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| http://dx.doi.org/10.1186/1297-9686-18-1-41 | DOI Listing |
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Background: Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus complicating the prediction of clinical outcomes for different severe acute respiratory syndrome coronavirus (SARS-CoV-2) variants.
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West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness.
View Article and Find Full Text PDFArtificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.
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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.
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Department of Gastroenterology, the Second Hospital of Dalian Medical University, Dalian, 116021, China.
The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, potential mechanisms, diagnostic markers, and therapeutic targets for CAD and NAFLD.
View Article and Find Full Text PDFA copy number variation detection method based on OCSVM algorithm using multi strategies integration.
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School of Computer Science and Technology, Liaocheng University, Liaocheng, 252000, Shandong, P.R. China.
Copy number variation (CNV) is an important part of human genetic variations, which is associated with various kinds of diseases. To tackle the limitations of traditional CNV detection methods, such as restricted detection types, high error rates, and challenges in precisely identifying the location of variant breakpoints, a new method called MSCNV (copy number variations detection method for multi-strategies integration based on a one-class support vector machine model) is proposed. MSCNV establishes a multi-signal channel that integrates three strategies: read depth, split read, and read pair.
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