Predictive modeling
Dotaz
Zobrazit nápovědu
Springer series in statistics
1st ed. 568 s.
Počet státních pojištěnců je důležitá položka pro odhad příjmů veřejného zdravotního pojištění. Cílem předkládané studie je kompletní analýza, to jest posouzení vstupních dat pomocí grafi cké analýzy, popisné statistiky a výběr nejvhodnějšího modelu pro predikci. V rámci studie byly porovnány stochastické modely SARIMA a regARIMA, kubická funkce s konstantami a bez nich. Podle BIC hodnoty a Ljung-Boxova testu vývoj státních pojištěnců nejlépe popisuje proces SARIMA(0,1,3)(0,1,1)12 bez konstanty. Na základě modelu se bude počet státních pojištěnců pohybovat na úrovni 5 866 796 osob, což by pro státní rozpočet v roce 2019 představovalo výdaj v hodnotě 71,74 miliardy Kč.
The number of state insured persons is an important item for the revenue side of the Czech system of public health insurance. The main aim of this paper is a complete analysis, which contains an assessment of the input dataset by the graphic analysis, descriptive statistics, and a choice of the best fi tting prediction model. In this study, SARIMA and regARIMA stochastic models, and cubic regression function with the constant term and without it were compared. Based on the BIC value and the Ljung-Box test, the SARIMA(0,1,3)(0,1,1)12 stochastic process without the constant term was proved the best fi tting model. Using this model, the average number of state insured persons was estimated at 5 866 796 people, which corresponds to CZK 71.74 billion expenditure in the 2019 state budget.
- MeSH
- lidé MeSH
- teoretické modely MeSH
- veřejné zdravotnictví ekonomika MeSH
- zdravotní pojištění * MeSH
- Check Tag
- lidé MeSH
Úvod: Karcinom žaludku je časté onemocnění se špatnou prognózou. Většina nemocných podstupuje pouze paliativní léčbu. Jednou z možností je chemoterapie, její efekt se však u jednotlivých nemocných liší. Metoda: Jde o retrospektivní studii, do které bylo na podkladě vstupních kritérií zařazeno 54 nemocných (N=54). Provedli jsme kvantifikaci genové exprese vybraných genů a některých mikroRNA z nádorové tkáně, která byla použita pro stanovení diagnózy. Získaná data jsme statisticky zhodnotili. Výsledky: Prokázali jsme prediktivní význam stanovení genové exprese thymidylate synthasy (TS) v nádorové tkáni pro předpověď efektu chemoterapie založené na bázi 5-fluorouracilu nebo Capecitabinu. Současně jsme také prokázali prediktivní význam stanovení exprese miR181, miR150, miR192 a miR342 z nádorové tkáně. Navíc se nám podařilo prokázat prediktivní význam stanovení exprese miR221, miR224, miR520 a miR375 pro předpověď efektu platinových derivátů. Závěr: Díky využití efektivních prediktorů léčby můžeme odlišit nemocné, kteří budou profitovat z podání chemoterapie od nemocných, u kterých efekt očekávat nemůžeme. Díky personifikované onkologické léčbě můžeme u některých nemocných zlepšit kvalitu života a současně snížit náklady na léčbu tím, že jim nebude podána neefektivní chemoterapie. Pouze časný záchyt karcinomu žaludku může zvrátit nepříznivý osud nemocných s tímto onemocněním.
Introduction: Gastric cancer is a frequent malignant disease with poor prognosis. Most patients undergo only palliative treatment. Chemotherapy is another alternative but its effect differs in individual patients. Method: This is retrospective study. We enrolled 54 patients (N=54) according to the inclusion criteria. We performed quantification of gene expression of selected genes and some microRNA from tumour tissue, which was used for the diagnosis. Statistical analysis of the data was performed. Results: We demonstrated a predictive value of gene expression of thynidylate synthase in tumour tissue for a therapeutic effect of chemotherapy based on 5-Fluorouracil or Capecitabine. At the same time, we demonstrated a predictive value of miR181, miR150, mir192 and miR342 microRNA levels from the tumour tissue. In addition, we succeeded to demonstrate a predictive value of miR221, miR224, miR520 and miR375 microRNA levels for a therapeutic effect of chemotherapy based on platinum derivates. Conclusion: Thanks to the use of efficient therapy predictors, we can distinguish those patients who will profit from chemotherapy from patients where an effect cannot be expected. Thanks to personified oncology therapy the quality of life of some patients can be improved while reducing the costs of the therapy by avoiding inefficient chemotherapy. Only an early diagnosis of gastric cancer can reverse the adverse prognosis of patients with this disease.
- Klíčová slova
- gen ERCC1,
- MeSH
- analýza přežití MeSH
- capecitabinum terapeutické užití MeSH
- DNA vazebné proteiny genetika MeSH
- endonukleasy genetika MeSH
- fluoruracil terapeutické užití MeSH
- individualizovaná medicína MeSH
- Kaplanův-Meierův odhad MeSH
- lidé MeSH
- mikro RNA * genetika MeSH
- nádorové biomarkery * genetika MeSH
- nádory žaludku * farmakoterapie genetika patologie MeSH
- neparametrická statistika MeSH
- organoplatinové sloučeniny terapeutické užití MeSH
- paliativní péče MeSH
- prognóza MeSH
- proporcionální rizikové modely MeSH
- protinádorové látky terapeutické užití MeSH
- retrospektivní studie MeSH
- stanovení celkové genové exprese MeSH
- thymidylátsynthasa genetika MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
Cíl: cílem práce bylo podle stanovených parametrů vstupní rány a výsledného uložení kovového nitroočního tělesa stanovit korelační závislost obou veličin. Materiál a metodika: do retrospektivní studie bylo zařazeno 50 pacientů (50 očí) s otevřeným poraněním oka a přítomným kovovým nitroočním tělesem. Klinicky zjištěná data vstupní rány a výsledného uložení cizího nitroočního tělesa (CNT) byla převedena do trojrozměrně definovaných parametrů pomocí počítačového modelu. Oba parametry byly statisticky zpracovány metodou korelační analýzy se stanovením korelačního koeficientu a koeficientu determinace. Výsledky: mírou korelace mezi dvěma proměnnými je tzv. koeficient korelace. Koeficient nabývá hodnot od -1 do +1. Čím je jeho hodnota bližší plus nebo minus jedné, tím více jsou veličiny korelované. Koeficient determinace nabývá hodnot od 0 do +1. Čím více se výsledky blíží hodnotě +1, tím lépe model popisuje závislost mezi dvěma veličinami. Výsledky zpracované korelační analýzou prokázaly nejvyšší hodnoty korelačního koeficientu, resp. koeficientu determinace 0,454, resp. 0,6411. Závěr: z výsledků provedené korelační analýzy vyplývá, že pomocí znalosti souřadnic vstupu nedokážeme predikovat konečné souřadnice tělesa v oku. Tyto dvě proměnné jsou navzájem nekorelované a proto přesná predikce konečné polohy tělesa v oku není možná. Na výsledné umístění kovového CNT po vstupu do oka mají zřejmě vlivy biofyzikální faktory, které nebyly zahrnuty do studie.
Aim: The aim of this study was to establish the correlation coefficient between given parameters of the entering wound and final position of the metallic intraocular foreign body. Material and methods: fifty patients (50 eyes) with a penetrating injury of the eye and present metallic intraocular foreign body were included in this study. Clinically found data of the entering wound and final position of the intraocular foreign body (IFB) as well were transformed with a computer model into the three-dimensional parameters. Both chirurparameters were statistically evaluated by means of correlation analysis, and correlation coefficient and determination coefficient were calculated. Results: The extent of correlation between two variables is called correlation coefficient. The coefficient values range between -1 to +1. The closer is the calculated value to ranges (to -1 or to +1) the more are the two variables more correlated. The coefficient of determination values range from 0 to +1. The closer the results are to +1, the better the model describes the dependence between the two variables. The results obtained by means of correlation analysis were for the correlation coefficient 0.454, and for the coefficient of determination 0.6411 respectively. Results: Results of the correlation analysis show that the knowledge of coordinates of the entering wound has no prediction value for final position of the foreign body in the eye. These two variables are not correlated and so the accurate final position of the foreign body cannot be predicted. The final position of the intraocular metallic foreign body is probably influenced by biophysical factors not included in this study.
- MeSH
- cytochrom P-450 CYP2D6 * metabolismus MeSH
- genetické testování MeSH
- klinická studie jako téma MeSH
- lidé MeSH
- paroxetin * farmakologie terapeutické užití MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
INTRODUCTION: Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue. METHODS: 14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed. RESULTS: Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task. CONCLUSIONS: HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models.
- MeSH
- aorta thoracica * MeSH
- biologické modely * MeSH
- biomechanika MeSH
- mechanické jevy * MeSH
- pevnost v tahu MeSH
- prasata MeSH
- testování materiálů MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
BACKGROUND: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. METHODS: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. RESULTS: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. CONCLUSIONS: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. FUNDING: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).
- MeSH
- COVID-19 * diagnóza epidemiologie MeSH
- epidemie * MeSH
- infekční nemoci * MeSH
- lidé MeSH
- předpověď MeSH
- retrospektivní studie MeSH
- statistické modely MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
- Research Support, U.S. Gov't, P.H.S. MeSH
502 s.
