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A large language model for delirium prediction in the intensive care unit using structured electronic health records.

Scientific reports
Delirium is an acute syndrome characterized by fluctuating attention, cognitive impairment, and severe disorganization of behavior, which has been shown to affect up to 31% of patients in the intensive care unit (ICU). Early detection can enable time...

Integrating machine learning and time-to-event models to explain and predict risk of hospitalization due to dengue in Colombia.

Scientific reports
Arboviral diseases such as dengue pose major public health challenges in endemic regions, notably in Norte de Santander (Colombia), where they place substantial pressure on healthcare services. We analyzed 8,814 confirmed dengue cases reported to the...

Large Language Model Versus Manual Review for Clinical Data Curation in Breast Cancer: Retrospective Comparative Study.

JMIR medical informatics
BACKGROUND: Manual review of electronic health records for clinical research is labor-intensive and prone to reviewer-dependent variations. Large language models (LLMs) offer potential for automated clinical data extraction; however, their feasibilit...

Preoperative plasma ceramide profiling coupled with machine learning accurately predicts recurrence of hepatocellular carcinoma after resection.

Lipids in health and disease
BACKGROUND: Accurate stratification of recurrence risk after curative resection remains a critical challenge in the management of hepatocellular carcinoma (HCC). Dysregulated ceramide (CER) metabolism has been implicated in HCC progression and relaps...

Torso synthetic CT generation by integrating deep learning and segmentation for FDG-PET/MR attenuation correction.

Biomedical physics & engineering express
Positron Emission Tomography/Magnetic Resonance () offers benefits over PET/CT including simultaneous PET and MR acquisition, intrinsic spatial registration accuracy, MR-based functional information, and superior soft tissue contrast. However, accura...

Reconstructing strontium-90 intake in beagles using neural networks: a data-driven assessment of historical inhalation records.

Journal of radiological protection : official journal of the Society for Radiological Protection
Dose estimation in response to internal radionuclide exposures requires reconstruction of the initial intake activity, which is frequently unknown due to the absence ofdata. In such scenarios, intake is inferred from bioassay measurements obtained at...

Deep generative models design mRNA sequences with enhanced translational capacity and stability.

Science (New York, N.Y.)
Despite the success of messenger RNA (mRNA) COVID-19 vaccines, extending this modality to more diseases necessitates substantial enhancements. We present GEMORNA, a generative RNA model that uses transformer architectures tailored for mRNA coding seq...

Interpretable machine learning for cardiovascular risk prediction: Insights from NHANES dietary and health data.

PloS one
BACKGROUND: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination...

Impact of blood culture positivity at intensive care unit admission on mortality in infective endocarditis: Machine learning and deep learning-based causal inference models.

PloS one
BACKGROUND: Infective endocarditis (IE) carries high in-hospital mortality, particularly among intensive care unit (ICU) patients. The predictive role of blood culture positivity in these patients remains unclear.