Hospital Records Show Missing Clinical Data Influences Machine Learning Predictions
A retrospective hospital study reveals that routine documentation gaps and missingness indicators shape cardiovascular machine-learning predictions.
A retrospective hospital study reveals that routine documentation gaps and missingness indicators shape cardiovascular machine-learning predictions.
Researchers have developed and validated a claims-based algorithm that identifies transthyretin amyloid cardiomyopathy cases in administrative databases with high accuracy ...
A decade-long follow-up study found no significant linear or nonlinear association between the triglyceride glucose-body mass index and repeat percutaneous ...
A prospective study demonstrates that combining coronary flow velocity reserve and dynamic stress echocardiography parameters significantly enhances cardiac event prediction ...
A collection of 34 studies highlights emerging precision tools and clinical markers designed to individualize the structured AF-CARE pathway for ...
A two-cohort study reveals that heart attack patients without standard cardiovascular risk factors suffer significantly higher thirty-day mortality and neurological ...
Cardiologists from NYU Langone Health are presenting new clinical findings on coronary artery disease, indoor air quality, and heart failure ...
A new precision phenotyping framework challenges routine medication withdrawal by distinguishing six biological causes of low blood pressure in heart ...
A meta-analysis of 2.6 million patients reveals that individuals having heart attacks without traditional risk factors face higher early mortality ...
Annual transcatheter aortic valve replacements in patients under 70 increased nearly ninefold in a single health system, driven by individuals ...
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© 2026 Scienmag - Science Magazine