Enhancing out-of-hospital emergency care via lexical machine learning modeling of chief complaints
Aaron C. Weidman, Remle P. Crowe, Ali Treichel, Francis X. Guyette, David D. Salcido
Aaron C. Weidman, Remle P. Crowe, Ali Treichel, Francis X. Guyette, David D. Salcido
Ethan Williams, Toshi Sinha, Matthew Summerscales, Yogesan Kanagasingam
Abstract Machine learning models that predict hospital admission at triage may support patient flow forecasting, yet the effects of covariate drift, concept drift, and retraining on long-term performance are poorly understood. We developed an Extreme Gradient Boosting (XGBoost) m…
Mehrdad Jamali, Meysam Zarezadeh, Mohammad Vesal Bideshki, Mohamed Khalifa, Michelle Cavaleri, Ahmad Saedisomeolia, et al.
Wenyu Zhang, Christina Pamporaki, René Jäkel, Georgiana Constantinescu, Mirko Peitzsch, Manuel Schulze, et al.
Abstract Commonly used screening tests for primary aldosteronism (PA) provide suboptimal diagnostic accuracy, particularly with antihypertensive medication use. This study utilized three datasets totaling 1380 patients with and without PA to develop machine learning models for sc…
Abstract Cancer multi-omics faces challenges in handling the scale, complexity, and heterogeneity of multi-omics data, limiting progress in variant interpretation, tumor classification, and modeling cancer evolution. Quantum computing offers a new paradigm using superposition, en…
Botang Guo, Shiqi Li, Minyao Li, Yuanshuo Ma, Ying Fu, Yihao Shu, et al.