Search

No study has examined the varying family experience of palliative sedation therapy (PST) for terminally ill patients in different settings.To examine and compare family concerns about PST use and its effect on the grief suffered by terminally ill patients' families in palliative care units (PCUs) or intensive care units (ICUs).A total of 154 family members of such patients were recruited in Taiwan, of whom 143 completed the study, with 81 from the PCU and 62 from the ICU. Data were collected on their concerns regarding PST during recruitment. Grief levels were assessed at three days and one month after the patient's death with the Texas Revised Inventory of Grief.Families' major concern about sedated patients in the PCU was that "there might be other ways to relieve symptoms" (90.2%), whereas families of ICU sedated patients gave the highest ratings to "feeling they still had something more to do" (93.55%), and "the patient's sleeping condition was not dignified" (93.55%). Family members recruited from the ICU tended to experience more grief than those from the PCU (P = 0.005 at Day 3 and 0.001 at Month 1). PST use predicted higher levels of grief in family members recruited from the PCU (P 0.001 at Day 3 and Month 1).Family experiences with the use of PST in terminally ill patients varied in different settings. Supportive care should address family concerns about PST use, and regular attention should be paid to the grief of individuals at higher risk.

This study aims to compare two machine learning techniques, random forests (RF) and support vector machine (SVM), for real-time radar-derived rainfall forecasting. The real-time radar-derived rainfall forecasting models use the present grid-based radar-derived rainfall as the output variable and use antecedent grid-based radar-derived rainfall, grid position (longitude and latitude) and elevation as the input variables to forecast 1- to 3-h ahead rainfalls for all grids in a catchment. Grid-based radar-derived rainfalls of six typhoon events during 2012–2015 in three reservoir catchments of Taiwan are collected for model training and verifying. Two kinds of forecasting models are constructed and compared, which are single-mode forecasting model (SMFM) and multiple-mode forecasting model (MMFM) based on RF and SVM. The SMFM uses the same model for 1- to 3-h ahead rainfall forecasting; the MMFM uses three different models for 1- to 3-h ahead forecasting. According to forecasting performances, it reveals that the SMFMs give better performances than MMFMs and both SVM-based and RF-based SMFMs show satisfactory performances for 1-h ahead forecasting. However, for 2- and 3-h ahead forecasting, it is found that the RF-based SMFM underestimates the observed radar-derived rainfalls in most cases and the SVM-based SMFM can give better performances than RF-based SMFM.