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| http://dx.doi.org/10.3389/frai.2022.1048568 | DOI Listing |
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A lightweight generative model for interpretable subject-level prediction.
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Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland; Department of Computer Science, Aalto University, Finland.
Recent years have seen a growing interest in methods for predicting an unknown variable of interest, such as a subject's diagnosis, from medical images depicting its anatomical-functional effects. Methods based on discriminative modeling excel at making accurate predictions, but are challenged in their ability to explain their decisions in anatomically meaningful terms. In this paper, we propose a simple technique for single-subject prediction that is inherently interpretable.
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View Article and Find Full Text PDFInformation needs for cancer screening and associated factors of information-seeking behaviour: a qualitative systematic review.
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School of Nursing, Nanjing Medical University, Nanjing, China.
Background: At present, the participation rate in cancer screening is still not ideal, and the lack of screening information or misunderstanding of information is an important factor hindering cancer screening behaviour. Therefore, a systematic synthesis of information needs related to cancer screening is critical.
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Background: Frailty in older adults is linked to increased risks and lower quality of life. Pre-frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre-frailty risk assessment among community-dwelling older adults.
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