Search results for "Prediction."

showing 10 items of 490 documents

The Impact of Population Ageing and Social Stratification: The Case of Latvia

2019

Population ageing and social stratification is widely assumed to have detrimental effects on the economy yet there is little empirical evidence about the magnitude of its effects. The aim of this article is to investigate the relationships between population ageing and social stratification and the state of economy of a small and post-transition economy. We are looking for these relationships and their strength of influence; at what time after shocking these variables reach their original levels. We apply standard Granger (non-) causality tests, VAR (Vector Auto-Regressive), IRF (Impulse Response Function) and the prediction error variance analysis by using quarterly data from 2000 to 2018.…

Population ageingEconomic development -- Econometric models03 medical and health sciencesPrediction error variance0302 clinical medicinepost-transition economy0502 economics and businessPer capitaEconomicssmall and open economy030212 general & internal medicineEmpirical evidenceLabor supply -- LatviaPopulation ageingSocial stratification -- Latviapopulation social stratification05 social sciencesGeneral Business Management and AccountingCausalitySocial stratificationPopulation aging -- Latvia:SOCIAL SCIENCES::Business and economics::Economics [Research Subject Categories]Demographic economicsLatvia -- Economic policyGeneral Economics Econometrics and Finance050212 sport leisure & tourism
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A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Popu…

2021

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models wer…

PopulationMachine learningcomputer.software_genreLogistic regressionPediatricsProcalcitoninRJ1-570Medicinerisk factorseducationOriginal Researcheducation.field_of_studyKawasaki diseasebusiness.industryRetrospective cohort studyNomogrammedicine.diseaseSupport vector machineprediction modelmachine learningPediatrics Perinatology and Child HealthKawasaki diseaseArtificial intelligencebusinesscomputerintravenous immunoglobulin resistancePredictive modellingFrontiers in Pediatrics
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Predictores de duelo complicado

2008

Pilar.Barreto@uv.es; Patricia.Yi@uv.es OBJETIVO: Estudiar el proceso de evolución en sus primeras etapas tras la pérdida y determinar cuáles son los factores de riesgo y protección previos a la muerte que permiten predecir el surgimiento de posibles complicaciones en los familiares/cuidadores de pacientes oncológicos. MÉTODO: Se evaluaron 236 dolientes cuyos familiares eran pacientes oncológicos atendidos en servicios de cuidados paliativos de Valencia y Madrid y se realizó un seguimiento en 2 momentos temporales: 2 y 6 meses tras la muerte, evaluándose la presencia/ausencia de complicaciones en el proceso de duelo mediante dos criterios diagnósticos (DSM e ICG). RESULTADOS: Se encontró una…

Predicción de duelo complicadoProtectoreslcsh:BF1-990Protectors:PSICOLOGÍA::Asesoramiento y orientación ::Orientación profesional [UNESCO]lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogensDuelolcsh:RC254-282lcsh:PsychologyGrief developmentRisk factorsPrediction of complicated bereavementDuelo; Evolución de la pena; Factores de riesgo; Protectores; Predicción de duelo complicadoUNESCO::PSICOLOGÍA::Asesoramiento y orientación ::Orientación profesionalEvolución de la penaBereavementFactores de riesgoPsicooncologia
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Hyper-concentrated flow and surface velocity estimation by digital imaging technique: a study case

2015

This paper investigates the utility of digital imaging technique for performing surface velocity measurements of hyper-concentrated flows. The analysis is conducted with the aid of data collected in a scale laboratory flume constructed at the Hydraulic laboratory of the Department of Civil, Environmental, Aerospatial and of Materials Engineering (DICAM) – University of Palermo (Italy). In particular the present paper describes the setup of a laboratory test and the applicability of a fully digital imaging approach.

Predictions hyper-concentrated flows flow velocity experiments digital measurementsSettore ICAR/01 - Idraulica
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Concordance of Radiological, Laparoscopic and Laparotomic Scoring to Predict Complete Cytoreduction in Women with Advanced Ovarian Cancer

2023

Objective: To identify the best method among the radiologic, laparoscopic and laparotomic scoring assessment to predict the outcomes of cytoreductive surgery in patients with advanced ovarian cancer (AOC). Methods: Patients with AOC who underwent pre-operative computed tomography (CT) scan, laparoscopic evaluation, and cytoreductive surgery between August 2016 and February 2021 were retrospectively reviewed. Predictive Index (PI) score and Peritoneal Cancer Index (PCI) scores were used to estimate the tumor load and predict the residual disease in the primary debulking surgery (PDS) and interval debulking surgery (IDS) after neoadjuvant chemotherapy (NACT) groups. Concordance percentages we…

Predictive index scoreCancer ResearchOncologyOvarian cancerPrediction modelCytoreductive surgeryPrimary debulking surgery.Interval debulking surgeryNeoadjuvant chemotherapyPeritoneal cancer index scoreSettore MED/40 - Ginecologia E Ostetriciaovarian cancer; cytoreductive surgery; prediction model; predictive index score; peritoneal cancer index score; primary debulking surgery; interval debulking surgery; neoadjuvant chemotherapyCancers
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MAVIE-Lab Sports: a mHealth for Injury Prevention and Risk Management in Sport

2018

International audience; Smart-phones technology and the development of mHealth (Mobile Health) applications offer an opportunity to design intervention tools to influence health behavior changes. The MAVIE-Lab is a mHealth application including a DSS (Desicion Support System) to assist in the personalized evaluation of HLIs (Home, Leisure and Sport Injuries) risk and to promote the adoption of prevention measures. MAVIE-Lab Sports will be the first module of the mobile application. The purpose of this PhD project is to improve a particular module of MAVIE-Lab, devoted to sports (MAVIE-Lab Sports), in different aspects: statistical modeling, design and ergonomics. It also aims to evaluate sy…

Process managementComputer scienceInjury030501 epidemiologyMathematics of computing[STAT.CO] Statistics [stat]/Computation [stat.CO][ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG]Bayesian networks BN03 medical and health sciences[STAT.ML]Statistics [stat]/Machine Learning [stat.ML][INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][STAT.AP] Statistics [stat]/Applications [stat.AP]Personal digital assistantsInjury preventioneHealthInjury Epidemiology[STAT.CO]Statistics [stat]/Computation [stat.CO]mHealthRisk managementComputingMilieux_MISCELLANEOUS[ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML][ STAT.CO ] Statistics [stat]/Computation [stat.CO][STAT.AP]Statistics [stat]/Applications [stat.AP]030505 public healthHome and leisure injuries[STAT.ME] Statistics [stat]/Methodology [stat.ME]business.industryHLIs[ STAT.AP ] Statistics [stat]/Applications [stat.AP]Human factors and ergonomicsUsability[ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]Human-centered computing[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]Intervention (law)Bayesian networks[ STAT.ME ] Statistics [stat]/Methodology [stat.ME][SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologieHuman-centered computing[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologieeHealth0305 other medical sciencebusinessAppPrediction[STAT.ME]Statistics [stat]/Methodology [stat.ME]
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Extracting similar sub-graphs across PPI Networks

2009

Singling out conserved modules (corresponding to connected sub-graphs) throughout protein-protein interaction networks of different organisms is a main issue in bioinformatics because of its potential applications in biology. This paper presents a method to discover highly matching sub-graphs in such networks. Sub-graph extraction is carried out by taking into account, on the one side, both protein sequence and network structure similarities and, on the other side, both quantitative and reliability information possibly available about interactions. The method is conceived as a generalization of a known technique, able to discover functional orthologs in interaction networks. Some preliminar…

Protein structure databaseBioinformatics network analysisProtein sequencingMatching (graph theory)GeneralizationComputer scienceReliability (computer networking)Protein function predictionGraph theoryData miningcomputer.software_genrecomputerNetwork analysis
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A computer system to perform structure comparison using TOPS representations of protein structure

2001

We describe the design and implementation of a fast topology-based method for protein structure comparison. The approach uses the TOPS topological representation of protein structure, aligning two structures using a common discovered pattern and generating measure of distance derived from an insert score. Heavy use is made of a constraint-based pattern-matching algorithm for TOPS diagrams that we have designed and described elsewhere (Bioinformatics 15(4) (1999) 317). The comparison system is maintained at the European Bioinformatics Institute and is available over the Web at tops.ebi.ac.uk/tops. Users submit a structure description in Protein Data Bank (PDB) format and can compare it with …

Protein structure databaseMeasure (data warehouse)Molecular StructureComputer scienceGeneral Chemical EngineeringProteinsSequence Homologycomputer.file_formatTOPSProtein structure predictioncomputer.software_genreProtein Data BankApplied Microbiology and BiotechnologyPattern Recognition AutomatedArtificial IntelligencePattern matchingData miningProtein topologyRepresentation (mathematics)computerAlgorithmsSoftwareBiotechnology
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Experimental Evaluation of Protein Secondary Structure Predictors

2009

Understanding protein biological function is a key issue in modern biology, which is largely determined by its 3D shape. Protein 3D shape, in its turn, is functionally implied by its amino acid sequence. Since the direct inspection of such 3D structures is rather expensive and time consuming, a number of software techniques have been developed in the last few years that predict a spatial model, either of the secondary or of the tertiary form, for a given target protein starting from its amino acid sequence. This paper offers a comparison of several available automatic secondary structure prediction tools. The comparison is of the experimental kind, where two relevant sets of proteins, a non…

Protein structure databasebusiness.industryProtein structure predictionBioinformaticsMachine learningcomputer.software_genreSet (abstract data type)Bioinformatics Protein PredictionTest caseGlobal distance testArtificial intelligenceCASPbusinessPeptide sequencecomputerProtein secondary structure
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Using Deep Learning to Extrapolate Protein Expression Measurements

2020

Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, in…

ProteomicsIn silicoQuantitative proteomicsComputational biologyBiologyBiochemistryprotein abundance predictionMass SpectrometryProtein expressionMice03 medical and health sciencesDeep LearningAbundance (ecology)AnimalsMolecular BiologyGeneResearch Articles030304 developmental biologydeep learning networks0303 health sciencesUniProt keywordsbusiness.industryDeep learning030302 biochemistry & molecular biologyProteinsRNAMolecular Sequence AnnotationMissing dataGene OntologyArtificial intelligencebusinessResearch ArticlePROTEOMICS
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