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Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study.
Annals of medicine
Apr 7, 2025
BACKGROUND: Reducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival.
Pseudotargeted metabolomics profiles potential damage-associated molecular patterns as machine learning predictors for acute pancreatitis.
Journal of pharmaceutical and biomedical analysis
Apr 7, 2025
Acute pancreatitis (AP) is a common gastrointestinal disease characterized by pancreatic cell damage and inflammation. Given the early clinical diagnosis and management challenges, exploring novel analytical frameworks from new orientations for inter...
Integrating large language models with human expertise for disease detection in electronic health records.
Computers in biology and medicine
Apr 7, 2025
OBJECTIVE: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelli...
Explainable transformer-based deep survival analysis in childhood acute lymphoblastic leukemia.
Computers in biology and medicine
Apr 7, 2025
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common type of leukemia among children and adolescents and can be life-threatening. The incidence of new cases has been increasing in recent years. Developing a predictive model to forecast t...
Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma.
BMC cancer
Apr 7, 2025
BACKGROUND: Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melan...
CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.
Genome biology
Apr 7, 2025
Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with real...
Prediction of new-onset migraine using clinical-genotypic data from the HUNT Study: a machine learning analysis.
The journal of headache and pain
Apr 7, 2025
BACKGROUND: Migraine is associated with a range of symptoms and comorbid disorders and has a strong genetic basis, but the currently identified risk loci only explain a small portion of the heritability, often termed the "missing heritability". We ai...
Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.
Scientific reports
Apr 7, 2025
Advancements in medical technology have extended long-term electrocardiogram (ECG) monitoring from the traditional 24 h to 7-14 days, significantly enriching ECG data. However, this poses unprecedented challenges for physicians in analyzing these ext...
Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies.
Scientific reports
Apr 7, 2025
Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessi...
Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.
Scientific reports
Apr 7, 2025
Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history,...
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