient parathyroidectomy by surgical indication.
Surgery
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Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA; Center for Surgery and Health Economics, Department of Surgery, University of Pennsylvania, Philadelphia, PA; Leonard David Institute of Health Economics, University of Pennsylvania, Philadelphia, PA; Division of Endocrine and Oncologic Surgery, Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA.
Background: A shift toward outpatient parathyroidectomy was seen in the early 2000s. Recent trends, especially for secondary and tertiary hyperparathyroidism, have not been described. This study evaluates temporal trends in same-day, overnight, and inpatient parathyroidectomy by surgical indication.
View Article and Find Full Text PDFMapping nutrient pollution in inland water bodies using multi-platform hyperspectral imagery and deep regression network.
J Hazard Mater
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Shanghai Municipal Institute of Surveying and Mapping, Shanghai, 200063, China.
Inland waters face multiple threats from human activities and natural factors, leading to frequent water quality issues, particularly the significant challenge of eutrophication. Hyperspectral remote sensing provides rich spectral information, enabling timely and accurate assessment of water quality status and trends. To address the challenge of inaccurate water quality mapping, we propose a novel deep learning framework for multi-parameter estimation from hyperspectral imagery.
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