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PubMed2000

Factors predicting return to work following mild traumatic brain injury: a discriminant analysis.

Studies of mild traumatic brain injury (MTBI) suggest that most individuals recover rapidly and return to their everyday activities. However, a percentage of MTBI patients report persistent problems with cognitive, physical, and emotional symptoms. There is also evidence that some experience changes in occupational functioning following MTBI. The current study used a stepwise discriminant function analysis (DFA) to examine the role of injury severity variables, cognitive performance, and ratings of symptoms of TBI in predicting work status following MTBI. Subjects included 121 MTBI patients who were all active-duty military personnel. The stepwise DFA revealed that age and three cognitive variables (verbal memory, verbal fluency, and a speed test of planning and strategy) were predictive of work status 3-15 months following a documented MTBI, correctly classifying work status 68.8% of the time. A cross-validation DFA was conducted, with a 66.1% correct classification rate. These findings highlight the importance of cognitive impairments in identifying those at risk for occupational impairment following MTBI.

Activities of Daily Living↗
PubMed1997

Discriminant analysis of the Ullrich-Turner syndrome neurocognitive profile.

Ullrich-Turner syndrome (UTS), or monosomy X, is a genetic disorder characterized by short stature, gonadal dysgenesis, and a particular neurocognitive profile of normally developed language abilities (particularly verbal IQ) and impaired visual-spatial and/or visual-perceptual abilities. The most frequently described profile in UTS includes difficulty with tasks involving memory and attention, decreased arithmetic skills, and impaired visual spatial processing. We used discriminant function analysis (DFA) to distinguish between the neurocognitive profiles of girls with UTS vs. controls matched for age, height, IQ, and socioeconomic status. DFA is a statistical method for deriving a linear function that optimally weights parameters to permit sensitive and specific differentiation among groups. We developed a modified discriminant function, based on seven cognitive test scores, that successfully discriminated between the UTS and control subjects with a sensitivity of 0.45 and a specificity of 0.97. To validate its performance, we applied the discriminant function to a small group of 45,X UTS subjects (n = 13) and control female subjects (n = 25), ages 7-16 years, who were not part of the previous analyses. The discriminant function (DF) identified 54% of these 13 UTS subjects as having the "UTS neurocognitive profile" and 92% of the 25 control subjects as not having the profile. We also compared the DF scores of UTS girls with various mosaic karyotypes and found that the group with 46,XX mosaicism had significantly higher scores (i.e., closer to normal controls) than the other two mosaic groups (t = 2.86, P < 0.005). The results of this study should be useful for genetic counseling and planning educational programs for girls with UTS.

Adolescent↗
PubMed1990

Logistic discriminant analysis of lipids and apolipoproteins in a population of coronary bypass patients and the significance of apolipoproteins C-III and E.

The lipid and apolipoprotein states of 74 men (mean age 49.96 +/- 5.9 years) were studied 24 h before coronary bypass surgery and their results were compared with those of a control group of 78 men (mean age 48.88 +/- 5.41 years). Apolipoproteins C-III (apo C-III) and E (apo E) were determined in particles with (LpB) and without (nonLpB) apo B separated using a concanavalin A reagent. Apo C-III was significantly increased in LpB particles (P less than 0.001), and apo E in LpB (P less than 0.001) and nonLpB (P less than 0.001) particles. The significant variables selected in logistic discriminant stepwise analysis were total cholesterol/HDL-cholesterol, apo E-nonLpB and apo C-III-LpB. This last parameter, which is more discriminant than triglycerides, provides a more specific indication of dyslipoproteinemia in coronary bypass patients; in association with the other two variables, it significantly improved the percentage of correctly classified individuals.

Adult↗
PubMed1986

Bayesian analysis versus discriminant function analysis: their relative utility in the diagnosis of coronary disease.

Both Bayesian analysis assuming independence and discriminant function analysis have been used to estimate probabilities of coronary disease. To compare their relative accuracy, we submitted 303 subjects referred for coronary angiography to stress electrocardiography, thallium scintigraphy, and cine fluoroscopy. Severe angiographic disease was defined as at least one greater than 50% occlusion of a major vessel. Four calculations were done: (1) Bayesian analysis using literature estimates of pretest probabilities, sensitivities, and specificities was applied to the clinical and test data of a randomly selected subgroup (group I, 151 patients) to calculate posttest probabilities. (2) Bayesian analysis using literature estimates of pretest probabilities (but with sensitivities and specificities derived from the remaining 152 subjects [group II]) was applied to group I data to estimate posttest probabilities. (3) A discriminant function with logistic regression coefficients derived from the clinical and test variables of group II was used to calculate posttest probabilities of group I. (4) A discriminant function derived with the use of test results from group II and pretest probabilities from the literature was used to calculate posttest probabilities of group I. Receiver operating characteristic curve analysis showed that all four calculations could equivalently rank the disease probabilities for our patients. A goodness-of-fit analysis suggested the following relationship between the accuracies of the four calculations: (1) less than (2) approximately equal to (4) less than (3). Our results suggest that data-based discriminant functions are more accurate than literature-based Bayesian analysis assuming independence in predicting severe coronary disease based on clinical and noninvasive test results.

Bayes Theorem↗
PubMed1989

Behavioral interaction between cocaine and caffeine: a drug discrimination analysis in rats.

The effects of caffeine upon the discriminative and rate-altering effects of cocaine were examined in rats. Using a food-reinforced two-lever operant procedure, 12 Sprague-Dawley male rats were trained to discriminate between 10 mg/kg cocaine and saline. Stimulus generalization tests with both cocaine and amphetamine resulted in a dose-related increase in cocaine-appropriate responding. A variable response rate topography was produced by cocaine. Caffeine also engendered a dose-related increase in cocaine-appropriate responding and resulted in a potency ratio of 15:1 when compared to cocaine. In contrast, increasing doses of caffeine produced a biphasic response rate function (first increases and then decreases). Response choice data suggested a potency relationship of amphetamine greater than cocaine greater than caffeine. Caffeine potentiated the discriminative stimulus properties of cocaine. Isobolographic analysis characterized this interaction as simple additivity. However, caffeine's effects upon the rate-altering effects of cocaine resulted in a biphasic interaction pattern. With low doses of cocaine in combination with various doses of caffeine, the interaction for rate reduction is best categorized as "supra-additive," in contrast, increasing either the cocaine dose or caffeine dose could change the interaction to simple additivity and/or infra-additivity.

Animals↗
PubMedDate not supplied

Introducing the consensus modeling concept in genetic algorithms: application to interpretable discriminant analysis.

An evolutionary statistical learning method was applied to classify drugs according to their biological target and also to discriminate between a compilation of oral and nonoral drugs. The emphasis was placed not only on how well the models predict but also on their interpretability. In an enhancement to previous studies, the consistency of the model weights over several runs of the genetic algorithm was considered with the goal of producing comprehensible models. Via this approach, the descriptors and their ranges that contribute most to class discrimination were identified. Selecting a bin step size that enables the average descriptor properties of the class being trained to be captured improves the interpretability and discriminatory power of a model. The performance, consistency, and robustness of such models were further enhanced by using two novel approaches that reduce the variability between individual solutions: consensus and splice modeling. Finally, the ability of the genetic algorithm to discriminate between activity classes was compared with a similarity searching method, while naïve Bayes classifiers and support vector machines were applied in discriminating the oral and nonoral drugs.

Algorithms↗
PubMed1993

Statistical discriminant analysis of arrhythmias using intracardial electrograms.

The problem of classifying ventricular arrhythmias from intracardial electrograms is considered. Standard statistical discrimination procedures are applied using a simple parametric model for the shape of the pulse near its peak. This approach makes simultaneous use of the model parameters, has well known statistical properties, and involves computations that can be carried out efficiently. Preliminary analyses of real data sets, using both linear and quadratic discrimination functions, yield promising results.

Algorithms↗
PubMed2005

Prediction of vocational outcome of people with brain injury after rehabilitation: a discriminant analysis.

Due to advancements in emergency evacuation procedures and neurosurgical techniques, more patients with brain injury (BI) can survive cerebral injury and regain a satisfactory degree of recovery through rehabilitation. Their improvement in physical and cognitive functions can facilitate their reintegration into the community, especially their return to work (RTW). Different factors were reported to affect this rate of RTW in previous studies. This retrospective study thus aimed to investigate the role of demographic data, the severity of injury, residual physical deficits, activities of daily living, and cognitive function using a stepwise discriminant function analysis (DFA). Data sets for 79 persons with BI were retrieved so as to identify predictors of their vocational outcomes upon discharge and during a follow-up period. The stepwise DFA revealed that the discharge score on the Disability Rating Scale (DRS), the attention-subtest of the Neurobehavioral Cognitive Status Examination (NCSE) or Cognistat, and pre-morbid occupation were significant predictors that correctly classified 65.8% of the subjects.

Adolescent↗
PubMed2005

The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with a typical Mediterranean climate.

Biological particles in the air such as pollen grains can cause environmental problems in the allergic population. Medical studies report that a prior knowledge of pollen season severity can be useful in the management of pollen-related diseases. The aim of this work was to forecast the severity of the Poaceae pollen season by using weather parameters prior to the pollen season. To carry out the study a historical database of 21 years of pollen and meteorological data was used. First, the years were grouped into classes by using cluster analysis. As a result of the grouping, the 21 years were divided into 3 classes according to their potential allergenic load. Pre-season meteorological variables were used, as well as a series of characteristics related to the pollen season. When considering pre-season meteorological variables, winter variables were separated from early spring variables due to the nature of the Mediterranean climate. Second, a neural network model as well as a discriminant linear analysis were built to forecast Poaceae pollen season severity, according to the three classes previously defined. The neural network yielded better results than linear models. In conclusion, neural network models could have a high applicability in the area of prevention, as the allergenic potential of a year can be determined with a high degree of reliability, based on a series of meteorological values accumulated prior to the pollen season.

Climate↗
PubMed2006

Metabolic profiling of glucuronides in human urine by LC-MS/MS and partial least-squares discriminant analysis for classification and prediction of gender.

Mass spectrometry (MS) is increasingly being used for metabolic profiling, but detection modes such as constant neutral loss or multiple reaction monitoring have not often been reported. These modes allow focusing on structurally related compounds, which could be advantageous for situations in which the trait under investigation is associated with a particular class of metabolites. In this study, we analyzed endogenous glucuronides excreted in human urine by monitoring characteristic transitions of putative steroid glucuronides by LC-MS/MS for discrimination of females from males. Two methods for data extraction were used: (i) a manual procedure based on visual inspection of the chromatograms and selection of 23 peaks and (ii) a software-supported method (MarkerView) set to extract 100 peaks. Data from 10 female and 10 male students were analyzed by principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) using software SIMCA. With PCA, only the manual peak selection resulted in clustering males and females. With PLS-DA, the manual method provided full separation on the basis of one single discriminant; the software-supported approach required a two-component model for complete separation. Loading plots were analyzed for their ability to reveal peaks with high discriminating power, that is, potential biomarkers. The PLS-DA models were validated with urine samples collected from five new females and five new males. Gender was correctly assigned for all. Our results indicate that inclusion of biological criteria for variable selection coupled to class-specific MS analysis and data extraction by appropriate software may constitute a valuable addition to the methods available for metabolomics.

Adult↗
PubMedDate not supplied

Investigation of bacteriophage MS2 viral dynamics using model discrimination analysis and the implications for phage therapy.

Lytic phages infect their bacterial hosts, use the host machinery to replicate, and finally lyse and kill their hosts, releasing progeny phages. Various mathematical models have been developed that describe these phage-host viral dynamics. The aim of this study was to determine which of these models best describes the viral dynamics of lytic RNA phage MS2 and its host Escherichia coli C-3000. Experimental data consisted of uninfected and infected bacterial cell densities, free phage density, and substrate concentration. Parameters of various models were either determined directly through other experimental techniques or estimated using regression analysis of the experimental data. The models were evaluated using a Bayesian-based model discrimination technique. Through model discrimination it was shown that phage-resistant cells inhibited the growth of phage population. It was also shown that the uninfected bacterial population was a quasispecies consisting of phage-sensitive and phage-resistant bacterial cells. When there was a phage attack the phage-sensitive cells died out and the phage-resistant cells were selected for and became the dominant strain of the bacterial population.

Cell Proliferation↗
PubMed2006

Discriminative analysis of relapsing neuromyelitis optica and relapsing-remitting multiple sclerosis based on two-dimensional histogram from diffusion tensor imaging.

It is difficult to completely differentiate patients with relapsing neuromyelitis optica (RNMO) from relapsing-remitting multiple sclerosis (RRMS) for their similarities in clinical manifestation. In this study, we proposed a novel approach, using two-dimensional histogram of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) of the brain derived from diffusion tensor imaging (DTI) as classification feature, to discriminate patients with RNMO from RRMS. In this approach, two-dimensional principal component analysis (2D-PCA) was used to extract feature and reduce dimensionality of matrix-formed data efficiently. Then linear discriminant analysis (LDA) was performed on these extracted features to find the best projection direction to separate patients with RNMO from RRMS. Finally, a minimum distance classifier was generated on the basis of projection scores. The correct recognition rate of our method reached 85.7%, validated by the leave-one-out method. This result was much higher than that using feature of ADC or FA separately (59.5% for ADC, 76.2% for FA). In conclusion, the proposed method on the basis of combined features is more effective for classification than those merely using the features separately, and it may be helpful in differentiating RNMO from RRMS patients.

Diffusion Magnetic Resonance Imaging↗
PubMed1999

Discriminant analysis of human kinematic data: application to human lumbar spinal motion.

A study was undertaken to determine the applicability of a multivariate discriminant technique in order to analyse human kinematic data, specifically lumbar motion during forward flexion. This method was used in an attempt to allow comparison of time-series data (three joint angles and three linear displacements) between groups of subjects. Results obtained from ten healthy subjects performing simulated abnormal styles of forward flexion indicate the feasibility and potential utility of this method in a clinical environment. Further investigations will be undertaken on clinical subjects to discriminate more effectively between healthy and pathological movements.

Adolescent↗