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Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study.

, , , , , and . BMC Medical Informatics Decis. Mak., 23 (1): 168 (December 2023)

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Brain tissue volumes in the general population of the elderly: The AGES-Reykjavik Study., , , , , , , , , and 3 other author(s). NeuroImage, 59 (4): 3862-3870 (2012)Hip Fracture Prediction using the First Principal Component Derived from FEA-Computed Fracture Loads., , , , , , , , , and . CoRR, (2022)Large-scale parcellation of the ventricular system using convolutional neural networks., , , , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 10953 of SPIE Proceedings, page 109530N. SPIE, (2019)Fast and robust femur segmentation from computed tomography images for patient-specific hip fracture risk screening., , , , , , , , , and . Comput. methods Biomech. Biomed. Eng. Imaging Vis., 11 (2): 253-265 (March 2023)Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder., , , , and . CoRR, (2018)Predicting changes in quality of life for patients in vocational rehabilitation., , , , and . EAIS, page 1-8. IEEE, (2018)Structural covariability hubs in old age., , , , and . NeuroImage, (2019)The use of predictive models in dynamic treatment planning., , , , and . ISCC, page 242-247. IEEE Computer Society, (2017)Fast and Robust Femur Segmentation from Computed Tomography Images for Patient-Specific Hip Fracture Risk Screening., , , , , , , , , and . CoRR, (2022)Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study., , , , , and . BMC Medical Informatics Decis. Mak., 23 (1): 168 (December 2023)