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نتیجه جستجو - Patient admission

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Impact of the 2005 and 2010 Spanish smoking laws on hospital admissions for tobacco-related diseases in Valencia, Spain
تأثیر قوانین مربوط به استعمال دخانیات 2005 و 2010 اسپانیا بر روی بستری در بیمارستان برای بیماری های مرتبط با دخانیات در والنسیا ، اسپانیا-2020
Objectives: This study aimed to assess the impact of the latest smoke-free legislation on hospital admission rates due to smoking-related diseases in Spain. Study design: A retrospective cohort study was conducted to evaluate changes in hospital admission rates for cardiovascular, respiratory diseases, and smoking-related cancer in Valencia, Spain, during the period 1995e2013. Law 28/2005 and then law 42/2010 prohibited smoking in bars and restaurants as well as playgrounds and access points to schools and hospitals. Methods: General population data by age and sex were obtained from the National Institute of Statistics census. Data on hospital admissions were obtained from the Minimum Basic Data Set. Diagnoses were codified according to the International Classification of Diseases- 9th revision. Data from all hospitals of the Valencian Community from 1995 to 2013 were analysed. Trend analyses in the periods before and after the approval of the 2005 law were conducted using least-squares linear regression models. Results: Adjusted hospital admission rates per 100,000 inhabitants for cardiovascular diseases significantly decreased after the 2005 Law (from 550.0/100,000 in 2005 to 500.5/100,000 in 2007), with a further decrease (to 434.6/100,000) in 2013, after the 2010 Law. Reductions in hospital admissions were seen in men and women, although declining trends were more marked in men. Hospital admission rates for respiratory diseases showed a reduction of a lower magnitude, whereas for smoking-related cancer admissions there was a slight decline only among men. Conclusions: The Spanish comprehensive smoking ban resulted in a remarkable reduction of the adjusted rate of hospital admissions mainly associated to cardiovascular diseases. The decrease in the number of persons requiring in-patient care is relevant and may be viewed as an improvement of the publics health.
Keywords: Smoking/prevention and control | Smoke-free policies | Cardiovascular diseases/prevention | and control | Patient admission | Health policy
مقاله انگلیسی
2 Development of machine learning algorithms for prediction of mortality in spinal epidural abscess
توسعه الگوریتم های یادگیری ماشین برای پیش بینی مرگ و میر در آبسه اپیدورال ستون فقرات-2019
BACKGROUND CONTEXT: In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements. Forecasting this potentially avoidable consequence at the time of admission could improve patient management and counseling. Few studies exist to meet this need, and none have explored methodologies such as machine learning. PURPOSE: The purpose of this study was to develop machine learning algorithms for prediction of in-hospital and 90-day postdischarge mortality in SEA. STUDY DESIGN/SETTING: Retrospective, case-control study at two academic medical centers and three community hospitals from 1993 to 2016. PATIENTS SAMPLE: Adult patients with an inpatient admission for radiologically confirmed diagnosis of SEA. OUTCOME MEASURES: In-hospital and 90-day postdischarge mortality. METHODS: Five machine learning algorithms (elastic-net penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed and assessed by discrimination, calibration, overall performance, and decision curve analysis. RESULTS: Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. The variables used for prediction of 90-day mortality, ranked by importance, were age, albumin, platelet count, neutrophil to lymphocyte ratio, hemodialysis, active malignancy, and diabetes. The final algorithm was incorporated into a web application available here: https://sorg-apps.shinyapps.io/seamortality/. CONCLUSIONS: Machine learning algorithms show promise on internal validation for prediction of 90-day mortality in SEA. Future studies are needed to externally validate these algorithms inindependent populations.
Keywords: Artificial intelligence | Healthcare | Machine learning | Mortality | Spinal epidural abscess | Spine surgery
مقاله انگلیسی
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