R script to develop ML models that dynamically predict CBTI outcomes

Mendeley Data2026-04-09 收录

失眠治疗疗效预测

机器学习临床建模

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The R script in"Machine learning models incorporating dynamic variables to predict outcomes of cognitive behavioral therapy for insomnia: a retrospective longitudinal study"

Random forest models of age at onset of T1D.

糖尿病预后

机器学习临床建模

Random forest models of age at onset of T1D.

NIAID Data Ecosystem100

Table_1_The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.docx

癌症淋巴结转移预测

机器学习临床建模

BackgroundLymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed

NIAID Data Ecosystem50

Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: a retrospective cohort study

感染风险预测

机器学习临床建模

Objective: The aim of the current study was to develop two predictive models, using data from the index admission as well as historic data on a patient, to predict the development of UTI at the time o

DataONE2021-03-14 更新40

Data_Sheet_1_Identifying Risk Factors for Complicated Post-operative Course in Tetralogy of Fallot Using a Machine Learning Approach.docx

先天性心脏病术后风险预测

机器学习临床建模

Introduction: Tetralogy of Fallot (TOF) repair is associated with excellent operative survival. However, a subset of patients experiences post-operative complications, which can significantly alter th

NIAID Data Ecosystem60

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records

急诊入院风险预测

机器学习临床建模

BackgroundEmergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admis

Figshare2018-11-20 更新50

R script to develop ML models that dynamically predict CBTI outcomes

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