Predictors of the individual components of MACE in all patients (N = 2693).

Figshare2016-10-14 更新2026-04-29 收录

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Predictors of the individual components of MACE in all patients (N = 2693).

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2016-10-14

Table_1_Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study.pdf

临床预测模型

症状严重程度评估

BackgroundPrediction of the severity of COVID-19 at its onset is important for providing adequate and timely management to reduce mortality. ObjectiveTo study the prognostic value of damage parameters

NIAID Data Ecosystem30

Novel insights into predicting the presence of micropapillary and solid components in stage IA lung adenocarcinoma using machine learning models of modifiable risk factors

肺腺癌病理亚型预测

临床预测模型

Lung adenocarcinoma (LUAC) patients with micropapillary (MP) and/or solid (S) generally demonstrate a poorer survival prognosis. In the diagnosis and treatment of stage IA LUAC, precisely establishing

Taylor & Francis Group2025-12-21 更新20

Five year follow up of patients at high cardiovascular risk who took part in randomised controlled trial of health promotion

心血管疾病预后与预防

干预效果评估

Five year follow up of patients at high cardiovascular risk who took part in randomised controlled trial of health promotion

PubMed Central1999-09-11 更新10

Adjusted factors associated with indicators of kidney damage.

肾脏损伤风险因素

临床预测模型

afactors included in multivariate logistic regression were: age(change by 10 years), female, diabetes, hypertension, history of CKD, history of CVD, current smoking, hyperuricemia(>422 µmol/L for men,

Figshare2015-12-02 更新30

Datasheet1_A nomogram for predicting neonatal apnea: a retrospective analysis based on the MIMIC database.pdf

新生儿呼吸暂停

临床预测模型

IntroductionThe objective of this study is to develop a model based on indicators in the routine examination of neonates to effectively predict neonatal apnea. MethodsWe retrospectively analysed 8024

NIAID Data Ecosystem10

Predictors of the individual components of MACE in all patients (N = 2693).

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