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PubMedDate not supplied

Modeling choice behavior for new pharmaceutical products.

This paper presents a dynamic generalization of a model often used to aid marketing decisions relating to conventional products. The model uses stated-preference data in a random-utility framework to predict adoption rates for new pharmaceutical products. In addition, this paper employs a Markov model of patient learning in drug selection. While the simple learning rule presented here is only a rough approximation to reality, this model nevertheless systematically incorporates important features including learning and the influence of shifting preferences on market share. Despite its simplifications, the integrated framework of random-utility and product attribute updating presented here is capable of accommodating a variety of pharmaceutical marketing and development problems. This research demonstrates both the strengths of stated-preference market research and some of its shortcomings for pharmaceutical applications.

Analgesics↗

PubMed2006

Prospective memory in HIV-1 infection.

The cognitive deficits associated with HIV-1 infection are thought to primarily reflect neuropathophysiology within the fronto-striato-thalamo-cortical circuits. Prospective memory (ProM) is a cognitive function that is largely dependent on prefronto-striatal circuits, but has not previously been examined in an HIV-1 sample. A form of episodic memory, ProM involves the complex processes of forming, monitoring, and executing future intentions vis-à-vis ongoing distractions. The current study examined ProM in 42 participants with HIV-1 infection and 29 demographically similar seronegative healthy comparison (HC) subjects. The HIV-1 sample demonstrated deficits in time- and event-based ProM, as well as more frequent 24-hour delay ProM failures and task substitution errors relative to the HC group. In contrast, there were no significant differences in recognition performance, indicating that the HIV-1 group was able to accurately retain and recognize the ProM intention when retrieval demands were minimized. Secondary analyses revealed that ProM performance correlated with validated clinical measures of executive functions, episodic memory (free recall), and verbal working memory, but not with tests of semantic memory, retention, or recognition discrimination. Taken together, these findings indicate that HIV-1 infection is associated with ProM impairment that is primarily driven by a breakdown in the strategic (i.e., executive) aspects of retrieving future intentions, which is consistent with a prefronto-striatal circuit neuropathogenesis.

Adult↗

PubMed2026

Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.

MOTIVATION: Genomic sequence-to-activity models can decipher gene regulatory mechanisms and predict the functional impact of regulatory variants. However, current models struggle to integrate information from sequences outside promoters, especially information from cell type specific regulatory elements. RESULTS: Here, we propose incorporating base-pair resolution evolutionary conservation data into genomic sequence-to-expression predictors. We explore two training strategies-training from scratch or fine-tuning an existing sequence-only model with additional conservation input. We find that in both cases, base-pair resolution conservation data improves cell type specific sequence-to-expression prediction, with training from scratch yielding the greatest benefit. The improvement in cell type specific expression prediction can be attributed in part to the fact that models trained on sequence and conservation data learn to better recognize cell type specific regulatory elements than models trained on sequence alone. AVAILABILITY: Code is available at https://github.com/ni-lab/basenji-phyloP.

Conserved Sequence↗

PubMed2006

Deterministic approach to robust adaptive learning of fuzzy models.

This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inference system, in a deterministic framework, in the presence of data uncertainties and modeling errors. The authors explore the use of Hinfinity estimation theory and least squares estimation for online learning of membership functions and consequent parameters without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties and modeling errors. The issues of data uncertainties, modeling errors, and time variations have been considered mathematically in a sensible way. The proposed robust approach to the adaptive learning of fuzzy models has been illustrated through the examples of adaptive system identification, time-series prediction, and estimation of an uncertain process.

Algorithms↗

PubMed2026

Donor Microbiota Features Associated With Liver Transplant Recipient Infectious Complications: A Pilot Study Using Deep Intestinal Sampling During Liver Procurement.

BACKGROUND: The gut microbiota of living organ donors has been linked to transplant outcomes. However, little is known about the characteristics of the deceased donor gut microbiota or its potential impact on recipient outcomes. METHODS: We analyzed the deep intestinal microbiota from 24 deceased donors. Samples included luminal stool from the right and left colon as well as bile. Microbial composition was characterized using 16S V4 rRNA sequencing. α- and β-diversity analyses were performed to compare microbial communities between donor enteric sites and against stool samples from 28 healthy community controls, 14 critically ill intensive care comparators, and 12 matched liver transplant recipients. Machine learning models and logistic regression analysis were applied to explore whether features of the donor microbiota could predict recipient post-transplant complications. FINDINGS: The deceased donor microbiota showed an absence of the expected compositional variability between sampling sites, with no significant differences in either α- or β-diversity observed between bile, right and left colonic samples (all p > 0.05). Donor samples exhibited distinct microbial profiles compared with stool from both healthy and ICU comparators, including increased abundance of potential pathogens within the Enterobacteriaceae family (all p < 0.001). Features of the donor microbiota, particularly enrichment of Enterobacteriaceae, were associated with an increased risk of early post-transplant infection in recipients (≤ 30 days; p = 0.011). INTERPRETATION: The deceased donor gut microbiota may represent a distinct microbial community with potential clinical relevance. Microbial profiling of donor enteric microbiota may help identify recipients at heightened risk of early post-transplant infectious complications.

Enterobacteriaceae↗

PubMed2001

A mechanism for savings in the cerebellum.

The phenomenon of savings (the ability to relearn faster than the first time) is a familiar property of many learning systems. The utility of savings makes its underlying mechanisms of special interest. We used a combination of computer simulations and reversible lesions to investigate mechanisms of savings that operate in the cerebellum during eyelid conditioning, a well characterized form of motor learning. The results suggest that a site of plasticity outside the cerebellar cortex (possibly in the cerebellar nucleus) can be protected from the full consequences of extinction and that the residual plasticity that remains can later contribute to the savings seen during relearning.

Animals↗

PubMed1992

Dating violence, social learning theory, and gender: a multivariate analysis.

The study of violence between dating partners is a logical extension of interest in marital violence. However, little of this research tests explanations of intimate violence using multivariate techniques, and only recently have such tests occurred within a theoretical framework. Drawing on a recent social learning model of courtship violence (Riggs & O'Leary, 1989), this paper empirically examines constructs hypothesized to be predictive of the use of dating violence and investigates possible gender differences in the underlying causal structure of such violence. Logit analysis indicates that parent-child violence, drug use, and knowledge of use of dating violence by others predict the use of courtship violence by females. Belief that violence between intimates is justifiable, drug use, and parental divorce are related to perpetration of dating aggression by males. Explanations for these results and the importance of a multivariate approach to the problem are discussed.

Adolescent↗

PubMed2001

A neural network approach for predicting and modelling the dynamical behaviour of cardiac ventricular repolarisation.

Physiological signals are usually patient specific, and they are difficult to predict, especially for the cardiovascular system. New methods capable to be adapted to each case and to learn the singular behavior of heart functions should be developed to support physicians in their decision-making. One of the most widely studied relations is the QT-RR one, between the total duration of the ventricle activation and inactivation, and the heart rate. In the past, different studies were made to approach this relation in the steady state. In this paper, a new method for modeling and predicting the transient dynamic behaviour of QT interval in relation to changing RR intervals is presented using artificial neural networks.

Action Potentials↗

PubMed2026

Instrumented Walkway Gait Analysis Predicts Fallers in Neurological Disorders: Identifying Digital Biomarkers for Balance Monitoring.

Assessing balance is crucial in neurological rehabilitation, yet while wearable sensors enable real-world monitoring, identifying reliable digital biomarkers remains challenging. This study utilized a high-fidelity instrumented walkway to determine which gait parameters best predict balance impairment, providing robust targets for future wearable applications. We analyzed 49 steady-state gait metrics from 140 individuals with diverse neurological conditions. Using statistical analysis and machine learning, we evaluated these parameters against objective force plate sway scores and clinical fall-history labels. Group analysis identified 16 parameters significantly distinguishing fallers from non-fallers, and a neural network classified fallers with an area under the curve of 0.75. Across all analytical approaches, overall gait variability, e.g., Stride Width S.D. and the Gait Variability Index, emerged as a universal predictor of balance impairment and fall risk. Furthermore, while traditional linear models emphasized spatial postural control, machine learning classification uniquely identified inter-limb asymmetry as a premier driver of fall prediction. These findings indicate that instrumented gait analysis effectively identifies digital biomarkers for balance deficits. Isolating these specific metrics provides a clear blueprint for meaningful metrics required for continuous objective monitoring and future development of personalized, adaptive rehabilitation strategies.

Humans↗

PubMed2001

The Alcohol Helplessness Scale and its prediction of depression among problem drinkers.

Event-specific scales commonly have greater power than generalized scales in prediction of specific disorders and in testing mediator models for predicting such disorders. Therefore, in a preliminary study, a 6-item Alcohol Helplessness Scale was constructed and found to be reliable for a sample of 98 problem drinkers. Hierarchical multiple regression and its derivative path analysis were used to test whether helplessness and self-efficacy moderate or mediate the link between alcohol dependence and depression. A test of a moderation model was not supported, whereas a test of a mediation model was supported. Helplessness and self-efficacy both significantly and independently mediated between alcohol dependence and depression. Nevertheless, a significant direct effect of alcohol dependence on depression also remained.

Adult↗

PubMed1990

Cerebral ischemia model with conscious mice. Involvement of NMDA receptor activation and derangement of learning and memory ability.

During anesthesia in mice, both common carotid arteries were tied loosely with an overhand knot suture (an occluder), while two snares (releasers) were placed in the knot so that it could be repeatedly tightened to occlude the arteries and loosened again to allow for reperfusion while the mice were conscious and unrestrained. The incidence of mortality as well as impairment of brain metabolism depended upon the length of cerebral ischemia. Cortical electroencephalogram (EEG) clearly reflected the regional ischemia as evidenced by electrical quiescence. Less mortality was observed with ischemic mice treated with dextrorphan (30 mg/kg p.o.). On day 1 (24 hr after ischemia), there were impairments in complex motor coordination, multichoice swim performance, and step-through or thermal pain-motivated avoidance responses. Thereafter, the battery of tests progressively improved. This improvement depended on the period of resumption of cerebral blood flow; the 7-day, postischemic lapse significantly reduced the deficit observed. Reduction in the degree of habituation of exploratory activity was also clearly observed following ischemic insult. Dextrorphan (1-30 mg/kg i.p.) given to ischemic mice was effective in the habituation and step-through-type passive avoidance test paradigms. In conclusion, 1) the decline in cognition as observed with ischemic mice is due to the temporal and reversible derangement of their neuronal networks; 2) excessively released glutamate is probably of major pathogenic importance in the consequences of cerebral ischemia based on the positive results of the N-methyl-D-aspartate receptor antagonist, dextrorphan; 3) the simple technique could be useful in elucidating the pathophysiologic mechanisms of ischemically elicited derangement of the cerebral organization; and 4) the model could be used to assess the efficiency of drugs with high clinical predictivity.

Animals↗

PubMed2000

The effect of shared responsibility and competition in perceptual games: a test of a cognitive game-theoretic extension of signal-detection theory.

Perceptual decisions are often made in complex social settings in which distinct observers can affect each other. To address such situations, I. Erev, D. Gopher, R. Itkin, and Y. Greenshpan (1995) proposed a formal extension of signal-detection theory and a descriptive modification of the extended theory. The current article presents 2 experiments that were designed to test these models in the context of repeated 2-person perceptual safety games. In both experiments, pairs of participants performed a simulation of an industrial-production process under distinct payoff rules. Each participant had to try to produce as much as possible while avoiding costly accidents. In line with the descriptive model's predictions, the results showed a slow adjustment to the incentive structure that can be approximated by a reinforcement learning process among different perceptual cutoff strategies. Providing players with prior information about the game had an initial effect but did not alter the pattern of the results.

Adult↗

PubMed1998

[Selective use of the Eight-Word subtest in psychogeriatrics].

Performance on the Amsterdam Dementia Screening Test (ADS) and the Expanded Mental Control Test (EMCT) was examined in a consecutive sample of 204 attendants of a psychogeriatric day care department. The ADS has six subtests: picture recognition, orientation, drawing alternating sequences, category fluency, copying geometric figures, and free recall with immediate yes-no recognition of eight words. As was determined by logistic regression analysis, attentional control (EMCT), category fluency and picture recognition scores were significant predictors of verbal free recall. Recognition memory for pictures and a short orientation questionnaire (month, year, place) predicted word recognition performance. In 97% of the subjects with poor scores on EMCT and category fluency, an equally poor free recall performance was found. In those cases the incremental diagnostic value of the free recall test is doubtful. Since word recall and word recognition were conceptually related to working memory and episodic memory respectively, the two components of the verbal learning test allow detection of differential impairments of these memory systems.

Aged↗

PubMedDate not supplied

Predicting physical-chemical properties of compounds from molecular structures by recursive neural networks.

In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, Delta(solv)G degrees , has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the Delta(solv)G degrees in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R = 0.9985, standard deviation S = 0.68 kJ mol(-1), and mean absolute error MAE = 0.46 kJ mol(-1). The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.

Journal Article↗

PubMed1980

Classification in well-defined and ill-defined categories: evidence for common processing strategies.

Early work in perceptual and conceptual categorization assumed that categories had criterial features and that category membership could be determined by logical rules for the combination of features. More recent theories have assumed that categories have an ill-defined structure and have prosposed probabilistic or global similarity models for the verification of category membership. In the experiments reported here, several models of categorization were compared, using one set of categories having criterial features and another set having an ill-defined structure. Schematic faces were used as exemplars in both cases. Because many models depend on distance in a multidimensional space for their predictions, in Experiment 1 a multidimensional scaling study was performed using the faces of both sets as stimuli, In Experiment 2, subjects learned the category membership of faces for the categories having criterial features. After learning, reaction times for category verification and typicality judgments were obtained. Subjects also judged the similarity of pairs of faces. Since these categories had characteristic as well as defining features, it was possible to test the predictions of the feature comparison model (Smith et al.), which asserts that reaction times and typicalities are affected by characteristic features. Only weak support for this model was obtained. Instead, it appeared that subjects developed logical rules for the classification of faces. A characteristic feature affected reaction times only when it was part of the rule system devised by the subject. The procedure for Experiment 3 was like that for Experiment 2, but with ill-defined rather than well-defined categories. The obtained reaction times had high correlations with some of the models for ill-defined categories. However, subjects' performance could best be described as one of feature testing based on a logical rule system for classification. These experiments indicate that whether or not categories have criterial features, subjects attempt to develop a set of feature tests that allow for exemplar classification. Previous evidence supporting probabilistic or similarity models may be interpreted as resulting from subjects' use of the most efficient rules for classification and the averaging of responses for subjects using different sets of rules.

Concept Formation↗

PubMed2006

Exploring predictive and reproducible modeling with the single-subject FIAC dataset.

Predictive modeling of functional magnetic resonance imaging (fMRI) has the potential to expand the amount of information extracted and to enhance our understanding of brain systems by predicting brain states, rather than emphasizing the standard spatial mapping. Based on the block datasets of Functional Imaging Analysis Contest (FIAC) Subject 3, we demonstrate the potential and pitfalls of predictive modeling in fMRI analysis by investigating the performance of five models (linear discriminant analysis, logistic regression, linear support vector machine, Gaussian naive Bayes, and a variant) as a function of preprocessing steps and feature selection methods. We found that: (1) independent of the model, temporal detrending and feature selection assisted in building a more accurate predictive model; (2) the linear support vector machine and logistic regression often performed better than either of the Gaussian naive Bayes models in terms of the optimal prediction accuracy; and (3) the optimal prediction accuracy obtained in a feature space using principal components was typically lower than that obtained in a voxel space, given the same model and same preprocessing. We show that due to the existence of artifacts from different sources, high prediction accuracy alone does not guarantee that a classifier is learning a pattern of brain activity that might be usefully visualized, although cross-validation methods do provide fairly unbiased estimates of true prediction accuracy. The trade-off between the prediction accuracy and the reproducibility of the spatial pattern should be carefully considered in predictive modeling of fMRI. We suggest that unless the experimental goal is brain-state classification of new scans on well-defined spatial features, prediction alone should not be used as an optimization procedure in fMRI data analysis.

Artifacts↗

PubMed2001

Predictive factors of social disability in patients with eating disorders.

OBJECTIVE: The study was designed to assess whether social avoidance symptoms and/or comorbid anxiety disorders were predictive factors of social disability in subjects with eating disorders. METHOD: Sixty-three subjects with anorexia nervosa or bulimia nervosa were assessed for lifetime diagnoses of anxiety disorders, childhood history of separation anxiety disorder, social avoidance symptoms and social disability. RESULTS: On the Groningen Social Disabilities Schedule, 86% of the anorexics and 65% of the bulimics had disability regarding the 'social role', and 86% and 61 % disability regarding the 'occupational role'. Using all subsets logistic regression analyses, predictive factors of disability were: (1) for the social role, social avoidance symptom score (p<0.002) and diagnosis of separation anxiety disorder (p<0.01); (2) for the occupational role, number of lifetime anxiety disorders (p<0.01) and diagnosis of separation anxiety disorder (p<0.06). DISCUSSION: Recognizing and treating comorbid anxiety disorders in subjects with eating disorders could improve social adaptation and global psychopathological outcome.

Activities of Daily Living↗

PubMed2003

Bounding the effect of noise in multiobjective learning classifier systems.

This paper analyzes the impact of using noisy data sets in Pittsburgh-style learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with noisy domains. For this reason, we developed a theoretical model for predicting the minimal achievable error in noisy domains. Combining this theoretical model for crisp learners with graphical representations of the evolved hypotheses through multiobjective techniques, we are able to bound the behavior of a learning classifier system. This kind of modeling lets us identify relevant characteristics of the evolved hypotheses, such as overfitting conditions that lead to hypotheses that poorly generalize the concept to be learned.

Algorithms↗