SEARCH · PubMed Health

Results for “Predictive Learning Models”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,783 records · Page 99Linked to original sources

PubMed2002

Neuroimpairments, activity limitations, and participation restrictions in children with cerebral palsy.

In a representative series of 176 children with cerebral palsy (CP), aged 5 to 8 years, associations were studied between additional neuroimpairments, activity limitations, and participation restrictions in the domains of mobility, education, and social relations as proposed in the International Classification of Functioning Disability and Health (ICF). Learning disability occurred in 40%, epilepsy in 35%, visual impairment in 20%, and infantile hydrocephalus in 9% of the children. Additional neuroimpairments were most frequently seen in children with tetraplegia and dystonic CP and in those with antecedents of brain malformations or severe perinatal compromise. Activity limitations were studied with the Gross Motor Function Classification System (GMFCS) and a system for grading bimanual fine motor function (BFMF) was developed. There was a strong correlation of 0.74 between the GMFCS and BFMF (p<0.001). Learning disability, activity limitations, and participation restrictions were all clinically strongly associated with each other (p<0.001). Restriction in mobility was best predicted by the GMFCS, learning disability, and the BFMF; in education by learning disability and the GFMCS; and in social relations by learning disability, the GMFCS, and BFMF. Motor function and learning disability were important predictors for participation restrictions in children with CP. The ICF has the capacity to be a model to help plan interventions for specific functional goals and to ascertain the child's participation in society.

Activities of Daily Living↗

PubMed2004

Optimality principles in sensorimotor control.

The sensorimotor system is a product of evolution, development, learning and adaptation-which work on different time scales to improve behavioral performance. Consequently, many theories of motor function are based on 'optimal performance': they quantify task goals as cost functions, and apply the sophisticated tools of optimal control theory to obtain detailed behavioral predictions. The resulting models, although not without limitations, have explained more empirical phenomena than any other class. Traditional emphasis has been on optimizing desired movement trajectories while ignoring sensory feedback. Recent work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online. This approach has allowed researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function. At the heart of the framework is the relationship between high-level goals, and the real-time sensorimotor control strategies most suitable for accomplishing those goals.

Adaptation, Physiological↗

PubMed2004

Data mining in bioinformatics using Weka.

UNLABELLED: The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. AVAILABILITY: http://www.cs.waikato.ac.nz/ml/weka.

Algorithms↗

PubMed2000

Neural network models of categorical perception.

Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.

Humans↗

PubMedDate not supplied

[Interhemispheric differences in the spatial-frequency description of images and the consequences for visual recognition].

Visual perception of images transformed by spatial-frequency filtering was investigated in tachistoscopic experiments. Evidences have been received that the left hemisphere describes preferentially low harmonics of an image, and right hemisphere--the high ones. A hypothesis is suggested that these differences are based on different sizes of the receptive fields of cortical modules. Some consequences of the proposed model are discussed--the consequences for the invariance of visual recognition (innate invariance mechanisms in the left hemisphere and learned invariance in the right hemisphere) and for the methods of visual image classification (discriminant or structural methods in the left and right hemisphere respectively). Some data confirming these predictions are presented.

Discrimination, Psychological↗

PubMedDate not supplied

Reading research for students with LD: a meta-analysis of intervention outcomes.

The present article provides a meta-analysis of instructional research with samples of children and adolescents with learning disabilities in the domains of word recognition and reading comprehension. The results of the synthesis showed that a prototypical intervention study has an effect size (ES) of .59 for word recognition and .72 for reading comprehension. Four important findings emerged from the synthesis: (a) Effect sizes for measures of comprehension were higher when studies included derivatives of both cognitive and direct instruction, whereas effect sizes were higher for word recognition when studies included direct instruction; (b) effect sizes related to reading comprehension were more susceptible to methodological variation than studies of word recognition; (c) the magnitude of ES for word recognition studies was significantly related to samples defined by cutoff scores (IQ > 85 and reading < 25th percentile), whereas the magnitude of ES for reading comprehension studies was sensitive to discrepancies between IQ and reading when compared to competing definitional criteria; and (d) instructional components related to word segmentation did not enter significantly into a weighted least square hierarchical regression analysis for predicting ES estimates of word recognition beyond an instructional core model, whereas small-group interactive instruction and strategy cuing contributed significant variance beyond a core model to ES estimates of reading comprehension. Implications related to definition and instructional components that optimize the magnitude of outcomes are discussed.

Child↗

PubMed2003

Neurological basis of language and sequential cognition: evidence from simulation, aphasia, and ERP studies.

The current research addresses the hypothesis that certain aspects of sequential cognition have made substantial contributions to the human language processing capability, from a functional neurophysiology perspective. We first describe a cognitive sequence processing model that was developed based on the functional neuroanatomy of primate cortex and basal ganglia. We demonstrate how this model is capable of simulating the behavior of human infants in extracting serial, temporal and abstract structure from language-like sound sequences as revealed in recent psycholinguistic experiments. We then demonstrate how, through training, this model can perform adult level syntactic comprehension, based on dissociated processing streams for open vs. closed class words. The model subsequently predicts: (1) that impaired syntactic processing (as in agrammatic aphasia) will be associated with impairments in corresponding non-linguistic cognitive sequencing tasks, and (2) that neurophysiological processes (as revealed by ERPs) involved in syntactic processing should also be involved in the corresponding non-linguistic cognitive sequencing tasks. Data confirming these predictions are reviewed. We conclude that the study of sequential cognition will provide a new paradigm for the investigation of the neurophysiological bases of language.

Aphasia, Broca↗

PubMed1986

A regression model to identify successful learner traits with CAI.

This descriptive study examined the personality traits and instructional time factors of successful and nonsuccessful students who utilized computer-assisted instruction (CAI) to learn psychopharmacological nursing. Data from this study support the contention that there is beginning evidence to imply that there are certain individual traits that can be utilized to predict successful learning with CAI. The stepwise multiple regression analysis utilized in this study identified 17 variables that contributed to successful learning. Specific academic, cognitive, demographic, and philosophical indicators emerged as the most consistent predictors of learning associated with these psychopharmacological lessons presented on the PLATO system. These results and others are explored in terms of their educational implications, and suggestions are made for future research that can be used to plan more effective and efficient use of computers in nursing education.

Computer-Assisted Instruction↗

PubMed1996

Models, mechanisms and management of anticipatory nausea and emesis.

Anticipatory nausea and vomiting (ANV) are learned responses to chemotherapy that develop in up to 25% of patients by the fourth treatment cycle. Post-treatment nausea and emesis must occur before development of ANV can take place. Certain patient characteristics and other responses to chemotherapy can also be used to predict their occurrence. Once they develop, ANV cannot be controlled by pharmacologic means including use of new 5-HT3 receptor antagonists. By contrast, behavioral therapies involving relaxation, most notably systematic desensitization, can be used to effectively treat ANV. Clinic personnel including oncologists and oncology nurses as well as behavioral psychologists can effectively administer systematic desensitization to chemotherapy patients.

Animals↗

PubMed1975

A proposed basis for delusion formation within an information-processing model of paranoid development.

A developmental model for paranoid behaviour is proposed within a broader theory of schizophrenic development. The model stipulates that given exposure to sustained aversive maternal control and a maternal communication style which is subtle and devious, the child comes to adapt with approach, stratagem-based behaviours and heightened vigilance for evaluative information (i.e. open adaptive style). The model, already supported empirically at several points, postulates that the delusion serves to organize an overextended and disorganized information-processing system for the person and generates its reinforcement from reduction of the anxiety associated with thought disorganization. The present study tested an aspect of this postulate. It was predicted that late-adolescent males who had an open style of adapting to experienced aversive maternal control would find the organization of disorganized evaluative cues uniquely reinforcing. Subjects were presented with an anagrams task made up of scrambled words which they were told came from a poll of mothers asked to evaluate their college-age sons. This task was followed by the individual tachistoscopic and slightly unfocused presentation of a longer series of words, again described as maternal evaluative terms. The longer series comprised the words used in the anagrams task and an equal number which were not. Reinforcement value of proper ordering of disorganized cues was inferred from the subject's heightened ability to detect previously solved anagram terms, indicating stronger learning of these terms upon prior solution. Males identified as open-style adapters were far more cognizant of words which they had previously ordered than other child-rearing groups, as predicted.

Adolescent↗

PubMed2026

Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.

OBJECTIVE: Radiogenomics aims to non-invasively predict tumour genotypes from imaging, but most studies assume molecular homogeneity by assigning a single biopsy-derived label to all lesions within a patient. This approach risks substantial label noise given well-documented interlesional heterogeneity. We investigated whether anchoring training to biopsy-confirmed lesions improves radiogenomic model performance and generalisability. MATERIALS AND METHODS: We retrospectively analysed 1646 patients (11473 segmented lesions) with contrast-enhanced CT and EGFR mutation status from next-generation sequencing at the Netherlands Cancer Institute, alongside an external NSCLC radiogenomics cohort (n = 158). All visible lesions were segmented, and the exact biopsy site was matched to its segmentation. Radiomic features were extracted, and machine learning models were trained with three lesion selection strategies: all lesions, non-biopsied lesions only, and biopsy-confirmed lesions only. To disentangle label quality from sample size, we created size-matched variants (one lesion per patient) for all-lesion and non-biopsied strategies. RESULTS: All models achieved significant discrimination of EGFR status on internal validation (AUC = 0.62-0.68). However, performance of the all-lesion and non-biopsied models declined on external validation (AUC = 0.55-0.63), while the biopsy-anchored model maintained stable performance (AUC = 0.62), despite having only 1/10th of the training sample size. When training sets were size-matched, the biopsy-anchored approach significantly outperformed a model trained on all available lesions on external validation (p = 0.037). CONCLUSIONS: Radiogenomic models trained on biopsy-confirmed lesions outperform conventional all-lesion strategies in external validation, despite using an order of magnitude fewer samples. Prioritising lesion-level label fidelity can mitigate heterogeneity-driven noise, enhancing robustness and clinical translation of imaging-based genomic prediction. KEY POINTS: Question Does assigning biopsy-derived molecular labels to all lesions introduce heterogeneity-driven label noise that reduces the generalisability of radiogenomic models? Findings Models trained exclusively on biopsy-confirmed lesions demonstrated superior external generalisability compared with all-lesion approaches, despite being trained on substantially fewer samples. Clinical relevance Biopsy-anchored radiogenomics improves the reliability of non-invasive mutation prediction by accounting for tumour heterogeneity, potentially supporting clinical decision-making when tissue sampling is limited or molecular results are discordant across lesions.

Humans↗

PubMed2001

Conjunctive representations, the hippocampus, and contextual fear conditioning.

The context in which events occur can be represented as both (1) a set of independent features, the feature representation view, and (2) a set of features bound into a unitary representation, the conjunction representation view. It is assumed that extrahippocampal (e.g., neocortical) areas provide a basis for feature representations, but the hippocampal formation makes an essential contribution to the automatic storage of conjunctive representations. We develop this dual-representation view and explore its implications for hippocampal contributions to contextual fear conditioning processes. To this end, we discuss how our framework can resolve some of the conflicts in the recent literature relating the hippocampus to contextual fear conditioning. We also present new data supporting the role of a key mechanism afforded by conjunctive representations--pattern completion (the ability of a subset of a memory pattern to activate the complete memory)--in contextual fear conditioning. As is implied by this mechanism, we report that fear can be conditioned to the memory representation of a context that is not actually present at the time of shock. Moreover, this result is predicted by our computational model of cortical and hippocampal function. We suggest that pattern completion demonstrated in animals and by our model provides a mechanistic bridge to human declarative memory.

Animals↗

PubMed2006

Hippocampal CA1 spiking during encoding and retrieval: relation to theta phase.

The hippocampal theta rhythm is a prominent oscillation in the field potential observed throughout the hippocampus as a rat investigates stimuli in the environment. A recent computational model [Hasselmo, M. E., Bodelon, C., & Wyble, B. P. (2002a). A proposed function for hippocampal theta rhythm: separate phases of encoding and retrieval enhance reversal of prior learning. Neural Computation, 14, 793-817. Neuromodulation, theta rhythm and rat spatial navigation. Neural Networks, 15, 689-707] suggested that the theta rhythm allows the hippocampal formation to alternate rapidly between conditions that promote memory encoding (strong synaptic input from entorhinal cortex to areas CA3 and CA1) and conditions that promote memory retrieval (strong synaptic input from CA3 to CA1). That model predicted that the preferred theta phase of CA1 spiking should differ for information being encoded versus information being retrieved. In the present study, the spiking activity of CA1 pyramidal cells was recorded while rats performed either an odor-cued delayed nonmatch-to-sample recognition memory test or an object recognition memory task based on the animal's spontaneous preference for novelty. In the test period of both tasks, the preferred theta phase exhibited by CA1 pyramidal cells differed between moments when the rat inspected repeated (match) and non-repeated (nonmatch) items. Also in the present study, additional modeling work extended the previous model to address the mean phase of CA1 spiking associated with stimuli inducing varying levels of retrieval relative to encoding, ranging from novel nonmatch stimuli with no retrieval to highly familiar repeated stimuli with extensive retrieval. The modeling results obtained here demonstrated that the experimentally observed phase differences are consistent with different levels of CA3 synaptic input to CA1 during recognition of repeated items.

Animals↗

PubMed2004

Simulated apoptosis/neurogenesis regulates learning and memory capabilities of adaptive neural networks.

Characterization of neuronal death and neurogenesis in the adult brain of birds, humans, and other mammals raises the possibility that neuronal turnover represents a special form of neuroplasticity associated with stress responses, cognition, and the pathophysiology and treatment of psychiatric disorders. Multilayer neural network models capable of learning alphabetic character representations via incremental synaptic connection strength changes were used to assess additional learning and memory effects incurred by simulation of coordinated apoptotic and neurogenic events in the middle layer. Using a consistent incremental learning capability across all neurons and experimental conditions, increasing the number of middle layer neurons undergoing turnover increased network learning capacity for new information, and increased forgetting of old information. Simulations also showed that specific patterns of neural turnover based on individual neuronal connection characteristics, or the temporal-spatial pattern of neurons chosen for turnover during new learning impacts new learning performance. These simulations predict that apoptotic and neurogenic events could act together to produce specific learning and memory effects beyond those provided by ongoing mechanisms of connection plasticity in neuronal populations. Regulation of rates as well as patterns of neuronal turnover may serve an important function in tuning the informatic properties of plastic networks according to novel informational demands. Analogous regulation in the hippocampus may provide for adaptive cognitive and emotional responses to novel and stressful contexts, or operate suboptimally as a basis for psychiatric disorders. The implications of these elementary simulations for future biological and neural modeling research on apoptosis and neurogenesis are discussed.

Animals↗

PubMed2005

Ambiguity resolution analysis in incremental parsing of natural language.

Incremental parsing gains its importance in natural language processing and psycholinguistics because of its cognitive plausibility. Modeling the associated cognitive data structures, and their dynamics, can lead to a better understanding of the human parser. In earlier work, we have introduced a recursive neural network (RNN) capable of performing syntactic ambiguity resolution in incremental parsing. In this paper, we report a systematic analysis of the behavior of the network that allows us to gain important insights about the kind of information that is exploited to resolve different forms of ambiguity. In attachment ambiguities, in which a new phrase can be attached at more than one point in the syntactic left context, we found that learning from examples allows us to predict the location of the attachment point with high accuracy, while the discrimination amongst alternative syntactic structures with the same attachment point is slightly better than making a decision purely based on frequencies. We also introduce several new ideas to enhance the architectural design, obtaining significant improvements of prediction accuracy, up to 25% error reduction on the same dataset used in previous work. Finally, we report large scale experiments on the entire Wall Street Journal section of the Penn Treebank. The best prediction accuracy of the model on this large dataset is 87.6%, a relative error reduction larger than 50% compared to previous results.

Algorithms↗

PubMed2026

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

Humans↗

PubMed2004

Graft-versus-host disease of the skin: life and death on the epidermal edge.

Despite impressive advances in the field of allogeneic hematopoietic transplantation, graft versus host disease (GVHD) remains a significant obstacle to be overcome; it would enhance the safety and efficacy of this life-saving therapy. This review provides a framework for understanding the molecular and cellular basis underlying GVHD. We propose a 3-phase model of GVHD that highlights the importance of the conditioning regimen on the recipient tissues administered prior to infusion of donor bone marrow inoculum. A novel skin explant model, designed to take into consideration the immunobiological consequences of conditioning regimens on resident host cells, is proposed to advance our understanding of GVHD and serve as a potential prognostic tool when allogeneic recipient/donor combinations are being contemplated in the clinic. Within this review, specific emphasis is placed on the importance of defining the apoptotic machinery engaged in epidermal keratinocytes triggered by both conditioning regimens, and by host resident and recruited immunocytes and soluble mediators produced at sites of injury. The review is completed with a working model for cutaneous GVHD. Although the skin is highlighted because of its accessibility for clinical observations and serial sampling opportunities, lessons learned from studies of cutaneous GVHD are likely to provide valuable insights into GVHD occurring in the gastrointestinal tract, lung, and liver. With new insights designed to better predict and prevent GVHD and novel agents designed to treat GVHD, overcoming this current impediment to successful bone marrow transplantation should become increasingly feasible.

Apoptosis↗

PubMed2025

Conserved HSFA1-dependent chromatin dynamics drive heat stress responses in plants.

Eukaryotic organisms remodel chromatin landscapes to regulate gene expression in response to environmental stress. In plants, heat stress (HS) induces widespread chromatin changes, yet the role of heat shock transcription factors (HSFs) in chromatin remodeling and their evolutionary conservation remains unclear. Using Marchantia polymorpha Mphsf mutants and Arabidopsis thaliana Athsfa1s mutants, we identify HSFA1 as a key regulator of HS-induced cis-regulatory element (CRE) accessibility, a mechanism conserved across land plants, mice, and humans. Gene regulatory network modeling reveals parallel transcription factor subnetworks, with MpWRKY10 and MpABI5B acting as indirect and negative HS regulators. We further showed that ABA modulates gene expression in an HSFA1-dependent manner without inducing chromatin remodeling. Finally, we develop a machine learning framework integrating chromatin accessibility and CRE information to predict gene expression across species, revealing stress-responsive regulatory logic at the transcriptional level. These findings provide insights into how TFs coordinate chromatin architecture to drive stress adaptation.

Heat-Shock Response↗