Cargando…
Fast Prediction of Deterioration and Death Risk in Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease Using Vital Signs and Admission History: Retrospective Cohort Study
BACKGROUND: Chronic obstructive pulmonary disease (COPD) has 2 courses with different options for medical treatment: the acute exacerbation phase and the stable phase. Stable patients can use the Global Initiative for Chronic Obstructive Lung Disease (GOLD) to guide treatment strategies. However, GO...
Autores principales: | Zhou, Mi, Chen, Chuan, Peng, Junfeng, Luo, Ching-Hsing, Feng, Ding Yun, Yang, Hailing, Xie, Xiaohua, Zhou, Yuqi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6913742/ https://www.ncbi.nlm.nih.gov/pubmed/31638595 http://dx.doi.org/10.2196/13085 |
Ejemplares similares
-
A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators
por: Peng, Junfeng, et al.
Publicado: (2020) -
Author Correction: A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators
por: Peng, Junfeng, et al.
Publicado: (2021) -
Identification of exacerbation risk in patients with liver dysfunction using machine learning algorithms
por: Peng, Junfeng, et al.
Publicado: (2020) -
Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study
por: Tseng, Tzu-Wei, et al.
Publicado: (2022) -
Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study
por: Peng, Junfeng, et al.
Publicado: (2020)