Occurrence of pneumonia according to baseline timed up-and-go test results (n = 19,804).

NIAID Data Ecosystem2026-05-01 收录

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https://figshare.com/articles/dataset/Occurrence_of_pneumonia_according_to_baseline_timed_up-and-go_test_results_n_19_804_/24949872 数据链接链接失效反馈

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Occurrence of pneumonia according to baseline timed up-and-go test results (n = 19,804).

创建时间：

2024-01-05

Summary of the meta-analysis depicting the association between dosimetric factors and risk of RP≥ grade 2 after SBRT in lung cancer.

放疗剂量学分析

肺炎风险预测

Summary of the meta-analysis depicting the association between dosimetric factors and risk of RP≥ grade 2 after SBRT in lung cancer.

NIAID Data Ecosystem30

Table_1_Development and performance assessment of novel machine learning models for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage patients: external validation in MIMIC-IV.DOC

肺炎风险预测

神经外科预后预测

BackgroundPostoperative pneumonia (POP) is one of the primary complications after aneurysmal subarachnoid hemorrhage (aSAH) and is associated with postoperative mortality, extended hospital stay, and

NIAID Data Ecosystem20

Table_1_Videofluoroscopic Swallowing Study Findings Associated With Subsequent Pneumonia in Patients With Dysphagia Due to Frailty.docx

吞咽障碍评估

肺炎风险预测

Dysphagia in frailty or deconditioning without specific diagnosis that may cause dysphagia such as stroke, traumatic brain injury, or laryngeal pathology, has been reported in previous studies; howeve

NIAID Data Ecosystem20

Cox proportional hazards regression models for the predictive factors of early development of aspiration pneumonia.

肺炎风险预测

生存分析模型

Model 1, HR adjusted by age and sex; Model 2, HR adjusted by age, sex, and dysphagia (yes/no) during the first 2 years of the disease. Cox proportional hazards regression models for the predictive fac

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Table 1_Identifying oral disease variables associated with pneumonia emergence by application of machine learning to integrated medical and dental big data to inform eHealth approaches.docx

肺炎风险预测

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BackgroundThe objective of this study was to build models that define variables contributing to pneumonia risk by applying supervised Machine Learning-(ML) to medical and oral disease data to define k

NIAID Data Ecosystem20

Occurrence of pneumonia according to baseline timed up-and-go test results (n = 19,804).

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