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 1,045 records · Page 58Linked to original sources

PubMed1991

Composite models never (well, hardly ever) compromise: reply to Schooler and Tanaka (1991)

With respect to the influence of postevent information, Schooler and Tanaka (1991) made a useful distinction between composite recollections--in which subjects retrieve "items from both the original and the postevent sources" (p.97)--and compromise recollections--in which subjects retrieve" at least one feature that cannot be exclusively associated with either the original or the postevent sources, but which reflects some compromise between [the] two" (p.97). Schooler and Tanaka argued that only the latter constitutes good evidence for blend-memory representations of the CHARM-type. As it turns out, Schooler and Tanaka's intuitions (and Metcalfe & Bjork's, initially) are faulty. Compromise recall--defined as a preference for an intervening alternative over either of the actually presented alternatives--is not normally a prediction of CHARM and may not be a prediction of composite-trace models in general. Only under specialized conditions--a systematic displacement of the test alternatives or a systematic shift attributable to assimilation to prior semantic knowledge--will computer simulations of CHARM produce unimodal compromise recollection. Equally surprising is the fact that separate-trace models, under a different set of conditions, can predict compromise recollection.

Association Learning↗

PubMed2006

Numbers and space: a computational model of the SNARC effect.

The SNARC (spatial numerical associations of response codes) effect reflects the tendency to respond faster with the left hand to relatively small numbers and with the right hand to relatively large numbers (S. Dehaene, S. Bossini, & P. Giraux, 1993). Using computational modeling, the present article aims to provide a framework for conceptualizing the SNARC effect. In line with models of spatial stimulus-response congruency, the authors modeled the SNARC effect as the result of parallel activation of preexisting links between magnitude and spatial representation and short-term links created on the basis of task instructions. This basic dual-route model simulated all characteristics associated with the SNARC effect. In addition, 2 experiments tested and confirmed new predictions derived from the model.

Adult↗

PubMed1988

Depth discrimination from optic flow.

A simple scheme for deriving relative depth (time-to-collision, or TTC) from optic flow is developed in which the total flow is first sensed by unconnected motion (imperfect filter) sensors and then the rotational component is subtracted to yield the translational component. Only the latter component yields depth information. This scheme is contrasted with one where the TTC sensors respond only to the translational component at the initial registration of the flow (perfect filter sensors or looming detectors). The simple scheme predicts the results of three experiments on discrimination of TTC: discrimination thresholds are elevated if the objects withdraw from rather than approach the observer, thresholds are elevated if a rotational component is added to the flow, and the amount of threshold elevation resulting from the addition of a rotational component is reduced by prior adaptation to a pure rotational flow. These results confirm the simple model and disconfirm predictions based on the looming detector scheme.

Attention↗

PubMed2005

Protein sequence-based risk classification for human papillomaviruses.

Human papillomaviruses (HPVs) are small DNA tumor viruses which infect epithelial tissues and induce hyperproliferative lesions. Infection by high-risk genital HPVs is associated with the development of anogenital cancers. Classification of risk types is important in understanding the mechanisms in infection and in developing novel instruments for medical examination such as DNA microarrays. The sequence-based classification methods are useful in classifying risk types by considering residues in conserved positions. In this paper, we present a machine learning approach to the classification of HPV risk types by using the protein sequences. Our approach is based on the hidden Markov model and the kernel method. The former searches informative subsequence positions and the latter computes efficiently to classify protein sequences. In the experiments, the classifier predicted four unknown HPV types exactly. An additional result shows that the kernel-based classifiers learned with more informative subsequences outperform the classifiers learned with the whole sequence or random subsequences.

Amino Acid Sequence↗

PubMed2000

Learning cortical topography from spatiotemporal stimuli.

Stimulus representation is a functional interpretation of early sensory cortices. Early sensory cortices are subject to stimulus-induced modifications. Common models for stimulus-induced learning within topographic representations are based on the stimuli's spatial structure and probability distribution. Furthermore, we argue that average temporal stimulus distances reflect the stimuli's relatedness. As topographic representations reflect the stimuli's relatedness, the temporal structure of incoming stimuli is important for the learning in cortical maps. Motivated by recent neurobiological findings, we present an approach of cortical self-organization that additionally takes temporal stimulus aspects into account. The proposed model transforms average interstimulus intervals into representational distances. Thereby, neural topography is related to stimulus dynamics. This offers a new time-based interpretation of cortical maps. Our approach is based on a wave-like spread of cortical activity. Interactions between dynamics and feedforward activations lead to shifts of neural activity. The psychophysical saltation phenomenon may represent an analogue to the shifts proposed here. With regard to cortical plasticity, we offer an explanation for neurobiological findings that other models cannot explain. Moreover, we predict cortical reorganizations under new experimental, spatiotemporal conditions. With regard to psychophysics, we relate the saltation phenomenon to dynamics and interaction in early sensory cortices and predict further effects in the perception of spatiotemporal stimuli.

Animals↗

PubMed1999

Machine learning approaches for the prediction of signal peptides and other protein sorting signals.

Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently, the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, have made it possible to achieve a level of reliability where practical use in, for example automatic database annotation is feasible. In this review, we concentrate on the present status and future perspectives of SignalP, our neural network-based method for prediction of the most well-known sorting signal: the secretory signal peptide. We discuss the problems associated with the use of SignalP on genomic sequences, showing that signal peptide prediction will improve further if integrated with predictions of start codons and transmembrane helices. As a step towards this goal, a hidden Markov model version of SignalP has been developed, making it possible to discriminate between cleaved signal peptides and uncleaved signal anchors. Furthermore, we show how SignalP can be used to characterize putative signal peptides from an archaeon, Methanococcus jannaschii. Finally, we briefly review a few methods for predicting other protein sorting signals and discuss the future of protein sorting prediction in general.

Algorithms↗

PubMed2006

Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods.

Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure-activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1-97.6% for ER binders and 80.2-96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.

Forecasting↗

PubMed2005

Reorganization and plasticity in the adult brain during learning of motor skills.

On the basis of brain imaging studies, Doyon and Ungerleider recently proposed a model describing the cerebral plasticity that occurs in both cortico-striatal and cortico-cerebellar systems of the adult brain during learning of new motor skilled behaviors. This theoretical framework makes several testable predictions with regards to the contribution of these neural systems based on the phase (fast, slow, consolidation, automatization, and retention) and nature of the motor learning processes (motor sequence versus motor adaptation) acquired through repeated practice. There has been recent behavioral, lesion and additional neuroimaging studies that have addressed the assumptions made in this theory that will help in the revision of this model.

Adult↗

PubMed1994

Classification and perceived similarity of compound gratings that differ in relative spatial phase.

Discrimination studies suggest that two, and only two, channels encode relative spatial phase shifts in compound gratings (Bennett & Banks, 1991; Field & Nachmias, 1984). The more sensitive channel consists of even-symmetric filters and responds best to cosine phase shifts (e.g., 0 degree-180 degrees); the other consists of odd-symmetric filters and responds best to sine phase shifts (e.g., 90 degrees-270 degrees). The present experiments investigated whether the two-channel model generalizes to suprathreshold perceptual tasks. Experiment 1 examined classification learning of compound gratings, consisting of a fundamental (f) and second harmonic (2f), that differed in 2f contrast and relative phase. Experiments 2 and 3 measured the perceived similarity of f + 2f gratings. The results of Experiment 1 were broadly consistent with the predictions of the two-channel model. Specifically, the classification data were best explained by assuming that classification was based on the responses of differentially sensitive even- and odd-symmetric filters. In Experiments 2 and 3, two-dimensional multidimensional scaling solutions provided a good account for the similarity judgments. In Experiment 2, Dimension 1 was strongly correlated with cosine phase, and Dimension 2 was moderately correlated with sine phase. In Experiment 3, cosine phase was again strongly related to Dimension 1, whereas the absolute value of sine phase was strongly related to Dimension 2. Overall, these results suggest that the two-channel model of phase discrimination provides a useful framework for interpreting classification and similarity judgments of compound gratings.

Adult↗

PubMed1977

Pattern structure and relational discrimination learning.

McGonigle and Jones take exception to Dodwell's explanation of anomalous transfer (AT) as the outcome of relational discriminations among a set of stimulus patterns which vary along a single dimension of orientational salience, from 'horizontal' to 'vertical'. In particular they do not think that the continuum is generated by units with Hubel-and-Wiesel type retinal receptive fields. Instead, they invoke Garner's notions about stimulus structure to explain Dodwell's results, as well as their own finding of two situations where AT fails to occur. It seems that McGonigle and Jones missed the point of the relational discrimination explanation of AT. In fact, it is shown that AT is not predicted by this model for the conditions in which they failed to obtain it. The relational model makes definite predictions about conditions under which AT will occur; as this is not true of their invocation of Garner's ideas, the former is to be preferred. Whether or not outputs are coded by Hubel-and Wiesel type units is not particularly relevant to the main point, that AT is a result of relational learning. In fact, the model is an instance of how structure, in Garner's sense, can be generated.

Animals↗

PubMed2000

Learning and selective attention.

Selective attention involves the differential processing of different stimuli, and has widespread psychological and neural consequences. Although computational modeling should offer a powerful way of linking observable phenomena at different levels, most work has focused on the relatively narrow issue of constraints on processing resources. By contrast, we consider statistical and informational aspects of selective attention, divorced from resource constraints, which are evident in animal conditioning experiments involving uncertain predictions and unreliable stimuli. Neuromodulatory systems and limbic structures are known to underlie attentional effects in such tasks.

Animals↗

PubMed2026

Breast Cancer Recurrence Status Assessment in 5 Years Using Multimodal Integrated Learning: A Feasibility Study.

Despite advances in breast cancer detection and treatment, recurrence after curative therapy continues to impact long-term survival and quality of life. Therefore, early identification of high-risk patients is crucial to guide personalized treatment and follow-up strategies. Although genomic assays provide valuable prognostic insights, their high cost and limited accessibility hinder widespread adoption in clinical practice. Recent machine learning or deep learning approaches leveraging clinical, imaging, or multimodal data have shown promise but do not reflect real-world clinical scenarios. This study proposes a deep learning-based multimodal framework for predicting 5-year breast cancer recurrence using routinely collected clinical data. The framework consists of three main components. First, we adopted automated tumor segmentation with MedSAM to extract the tumor region from ultrasound images. The radiomics features are extracted from those tumor regions. Second, report features are extracted using a Med-Contrastive Pre-trained Transformers (MedCPT)-based approach incorporating predefined, clinically informed queries. Third, a multimodal integration model jointly processes image, radiomics, clinical features, and report features through modality-specific branches. The image branch employs the Ultrasound Foundation Model (USFM) as the backbone, while structured tabular data is processed using the FT-Transformer architecture. The features of all branches are fused using a mixture-of-experts (MoE)-based classifier, and the entire model is trained using a progressive fusion training strategy. Experimental results confirm the feasibility of using ultrasound images with tumor mask integration for recurrence prediction and demonstrate the additive value of integrating multiple data modalities through the proposed multimodal integration model. The final model for recurrence prediction achieved an AUC of 0.7540, accuracy of 74.61%, sensitivity of 70.41%, and specificity of 76.44%. This feasibility study's findings underscore the potential of the proposed multimodal deep learning framework to provide accessible, accurate, and generalizable recurrence risk prediction using routinely available clinical data, potentially supporting more informed treatment decisions and personalized post-treatment monitoring in real-world clinical practice.

Breast cancer recurrence↗

PubMed2003

Predicting risk of coronary artery disease from DNA microarray-based genotyping using neural networks and other statistical analysis tool.

This paper presents a novel approach for complex disease prediction that we have developed, exemplified by a study on risk of coronary artery disease (CAD). This multi-disciplinary approach straddles fields of microarray technology and genetics, neural networks (NN), data mining and machine learning, as well as traditional statistical analysis techniques, namely principal components analysis (PCA) and factor analysis (FA). A description of the biological background of the study is given, followed by a detailed description of how the problem has been modeled for analyses by neural networks and FA. A committee learning approach for NN has been used to improve generalization rates. We show that our NN approach is able to yield promising prediction results despite using only the most fundamental network structures. More interestingly, through the statistical analysis process, genes of similar biological functions have been clustered. In addition, a gene marker involved in breaking down lipids has been found to be the most correlated to CAD.

Algorithms↗

PubMed1997

[Information model of systemic organization of human mental functions: new approach to the problem of artificial intelligence].

The information model of systemic functions of the human brain reproduces important features of human intelligence: goal planning, prediction of needed results, behavior correction by feedback from the parameters of the results obtained and their comparison with the acceptor of action results. Human behavior imitation in the model is statistically proved by testing the model on a computer. An Adaptron apparatus based on the model quantitatively estimates the parameters of an important mechanism of the brain, namely human intuitive learning. With the Adaptron, the ade-dependent properties and some human intellectual dysfunctions are estimated, which reflect the mechanisms of prediction of future results, the building of memory traces in intuitive learning when the findings coincide with the planned results and the restructure of memory and respective behavior changes when the findings do not coincide with the planned ones.

Artificial Intelligence↗

PubMedDate not supplied

Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs.

The support vector machine (SVM), as a novel type of a learning machine, for the first time, was used to develop a QSPR model that relates the structures of 35 amino acids to their isoelectric point. Molecular descriptors calculated from the structure alone were used to represent molecular structures. The seven descriptors selected using GA-PLS, which is a sophisticated hybrid approach that combines GA as a powerful optimization method with PLS as a robust statistical method for variable selection, were used as inputs of RBFNNs and SVM to predict the isoelectric point of an amino acid. The optimal QSPR model developed was based on support vector machines, which showed the following results: the root-mean-square error of 0.2383 and the prediction correlation coefficient R=0.9702 were obtained for the whole data set. Satisfactory results indicated that the GA-PLS approach is a very effective method for variable selection, and the support vector machine is a very promising tool for the nonlinear approximation.

Amino Acids↗

PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2004

Enhancing active learning in the student laboratory.

We previously examined how three approaches to directing students in a laboratory setting impacted their ability to repair a faulty mental model in respiratory physiology (Modell, HI, Michael JA, Adamson T, Goldberg J, Horwitz BA, Bruce DS, Hudson ML, Whitescarver SA, and Williams S. Adv Physiol Educ 23: 82-90, 2000). This study addresses issues raised by the results of that work. In one group, a written protocol directed students to predict what would happen to frequency and depth of breathing during exercise on a bicycle ergometer, run the experiment, and compare their results to their predictions ("predictor without verification"). In a "predictor with verification" group, students followed the same written protocol but were also required to show the instructor their predictions before running the experiment. Students in a third group reported their predictions verbally to an instructor immediately before exercise and reviewed their results with that instructor immediately after exercise ("instructor intervention group"). Results of this study were consistent with our earlier work. The predictor with verification and predictor without verification protocols yielded similar results. The instructor intervention protocol yielded higher success rates in repairing students' mental models. We subsequently assessed the efficacy of a prediction period at the beginning of the lab session and a wrap-up period at the end to compare predictions and results. This predict and wrap-up protocol was more effective than the predictor without verification protocol, but it was not as effective as the instructor intervention protocol. Although these results may reflect multiple factors impacting learning in the student laboratory, we believe that a major factor is a mismatch between students' approaches to learning and the intended learning outcomes of the experience.

Education, Medical, Undergraduate↗

PubMed2002

Simulating closed- and open-loop voluntary movement: a nonlinear control-systems approach.

In many recent human motor control models, including feedback-error learning and adaptive model theory (AMT), feedback control is used to correct errors while an inverse model is simultaneously tuned to provide accurate feedforward control. This popular and appealing hypothesis, based on a combination of psychophysical observations and engineering considerations, predicts that once the tuning of the inverse model is complete the role of feedback control is limited to the correction of disturbances. This hypothesis was tested by looking at the open-loop behavior of the human motor system during adaptation. An experiment was carried out involving 20 normal adult subjects who learned a novel visuomotor relationship on a pursuit tracking task with a steering wheel for input. During learning, the response cursor was periodically blanked, removing all feedback about the external system (i.e., about the relationship between hand motion and response cursor motion). Open-loop behavior was not consistent with a progressive transfer from closed- to open-loop control. Our recently developed computational model of the brain--a novel nonlinear implementation of AMT--was able to reproduce the observed closed- and open-loop results. In contrast, other control-systems models exhibited only minimal feedback control following adaptation, leading to incorrect open-loop behavior. This is because our model continues to use feedback to control slow movements after adaptation is complete. This behavior enhances the internal stability of the inverse model. In summary, our computational model is currently the only motor control model able to accurately simulate the closed- and open-loop characteristics of the experimental response trajectories.

Adaptation, Biological↗