519 research outputs found
Impact of COVID-19 on emergency department use among home care recipients
BACKGROUND: The impact of COVID-19 pandemic on Emergency Department (ED) was remarkable throughout Europe. We focused upon ED utilization among integrated home care (IHC) recipients comparing ED between pandemic period with pre-pandemic (February -December 2020 and 2019, respectively) in Piedmont, Italy. METHODS: A retrospective observational study was conducted. All recipients of IHC during the two periods studied were enrolled and all ED visits that occurred among IHC recipients were accounted for. Several variables related to IHC admission, reason of ED visits and demographic characteristics were collected. The average of ED visits in pre-pandemic and pandemic periods were calculated. Analyses were stratified by all variables. RESULTS: Patients enrolled were 11968 in 2019 and 8938 in 2020. In 2019, 3573 patients had at least one ED visit and 1668 patients in 2020. Number of ED visits was 5503 in 2019 and 2197 in 2020. The average of ED visits in 2020 has reduced in comparison with 2019 (0.464 C.I. [0.44-0.489] and 0.24 C.I. [0.227-0.252], p < 0.001 in 2019 and 2020 respectively). This reduction is regardless of sex, age, duration of IHC, presence of a non-family caregiver or reason for ED visits, except for abdominal pain, cardiac rhythm alteration and gynaecological symptoms. The averages of ED visits were significantly lower for IHC recipients with neoplasm (0.549 C.I. [0.513-0.585] and 0.328 C.I. [0.298-0.358], p < 0.001, and with low level of emergency (1.77 C.I. [1.662-1.877] and 1.397 C.I. [1.348-1.447], p < 0.036), but an increase in mortality rate was not registered. CONCLUSIONS: Our results showed a reduction of ED visits among integrated home care recipients in pandemic period in comparison with pre-pandemic period. If the reduction can be the consequence of an unprepared health service that needs of necessary changes in its organization, these results suggest a great potential of the home care system to reduce the use of the hospital especially for low-risk conditions. KEY MESSAGES: • The COVID-19 pandemic overwhelmed health services of all European Countries. A reduced utilization of ED has been shown by literature, especially during the early phase of the COVID-19 pandemic. • We showed a reduction in IHC recipients and a great decrease in ED visits among IHC patients in 2020 versus 2019, mainly in oncological patients, while an increase in mortality rate was not reported
Compensation of Nuisance Factors for Speaker and Language Recognition
The variability of the channel and environment is
one of the most important factors affecting the performance of
text-independent speaker verification systems. The best techniques
for channel compensation are model based. Most of them have
been proposed for Gaussian mixture models, while in the feature
domain blind channel compensation is usually performed. The
aim of this work is to explore techniques that allow more accurate
intersession compensation in the feature domain. Compensating
the features rather than the models has the advantage that the
transformed parameters can be used with models of a different
nature and complexity and for different tasks. In this paper,
we evaluate the effects of the compensation of the intersession
variability obtained by means of the channel factors approach. In
particular, we compare channel variability modeling in the usual
Gaussian mixture model domain, and our proposed feature domain
compensation technique. We show that the two approaches
lead to similar results on the NIST 2005 Speaker Recognition
Evaluation data with a reduced computation cost. We also report
the results of a system, based on the intersession compensation
technique in the feature space that was among the best participants
in the NIST 2006 Speaker Recognition Evaluation. Moreover, we
show how we obtained significant performance improvement in
language recognition by estimating and compensating, in the
feature domain, the distortions due to interspeaker variability
within the same language.
Index Terms—Factor anal
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