SEARCH · PubMed Health

Results for “generative artificial intelligence”

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 487 records · Page 27Linked to original sources

PubMed2006

Learning the relationship between patient geometry and beam intensity in breast intensity-modulated radiotherapy.

Intensity modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time adjusting IMRT optimization parameters in order to get a clinically acceptable plan. We demonstrate that the relationship between patient geometry and radiation intensity distributions can be automatically inferred using a variety of machine learning techniques in the case of two-field breast IMRT. Our experiments show that given a small number of human-expert-generated clinically acceptable plans, the machine learning predictions produce equally acceptable plans in a matter of seconds. The machine learning approach has the potential for greater benefits in sites where the IMRT planning process is more challenging or tedious.

Artificial Intelligence↗

PubMed2005

A soft double regularization approach to parametric blind image deconvolution.

This paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures. Conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information. This paper attempts to address this issue by assessing the relevance of parametric blur information, and incorporating the knowledge into the parametric double regularization (PDR) scheme. The PDR method assumes that the actual blur satisfies up to a certain degree of parametric structure, as there are many well-known parametric blurs in practical applications. Further, it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available. A manifold soft parametric modeling technique is proposed to generate the blur manifolds, and estimate the fuzzy blur structure. The PDR scheme involves the development of the meaningful cost function, the estimation of blur support and structure, and the optimization of the cost function. Experimental results show that it is effective in restoring degraded images under different environments.

Algorithms↗

PubMed1990

Bifurcation analysis of nonlinear retinal horizontal cell models. II. Network properties.

1. We have previously presented a model of horizontal-cell soma isolated from fish retina. The model consists of a synaptic conductance representing input from photoreceptors in parallel with voltage-dependent membrane currents. Membrane-current models are based on I-V curves measured in isolated fish horizontal cells. Bifurcation theory was used to analyze model properties. The major findings of this study were 1) the inward Ca2+ current must be inactivated to account for horizontal-cell resting potentials and hyperpolarizing responses to light stimuli in a background of dark, and 2) the synaptic conductance controls the bifurcation structure of the model, with bistable behavior occurring at small and monostable behavior occurring at larger values of the synaptic conductance. The synaptic conductance at the point of transition from bistable to monostable behavior corresponds to the activation of as few as 100 synaptic channels. Thus tonic synaptic input from photoreceptors and inactivation of the inward Ca2+ current act to "linearize" responses of isolated horizontal-cell models. 2. The model described in this paper extends these analyses to large networks of horizontal cells in which each cell is coupled resistively to its nearest neighbors and is modeled with the use of the full complement of nonlinear membrane currents. Network responses to arbitrary patterns of conductance change (simulating inputs from photoreceptors), current-, or voltage-clamp stimuli are computed using the Newton iteration. The Newton descent direction is computed using either conjugate gradient (CG) or preconditioned CG algorithms. 3. An analysis of network stability properties is performed. Network I-V curves are computed by voltage-clamping the center node and computing the current required to maintain the clamp voltage. Computations are performed on networks of model cells in which the Ca2+ current is fully activated and the synaptic conductance is zero, thus making each cell as nonlinear as possible. Coupling conductance values slightly greater than 100 pS provide a current shunt sufficient to prevent the generation of Ca2+ action potentials in the network. This coupling conductance corresponds to the conductance of as few as two gap-junction channels and is more than two orders of magnitude less than the coupling known to exist between pairs of cultured horizontal cells.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗

PubMedDate not supplied

Feasibility of physician-developed expert systems.

The authors developed an experimental domain-independent "expert system generator" intended for direct use by physicians. They then undertook a four-year study to determine whether physicians could use such a system effectively. During this period they taught the use of the expert system generator to 70 medical students, who utilized it to build two small medical expert systems. At the conclusion of the course, students were examined on decision-making concepts and completed anonymous questionnaires. Performance scores, a composite of test and project grades, were calculated for each student. There was no significant association between previous computer experience and performance score. Thirty-two of 47 students responding felt the expert system generator was easy to use; 15 felt it was of moderate difficulty. Forty-three of 47 thought it a useful teaching aid. These data support the conclusion that physicians can learn to use domain-independent software to implement medical expert systems directly, without a knowledge engineer as an intermediary.

Artificial Intelligence↗

PubMed2005

Three-dimensional vision with dual acousto-optic deflection encoding.

We report a new method of sensing a three-dimensional (3D) object surface with an arbitrary geometric shape. In this approach, the first-order beams diffracted from two acousto-optic deflectors (AODs) interfere with each other to form a spatial carrier that is used to encode the depth information from the 3D object surface. A direct digital synthesizer is utilized to control two AODs to generate sequentially spatial carriers with different spatial frequencies so that a modified temporal phase-unwrapping technique can be applied for decoding the shape information of the test surface. Preliminary experimental results are presented to demonstrate the effectiveness of this method.

Algorithms↗

PubMed1998

Population size and quality in genetics-based rule learning from medical data.

Population size and quality are parameters which control the performance of genetic algorithms. We researched these parameters in a genetic-based machine learning system Galactica which was used to discover the differential diagnostic rules for female urinary incontinence from case data. The performance of the system was measured with on-line and off-line criteria. Surprisingly, randomly generated small populations (30 and 70 rules) did not promote the best on-line performance as earlier results suggested. Probable explanation is the lack of diversity in initial populations. The seeding of population with positive learning examples was used to obtain more divergent populations. As expected, the seeding increased the on-line performance of small populations. The results are mainly in accord with the earlier results indicating that large randomly generated populations (150 rules) lead to the better off-line performance. Again, the seeding of small populations was successful producing even the better off-line performance than a large population. In conclusion, the seeding allowed the small populations to converge to good rules in relatively short period of time.

Algorithms↗

PubMed2026

Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.

Hyperlipidemia, characterized by elevated blood lipid levels, represents a major global health concern due to its strong association with cardiovascular disease, diabetes, and metabolic syndrome. While current therapies - such as statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors - are effective in controlling hyperlipidemia, they are often associated with adverse effects, potential drug resistance, and suboptimal efficacy in certain patient populations. All of the above underscore the urgent need for safer and more effective therapeutic alternatives. Among the major molecular targets involved in the regulation of lipid metabolism are HMG-CoA reductase, PCSK9, peroxisome proliferator-activated receptors (PPARs), cholesteryl ester transfer protein (CETP), and nuclear receptors, including the liver X receptor (LXR) and farnesoid X receptor (FXR), which are also targets for future antihyperlipidemic drug development. Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed and accelerated drug discovery by enabling the processing of vast amounts of genomic, proteomic, and chemical data. Furthermore, ML tools such as quantitative structure-activity relationship (QSAR) modelling, deep learning, random forest, and support vector machines (SVM) have proven predictive and effective in identifying novel lipid metabolism modulators, thereby enhancing the efficacy and accuracy of virtual screening. Meanwhile, molecular docking has become an integral part of structure-based drug design (SBDD), and software such as AutoDock, Glide, and GOLD have proven effective in generating accurate ligand-target docking models. Molecular docking, together with ML-based approaches, enables the identification of potent and selective drug candidates. Overall, the combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.

HMG-CoA reductase↗

PubMed2004

New paradigms in telemedicine: ambient intelligence, wearable, pervasive and personalized.

After decades of development of information systems dedicated to health professionals, there is an increasing demand for personalized and non-hospital based care. An especially critical domain is cardiology: almost two third of cardiac deaths occur out of hospital, and victims do not survive long enough to benefit from in-hospital treatments. We need to reduce the time before treatment. But symptoms are often interpreted wrongly. The only immediate diagnostic tool to assess the possibility of a cardiac event is the electrocardiogram (ECG). Event and transtelephonic ECG recorders are used to improve decision making but require setting up new infrastructures. The European EPI-MEDICS project has developed an intelligent Personal ECG Monitor (PEM) for the early detection of cardiac events. The PEM embeds advanced decision making techniques, generates different alarm levels and forwards alarm messages to the relevant care providers by means of new generation wireless communication. It is cost saving, involving care provider only if necessary and requiring no specific infrastructure. This solution is a typical example of pervasive computing and ambient intelligence that demonstrates how personalized, wearable, ubiquitous devices could improve healthcare.

Artificial Intelligence↗

PubMed1991

CAMBIO: software for modelling and simulation of bioprocesses.

CAMBIO, a software package devoted to bioprocess modelling, which runs on Apollo computers, is described. This software enables bioengineers to easily and interactively design appropriate mathematical models directly from their perception of the process. CAMBIO provides the user with a set of design symbols and mnemonic icons in order to interactively design a functional diagram. This diagram has to exhibit the most relevant components with their related interactions through biological and physico-chemical reactions. Then, CAMBIO automatically generates the dynamical material balance equations of the process in the form of an algebraic-differential system by taking advantage of the knowledge involved in the functional diagram. The model may be used for control design purpose or completed by kinetics expressions with a view to simulation. CAMBIO offers facilities to generate a simulation model (for coding of kinetics, introducing auxiliary variables, etc.). This model is automatically interfaced with a specialized simulation software which allows an immediate visualization of the process dynamical behaviour under various operational conditions (possibly involving feedback control strategies). An example of an application dealing with yeast fermentation is given.

Algorithms↗

PubMed2006

Automatic 3-D grayscale volume matching and shape analysis.

Recently, shape matching in three dimensions (3-D) has been gaining importance in a wide variety of fields such as computer graphics, computer vision, medicine, and biology, with applications such as object recognition, medical diagnosis, and quantitative morphological analysis of biological operations. Automatic shape matching techniques developed in the field of computer graphics handle object surfaces, but ignore intensities of inner voxels. In biology and medical imaging, voxel intensities obtained by computed tomography (CT), magnetic resonance imagery (MRI), and confocal microscopes are important to determine point correspondences. Nevertheless, most biomedical volume matching techniques require human interactions, and automatic methods assume matched objects to have very similar shapes so as to avoid combinatorial explosions of point. This article is aimed at decreasing the gap between the two fields. The proposed method automatically finds dense point correspondences between two grayscale volumes; i.e., finds a correspondent in the second volume for every voxel in the first volume, based on the voxel intensities. Mutiresolutional pyramids are introduced to reduce computational load and handle highly plastic objects. We calculate the average shape of a set of similar objects and give a measure of plasticity to compare them. Matching results can also be used to generate intermediate volumes for morphing. We use various data to validate the effectiveness of our method: we calculate the average shape and plasticity of a set of fly brain cells, and we also match a human skull and an orangutan skull.

Algorithms↗

PubMed1989

An AI-based communication system for motor and speech disabled persons: design methodology and prototype testing.

An intelligent communication device is developed to assist the nonverbal, motor disabled in the generation of written and spoken messages. The device is centered on a knowledge base of the grammatical rules and message elements. A "belief" reasoning scheme based on both the information from external sources and the embedded knowledge is used to optimize the process of message search. The search for the message elements is conceptualized as a path search in the language graph, and a special frame architecture is used to construct and to partition the graph. Bayesian "belief" reasoning from the Dempster-Shafer theory of evidence is augmented to cope with time-varying evidence. An "information fusion" strategy is also introduced to integrate various forms of external information. Experimental testing of the prototype system is discussed.

Artificial Intelligence↗

PubMed2003

A comparative study of machine-learning methods to predict the effects of single nucleotide polymorphisms on protein function.

MOTIVATION: The large volume of single nucleotide polymorphism data now available motivates the development of methods for distinguishing neutral changes from those which have real biological effects. Here, two different machine-learning methods, decision trees and support vector machines (SVMs), are applied for the first time to this problem. In common with most other methods, only non-synonymous changes in protein coding regions of the genome are considered. RESULTS: In detailed cross-validation analysis, both learning methods are shown to compete well with existing methods, and to out-perform them in some key tests. SVMs show better generalization performance, but decision trees have the advantage of generating interpretable rules with robust estimates of prediction confidence. It is shown that the inclusion of protein structure information produces more accurate methods, in agreement with other recent studies, and the effect of using predicted rather than actual structure is evaluated. AVAILABILITY: Software is available on request from the authors.

Algorithms↗

PubMed2004

Automatically parcellating the human cerebral cortex.

We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This procedure incorporates both geometric information derived from the cortical model, and neuroanatomical convention, as found in the training set. The result is a complete labeling of cortical sulci and gyri. Examples are given from two different training sets generated using different neuroanatomical conventions, illustrating the flexibility of the algorithm. The technique is shown to be comparable in accuracy to manual labeling.

Algorithms↗

PubMed2026

Machine learning to differentiate colonization from infection in multidrug-resistant Gram-negative bacteria: implications for further research.

PURPOSE OF REVIEW: Machine learning has emerged as a promising tool to support antimicrobial decision-making in infectious diseases. In colonized patients, distinguishing multidrug-resistant Gram-negative bacteria (MDR-GNB) colonization from true infection remains a major clinical challenge, as both delayed appropriate therapy in severe infections and unnecessary broad-spectrum antimicrobial use may adversely affect patient outcomes and antimicrobial stewardship. This review discusses the current evidence on machine learning models for predicting or detecting MDR-GNB infection in colonized patients, highlights key methodological limitations of the available literature, and outlines future research priorities. RECENT FINDINGS: Current evidence specifically evaluating machine learning models beyond logistic regression in MDR-GNB-colonized patients remains limited. Overall, while machine learning may achieve encouraging discriminatory performance, important methodological limitations persist. Most notably, predictive models are frequently developed in heterogeneous populations that do not reflect the clinically relevant populations of colonized patients in which treatment decisions are made. Furthermore, improvements in predictive performance remain modest, possibly reflecting limited sample sizes and data granularity rather than insufficient algorithmic complexity. In our opinion, future advances could require multicenter datasets enriched with longitudinal clinical, microbiological, and genomic information, together with automated feature extraction from electronic health records. SUMMARY: The main challenge for machine learning in predicting MDR-GNB infection in colonized patients may lie not in developing increasingly sophisticated algorithms, but in generating clinically representative datasets and adopting rigorous methodological standards for model development, validation, calibration, and implementation. Future research should prioritize clinically meaningful target populations and demonstrate improvements in patient outcomes and antimicrobial stewardship beyond conventional measures of predictive performance.

antimicrobial resistance↗

PubMed2003

Generating neural circuits that implement probabilistic reasoning.

We extend the hypothesis that neuronal populations represent and process analog variables in terms of probability density functions (PDFs). Aided by an intermediate representation of the probability density based on orthogonal functions spanning an underlying low-dimensional function space, it is shown how neural circuits may be generated from Bayesian belief networks. The ideas and the formalism of this PDF approach are illustrated and tested with several elementary examples, and in particular through a problem in which model-driven top-down information flow influences the processing of bottom-up sensory input.

Action Potentials↗

PubMed2005

Rotation-invariant multiresolution texture analysis using radon and wavelet transforms.

A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A kappa-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods.

Algorithms↗

PubMed1993

ASTM E31.15 on health knowledge representation: the Arden Syntax.

ASTM subcommittee E31.15 on Health Knowledge Representation was formed to promote standards for defining and sharing health knowledge bases. Its first standard, the Ardan Syntax, is focused on knowledge bases that can be represented as a set of independent modules called Medical Logic Modules (MLMs). The standard is in clinical use and has generated significant interest in industry and academics. The Extensions task group plans to extend the syntax where appropriate, to expand to other types of knowledge bases. The Validation/Verification task group is approaching the enormous problem of evaluating knowledge bases and the process of sharing them.

Artificial Intelligence↗

PubMed1999

The use of physician domain knowledge to improve the learning of rule-based models for decision-support.

This paper describes a study testing the hypothesis that the learning of a decision-support model by a computer learning algorithm from clinical data can be improved by the addition of domain knowledge from practicing physicians. The domain of the experiment is community-acquired pneumonia. The overall design of the study compares a computer learning algorithm given clinical data to one given clinical data plus domain knowledge added by physician subjects. This study showed that the performance of the computer-generated models augmented with knowledge added by physician subjects were significantly better than the computer-generated models generated without added knowledge using a two-stage rule induction algorithm in the domain of community-acquired pneumonia. This result was highly significant and shows that the addition of domain knowledge may be beneficial to the learning of clinical decision-support models, especially in domains where data is limited.

Algorithms↗