Researchers developed a two-stage machine learning framework to separate prolonged-stay risk stratification from day-level hospital stay estimation using 209,088 inpatient ...
A retrospective hospital study reveals that routine documentation gaps and missingness indicators shape cardiovascular machine-learning predictions.
A multicenter study of 51,843 emergency patients reveals that extracting structured chest CT findings with a large language model reliably ...
A sequence-clustering framework applied to longitudinal electronic health records uncovered five distinct pre-surgical care pathways with varying progression timelines and ...
A retrospective multicenter study demonstrates that a multimodal Transformer model tracking inflammatory and metabolic dynamics can stratify postoperative complication risks ...
A systematic review reveals that while biological age prediction models vary widely, a core set of routine blood biomarkers frequently ...
A new study demonstrates that Quantum Support Vector Machines can outperform classical models in detecting physiological anomalies in noisy ICU ...
Researchers developed and deployed a fine-grained natural language processing framework that accurately extracts context-aware medication details from unstructured Hungarian clinical ...
In a retrospective two-centre study, an artificial intelligence electrocardiogram model integrating age and sex demonstrated discriminative value for acute aortic ...
A retrospective study of 284 notifiable-disease episodes found delayed and inadequate hospital notifications while mapping electronic records that could support ...
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