• MeSH
• rozšiřování inovací MeSH
• sociální změna MeSH
• socioekonomické faktory MeSH
• technologie trendy   MeSH
• Publikační typ
• periodika MeSH

• Konspekt
• Veřejné zdraví a hygiena
• NLK Obory
• sociologie
• environmentální vědy
• technika

• MeSH
• populační růst MeSH
• předpověď MeSH
• statistika přirozeného pohybu MeSH

• Konspekt
• Demografie. Populace
• NLK Obory
• demografie

This article deals with predicting the results of sport games in the selected sports segments. The Potůček (2006) quantitative and qualitative predicting methods (Štědroň, 2012, 2014, 2015) offer many procedures and models. The method of Allan Lichtman, which was tested in the USA during the presidential election, was chosen for our experiment. The method was modified for the sports segment. Preliminary results demonstrate that the analysed method can become a basis for forecasting in sports.

• MeSH
• heuristika MeSH
• lidé MeSH
• pravděpodobnost MeSH
• předpověď * metody   MeSH
• sporty * MeSH
• Check Tag
• lidé MeSH

OBJECTIVE: This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy Assist Device) in a pseudo-prospective mode using iEEG from four canines with naturally occurring epilepsy. APPROACH: The system was trained and tested on 75 seizures collected over 1608 d utilizing a genetic algorithm to optimize forecasting hyper-parameters (prediction horizon (PH), median filter window length, and probability threshold) for each subject-specific seizure forecasting model. The trained CNN models were deployed on a hand-held tablet computer and tested on testing iEEG datasets from four canines. The results from the iEEG testing datasets were compared with Monte Carlo simulations using a Poisson random predictor with equal time in warning to evaluate seizure forecasting performance. MAIN RESULTS: The results show the CNN models forecasted seizures at rates significantly above chance in all four dogs (p   <  0.01, with mean 0.79 sensitivity and 18% time in warning). The deep learning method presented here surpassed the performance of previously reported methods using computationally expensive features with standard machine learning methods like logistic regression and support vector machine classifiers. SIGNIFICANCE: Our findings principally support the feasibility of deploying trained CNN models on a hand-held computational device (Mayo Epilepsy Assist Device) that analyzes streaming iEEG data for real-time seizure forecasting.

• MeSH
• deep learning * MeSH
• elektrokortikografie přístrojové vybavení   metody   MeSH
• epilepsie diagnóza   patofyziologie   MeSH
• implantované elektrody * MeSH
• předpověď MeSH
• psi MeSH
• záchvaty diagnóza   patofyziologie   MeSH
• zvířata MeSH
• Check Tag
• psi MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Smoking is the leading behavioural risk factor for mortality globally, accounting for more than 175 million deaths and nearly 4·30 billion years of life lost (YLLs) from 1990 to 2021. The pace of decline in smoking prevalence has slowed in recent years for many countries, and although strategies have recently been proposed to achieve tobacco-free generations, none have been implemented to date. Assessing what could happen if current trends in smoking prevalence persist, and what could happen if additional smoking prevalence reductions occur, is important for communicating the effect of potential smoking policies. METHODS: In this analysis, we use the Institute for Health Metrics and Evaluation's Future Health Scenarios platform to forecast the effects of three smoking prevalence scenarios on all-cause and cause-specific YLLs and life expectancy at birth until 2050. YLLs were computed for each scenario using the Global Burden of Disease Study 2021 reference life table and forecasts of cause-specific mortality under each scenario. The reference scenario forecasts what could occur if past smoking prevalence and other risk factor trends continue, the Tobacco Smoking Elimination as of 2023 (Elimination-2023) scenario quantifies the maximum potential future health benefits from assuming zero percent smoking prevalence from 2023 onwards, whereas the Tobacco Smoking Elimination by 2050 (Elimination-2050) scenario provides estimates for countries considering policies to steadily reduce smoking prevalence to 5%. Together, these scenarios underscore the magnitude of health benefits that could be reached by 2050 if countries take decisive action to eliminate smoking. The 95% uncertainty interval (UI) of estimates is based on the 2·5th and 97·5th percentile of draws that were carried through the multistage computational framework. FINDINGS: Global age-standardised smoking prevalence was estimated to be 28·5% (95% UI 27·9-29·1) among males and 5·96% (5·76-6·21) among females in 2022. In the reference scenario, smoking prevalence declined by 25·9% (25·2-26·6) among males, and 30·0% (26·1-32·1) among females from 2022 to 2050. Under this scenario, we forecast a cumulative 29·3 billion (95% UI 26·8-32·4) overall YLLs among males and 22·2 billion (20·1-24·6) YLLs among females over this period. Life expectancy at birth under this scenario would increase from 73·6 years (95% UI 72·8-74·4) in 2022 to 78·3 years (75·9-80·3) in 2050. Under our Elimination-2023 scenario, we forecast 2·04 billion (95% UI 1·90-2·21) fewer cumulative YLLs by 2050 compared with the reference scenario, and life expectancy at birth would increase to 77·6 years (95% UI 75·1-79·6) among males and 81·0 years (78·5-83·1) among females. Under our Elimination-2050 scenario, we forecast 735 million (675-808) and 141 million (131-154) cumulative YLLs would be avoided among males and females, respectively. Life expectancy in 2050 would increase to 77·1 years (95% UI 74·6-79·0) among males and 80·8 years (78·3-82·9) among females. INTERPRETATION: Existing tobacco policies must be maintained if smoking prevalence is to continue to decline as forecast by the reference scenario. In addition, substantial smoking-attributable burden can be avoided by accelerating the pace of smoking elimination. Implementation of new tobacco control policies are crucial in avoiding additional smoking-attributable burden in the coming decades and to ensure that the gains won over the past three decades are not lost. FUNDING: Bloomberg Philanthropies and the Bill & Melinda Gates Foundation.

• MeSH
• celosvětové zdraví statistika a číselné údaje   MeSH
• dospělí MeSH
• globální zátěž nemocemi * MeSH
• kouření * epidemiologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• naděje dožití * trendy   MeSH
• předpověď * MeSH
• prevalence MeSH
• senioři MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• Betacoronavirus MeSH
• COVID-19 MeSH
• epidemický výskyt choroby MeSH
• epidemiologické monitorování MeSH
• lůžková kapacita nemocnice MeSH
• osobní ochranné prostředky MeSH
• reagenční diagnostické soupravy MeSH
• vybavení a zásoby nemocnice MeSH
• zdravotničtí pracovníci MeSH

• Konspekt
• Veřejné zdraví a hygiena
• NLK Obory
• veřejné zdravotnictví
• infekční lékařství
• NLK Publikační typ
• publikace WHO

INTRODUCTION: Time series analysis is used by statisticians to make predictions from time-ordered data. This is crucial for planning for the future. The inclusion of little-known forecasting function in ExcelTM has brought this type of analysis within the ability of less mathematically sophisticated individuals, including doctors. There are two main models for time series analysis: ARIMA (Autoregressive Integrated Moving Average) and exponential smoothing. This paper will demonstrate how the ubiquitous Excel facilitates a little-known sophisticated forecasting technique that employs the latter and presents a facilitating spreadsheet. METHODS: Excel's FORECAST.ETS function was invoked with supporting macros. RESULTS: A bespoke spreadsheet was created that would prompt for data to be pasted in columns A and B, formatted as a valid date in A and data in B. After error trapping and a horizon date, the FORECAST.ETS function calculates forecasts with 95% CI and a line graph. The FORECAST.ETS.CONFINT was also invoked using a macro to obtain a 95, 96, 97, 98 and 99% confidence intervals table. DISCUSSION: Forecasting is vital in all fields, including the medical field, for innumerable reasons. Statisticians are capable of far more sophisticated time series analyses and techniques and may use multiple techniques that are beyond the competence of ordinary clinicians. However, the sophisticated Excel tool described in this paper allows simple forecasting by anyone with some knowledge of this ubiquitous software. It is hoped that the spreadsheet included with this paper helps to encourage colleagues to engage with this simple-to-use Excel function.

• MeSH
• lidé MeSH
• předpověď * MeSH
• software MeSH
• statistické modely MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• Betacoronavirus MeSH
• COVID-19 MeSH
• epidemický výskyt choroby MeSH
• epidemiologické monitorování MeSH
• koronavirové infekce epidemiologie   MeSH
• lůžková kapacita nemocnice MeSH
• osobní ochranné prostředky MeSH
• reagenční diagnostické soupravy MeSH
• vybavení a zásoby nemocnice MeSH
• zdravotničtí pracovníci MeSH

• Konspekt
• Veřejné zdraví a hygiena
• NLK Obory
• veřejné zdravotnictví
• epidemiologie
• NLK Publikační typ
• publikace WHO

• Konspekt
• Statistika
• NLK Obory
• statistika, zdravotnická statistika

The main objective of this study was to determine and describe the lead transfer in the soil-plant-animal system in areas polluted with this metal at varying degrees, with the use of mathematical forecasting methods and data mining tools contained in the Statistica 9.0 software programme. The starting point for the forecasting models comprised results derived from an analysis of different features of soil and plants, collected from 139 locations in an area covering 100km(2) around a lead-zinc ore mining and processing plant ('Boleslaw'), at Bukowno in southern Poland. In addition, the lead content was determined in the tissues and organs of 110 small rodents (mainly mice) caught in the same area. The prediction models, elaborated with the use of classification algorithms, forecasted with high probability the class (range) of pollution in animal tissues and organs with lead, based on various soil and plant properties of the study area. However, prediction models which use multilayer neural networks made it possible to calculate the content of lead (predicted versus measured) in animal tissues and organs with an excellent correlation coefficient.

• MeSH
• algoritmy MeSH
• anatomické struktury zvířat chemie   účinky léků   MeSH
• látky znečišťující půdu analýza   MeSH
• myši MeSH
• olovo analýza   toxicita   MeSH
• potravní řetězec MeSH
• předpověď MeSH
• rostliny chemie   MeSH
• teoretické modely * MeSH
• zinek analýza   MeSH
• životní prostředí * MeSH
• znečištění životního prostředí analýza   MeSH
• zvířata MeSH
• Check Tag
• myši MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Polsko MeSH