Search results for " receiver operating characteristic curve"

showing 4 items of 4 documents

Evaluation of serum s-IgE/total IgE ratio in predicting clinical response to allergen-specific immunotherapy.

2009

Background: To date, no predictive tests for the clinical response to allergen-specific immunotherapy (ASI) are available. Therefore an in vivo or in vitro test would be of great value. Objective: We sought to evaluate pretreatment parameters used in diagnosing allergic rhinitis and determining serum specific IgE (s-IgE) levels, serum total IgE (t-IgE) levels, and blood eosinophil counts and to identify whether can be used to predict clinical improvement in monosensitized patients with allergic rhinitis with or without asthma treated with immunotherapy. Methods:We analyzed 279 patients who had undergone 4 years of ASI administered either by means of the subcutaneous immunotherapy (76 patien…

AdultMaleAllergySettore MED/09 - Medicina InternaRhinitis Allergic PerennialAdolescentmedicine.medical_treatmentImmunologyspecific IgEImmunoglobulin Eblood eosinophil countsYoung AdultBlood serummedicineImmunology and AllergyHumansreceiver operating characteristic curveAsthmaDesensitization (medicine)Retrospective StudiesSkin Testsserum s-IgE/total IgE ratio; allergen-specific immunotherapyHouse dust miteserum s-IgE/total IgE ratiobiologyserum-specific IgE/serum total IgE ratiobusiness.industryAllergen-specific immunotherapy; blood eosinophil counts; receiver operating characteristic curve; serum-specific IgE/serum total IgE ratio; specific IgE; total IgEArea under the curveImmunotherapyAllergensImmunoglobulin EMiddle Agedmedicine.diseasebiology.organism_classificationPrognosisAllergen-specific immunotherapyBlood Cell Counttotal IgEEosinophilsTreatment OutcomeDesensitization ImmunologicSpirometryImmunologybiology.proteinFemalebusiness
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Analysis of a database to predict the result of allergy testing in vivo in patients with chronic nasal symptoms and the development of the software A…

2014

Background. This thesis consist of parts(i)Introduction in wich we present the clinical problem of rhinitis;(ii)the methods to evaluate the diagnostic choises;(iii)the rational errors in Allergy,(iv)the experimental part of thesis with wich we developed the software ARTSTAT,wich is the application of the analysis reported.Objective: We studied the ability of the logistic regression model obtained by the evaluaqtion of a database, to detect patients with positive allergy skin prick test(SPT)and patients with negative SPT. The model developed was valitated using the data set obtained from another medical institution. Methods: The analysis was carried out using a database obtained from a quest…

Allergic rhinitis Nonallergic rhinitis Decision Matrix Logistic regression model Receiver Operating Characteristic curve probability Diagnostic decision making nasal symptom Skin prick test (SPT) Cognitive Errors
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Dysfunction of attention switching networks in amyotrophic lateral sclerosis

2019

Objective To localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms. Rationale The MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary attention switching. This therefore indicates pathological impairment of the neural network components which generate these functions. Source localisation can mitigate the poor spatial resolution of sensor-level EEG analysis by associating the sensor-level signals to the contributing brain sources. The functional activity in each generating source can therefore be individually measured and investigat…

MaleMismatch negativitySource localisationEEG ElectroencephalographyMismatch negativityNetworkElectroencephalographylcsh:RC346-429PET Positron emission tomographyCognition0302 clinical medicineC9orf72AttentionEEGAUROC Area under receiver operating characteristic curveAmyotrophic lateral sclerosisAged 80 and overmedicine.diagnostic_test05 social sciencesCognitive flexibilityBrainRegular ArticleElectroencephalographyCognitionMiddle AgedSTG Superior temporal gyrusNeurologyMTG Mid temporal gyrusDLPFC Dorsolateral prefrontal cortexlcsh:R858-859.7FemaleLCMV Linearly constrained minimum varianceIFG Inferior frontal gyrusAdultCognitive Neurosciencelcsh:Computer applications to medicine. Medical informatics050105 experimental psychologyCWIT Colour-word interference test03 medical and health sciencesfMRI Functional magnetic resonance imagingMEG MagnetoencephalographymedicineMMN Mismatch negativityHumans0501 psychology and cognitive sciencesRadiology Nuclear Medicine and imagingLS Amyotrophic Lateral SclerosisAAL Automated Anatomical Labellinglcsh:Neurology. Diseases of the nervous systemAEP Auditory evoked potentialAgedbusiness.industryAmyotrophic Lateral SclerosisIQR Interquartile rangeNeurophysiologyqEEG Quantitative EEGmedicine.diseaseNeurology (clinical)Nerve NetFunctional magnetic resonance imagingbusinessNeuroscience030217 neurology & neurosurgeryeLORETA Exact low-resolution brain electromagnetic tomographyNeuroImage: Clinical
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Validation procedures in radiological diagnostic models. Neural network and logistic regression

1999

The objective of this paper is to compare the performance of two predictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validatio…

Validation methodsReceiver operating characteristicArtificial neural networkComputer scienceRadiological weaponResamplingSkull neoplasms logistic regression neural networks receiver operating characteristic curve statistics resamplingStatisticsWord error ratejel:C13Logistic regressionCross-validationjel:C14
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