Aim: This systematic review evaluated the effects of telehome monitoring-based telenursing (THMTN) on health outcomes and use of healthcare services and compared them with the effects of conventional treatment in patients with severe and very severe chronic obstructive pulmonary disease (COPD). Methods: An extensive published work search of several databases was performed in May and October 2011. Randomized controlled trials and non-randomized controlled clinical trials were evaluated. Parameters included hospitalization rate, number of visits to the emergency department, exacerbations, mean number of hospitalizations, mean duration of bed days of care, mortality, and health-related quality of life by the duration of THMTN and COPD severity. A random effects model was applied. Risk ratio and mean difference were calculated. Heterogeneity was assessed using the I 2 statistic.Results: Nine original articles involving 550 participants were identified in the meta-analysis. THMTN decreased hospitalization rates, emergency department visits, exacerbations, mean number of hospitalizations, and mean duration of bed days of care in severe and very severe COPD patients. Hospitalization rates and emergency department visits were comparable between patients undergoing THMTN of different durations. In addition, THMTN had no effect on mortality. Conclusion: THMTN significantly decreases the use of healthcare services; however, it does not affect mortality in severe and very severe COPD patients.

Purpose Using a randomized controlled trial, we aimed to determine the effects of a home monitoring-based telenursing TN practice considering two endpoints preventing acute respiratory exacerbation primary outcome and readmission secondary outcome among chronic obstructive pulmonary disease COPD patients with home oxygen therapy HOT .Methods Thirty-seven COPD HOT patients were randomly assigned to TN experimental

No abstract

A fundamental issue in opinion mining is to search a corpus for opinion units, each of which typically comprises the evaluation by an author for a target object from an aspect, such as "This hotel is in a good location". However, few attempts have been made to address cases where the validity of an evaluation is restricted on a condition in the source text, such as "for traveling with small kids". In this paper, we propose a method to extract condition-opinion relations from online reviews, which enables fine-grained analysis for the utility of target objects depending the user attribute, purpose, and situation. Our method uses supervised machine learning to identify sequences of words or phrases that comprise conditions for opinions. We propose several features associated with lexical and syntactic information, and show their effectiveness experimentally.