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| http://dx.doi.org/10.1097/CM9.0000000000002856 | DOI Listing |
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Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.
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Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
View Article and Find Full Text PDFDevelopment and validation of a prognostic model based on m6A-related lncRNAs to predict prognosis for papillary renal cell cancer patients.
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Department of Urology Surgery, The First Affiliation Hospital of China Medical University, Shenyang, 110000, Liaoning, China.
To evaluate the predictive utility of N6-methyladenosine (m6A)-associated long non-coding RNAs (lncRNAs) for the prognosis and immunotherapy response in papillary renal cell carcinoma (pRCC). Transcriptomic data of pRCC samples were extracted from the TCGA database. The m6A-related lncRNAs were identified by Pearson correlation analysis.
View Article and Find Full Text PDFEarly prediction of functional impairment at hospital discharge in patients with osteoporotic vertebral fracture: a machine learning approach.
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Department of Pharmacoepidemiology, Graduate School of Medicine and PublicHealth, Kyoto University, Kyoto, Japan.
Although conservative treatment is commonly used for osteoporotic vertebral fracture (OVF), some patients experience functional disability following OVF. This study aimed to develop prediction models for new-onset functional impairment following admission for OVF using machine learning approaches and compare their performance. Our study consisted of patients aged 65 years or older admitted for OVF using a large hospital-based database between April 2014 and December 2021.
View Article and Find Full Text PDFBias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.
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Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.
View Article and Find Full Text PDFAssociation of sleep quality and its change with the risk of depression in middle-aged and elderly people: A 10-year cohort study from England.
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Department of Radiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.; Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China.. Electronic address:
Background: Persistently poor sleep quality in young adults is linked to a higher risk of depression. However, the impact of changes in sleep quality on depression risk in middle-aged and older adults remain unclear. This study investigates the association between sleep quality, its changes, and the risk of depression in middle-aged and elderly people.
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