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TruaceTracing the truth around AIWednesday, August 5, 2026
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TRV-2026-0641Certified recordPeer-reviewed

Comparison of Machine Learning and Logistic Regression in Predicting Mortality from Acute Poisoning in Young Adults: A Multicenter Study Identifying Herbicide Exposure as the Predominant Risk Determinant

Objective Compare the effectiveness of machine learning algorithms and traditional logistic regression in predicting the mortality risk of young patients with acute poisoning, and establish a risk stratification nomogram. Methods This multicenter retrospective study derived a derivation cohort of 406 young adults with acute poisoning from Wenzhou and an external validation cohort of 150 patients from Lishui.LASSO regression was used to screen predictive factors from 43 candidate variables. Compare the predictive…

Health · G Space — documented gain · certified 2026-08-04 · v1 · article view · machine-readable

Current reading — gain

Logistic regression achieved comparable discrimination to 14 machine learning algorithms for 28-day mortality prediction in young adults with acute poisoning and enabled a clinically usable nomogram for early risk stratification.

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Truvace Impact Record TRV-2026-0641, v1: “Comparison of Machine Learning and Logistic Regression in Predicting Mortality from Acute Poisoning in Young Adults: A Multicenter Study Identifying Herbicide Exposure as the Predominant Risk Determinant.” Truvace, 2026-08-04. /record/TRV-2026-0641 (accessed at citation time). sha256 4d05bcf10ac20c80…

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