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Results for “generative artificial intelligence”

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PubMed2001

Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach.

In this article, we revise and try to resolve some of the problems inherent in questionnaire screening of sleep apnea cases and apnea diagnosis based on attributes which are relevant and reliable. We present a way of learning information about the relevance of the data, comparing this with the definition of the information by the medical expert. We generate a predictive data model using a data aggregation operator which takes relevance and reliability information about the data into account to produce a diagnosis for each case. We also introduce a grade of membership for each question response which allows the patient to indicate a level of confidence or doubt in their own judgement. The method is tested with data collected from patients in a Sleep Clinic using questionnaires specially designed for the study. Other artificial intelligence predictive modeling algorithms are also tested on the same data and their predictive accuracy compared to that of the aggregation operator.

Adult↗

PubMed1994

A C++ framework for developing Medical Logic Modules and an Arden Syntax compiler.

When developing a clinical decision support system that uses knowledge expressed in Arden Syntax, the availability of a robust means of translating Arden Syntax into an executable module becomes critical. This paper describes an approach where Arden Syntax is translated into an intermediate pseudo-Arden language that is in turn compiled and linked to create the executable module. The pseudo-Arden language is defined in C++ using specialized class libraries and preprocessor macros. This approach provides an alternative means of developing the code generator for an Arden Syntax compiler.

Artificial Intelligence↗

PubMed2004

Functional genomic hypothesis generation and experimentation by a robot scientist.

The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data are being generated much faster than they can be effectively analysed. We describe a physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises experiments to test these hypotheses, physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses inconsistent with the data, and then repeats the cycle. Here we apply the system to the determination of gene function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth experiments. We built and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid synthesis pathway. In biological experiments that automatically reconstruct parts of this model, we show that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold (respectively), both cheapest and random-experiment selection.

Algorithms↗

PubMed1998

Building cross-thesauri with the support of UMLS.

The absence of a robust system of descriptors (cross-thesaurus) hampers the development of combinatorial terminological systems. We developed a tool (I-BROWSE) to produce a cross-thesaurus by analyzing terminological corpora. To facilitate the work of experts and to produce re-usable results, our application interacts via the Internet with the UMLS Knowledge Sources Server. We applied our tool on 2999 dissections on surgical procedures produced in the project GALEN-IN-USE, as a part of the internal Quality Assurance program. Support from UMLS seems mostly promising about descriptors on , , and . Additional assistance can be given to domain experts on less frequent descriptors on pervasive modifiers. We plan to apply our tool also to production of terminological standards in CEN, as a part of a worldwide process of gradual convergence and transformation of coding systems into second-generation systems and terminological services.

Artificial Intelligence↗

PubMed1994

Use of artificial intelligence to analyze clinical database reduces workload on surgical house staff.

BACKGROUND: The current quantity and diversity of hospital clinical, laboratory, and pharmacy records have resulted in a glut of information, which can be overwhelming to house staff. This study was performed to measure the impact of artificial intelligence analysis of such data on the junior surgical house staff's workload, time for direct patient care, and quality of life. METHODS: A personal computer was interfaced with the hospital computerized patient data system. Artificial intelligence algorithms were applied to retrieve and condense laboratory values, microbiology reports, and medication orders. Unusual laboratory tests were reported without artificial intelligence filtering. RESULTS: A survey of 23 junior house staff showed a requirement for a total of 30.75 man-hours per day, an average of 184.5 minutes per service twice a day for five surgical services each with an average of 40.7 patients, to manually produce a report in contrast to a total of 3.4 man-hours, an average of 20.5 minutes on the same basis (88.9% reduction, p < 0.001), to computer generate and distribute a similarly useful report. Two thirds of the residents reported an increased ability to perform patient care. CONCLUSIONS: Current medical practice has created an explosion of information, which is a burden for surgical house staff. Artificial intelligence preprocessing of the hospital database information focuses attention, eliminates superfluous data, and significantly reduces surgical house staff clerical work, allowing more time for education, research, and patient care.

Artificial Intelligence↗

PubMed2004

A bridged diagnostic method for the monitoring of polymorphic discrete-event systems.

Diagnosis of discrete-event systems (DESs) is a challenging problem that has been tackled both by automatic control and artificial intelligence communities. The relevant approaches share similarities, including modeling by automata, compositional modeling, and model-based reasoning. This paper aims to bridge two complementary approaches from these communities, namely, the diagnoser approach and the active system approach, respectively. The more significant shortcomings of such approaches are, on the one side, the need for the generation of the global system model and, on the other, the lack of monitoring capabilities. The former makes the application of the diagnoser approach prohibitive in real contexts, where the system model is too large to be generated, even offline. The latter requires the completion of the system observation before starting the diagnostic task, thereby, making the monitoring of the system. impossible. The bridged diagnostic method subsumes, to a large extent on the peculiarities of the two approaches and is capable of coping with an extended class of DESs that integrate both synchronous and asynchronous behavior. The bridge is built by extending the active system approach by means of several enhanced techniques, which eventually, allow the efficient monitoring of polymorphic DESs. Upon the occurrence of each system message, two pieces of diagnostic information are generated, namely, the snapshot and historic diagnostic sets. While the former accounts for the faults pertinent to the newly generated message only, the latter is based on the whole sequence of messages yielded by the system during operation.

Algorithms↗

PubMedDate not supplied

Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB.

The current paper reviews a connectionist model, the recurrent neural network with parametric biases (RNNPB), in which multiple behavior schemata can be learned by the network in a distributed manner. The parametric biases in the network play an essential role in both generating and recognizing behavior patterns. They act as a mirror system by means of self-organizing adequate memory structures. Three different robot experiments are reviewed: robot and user interactions; learning and generating different types of dynamic patterns; and linguistic-behavior binding. The hallmark of this study is explaining how self-organizing internal structures can contribute to generalization in learning, and diversity in behavior generation, in the proposed distributed representation scheme.

Artificial Intelligence↗

PubMed2003

Estimation of urban runoff and water quality using remote sensing and artificial intelligence.

Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.

Artificial Intelligence↗

PubMed1995

Toxicity estimation by chemical substructure analysis: the TOX II program.

The objectives of our work are to develop methodologies capable of identifying the potential environmental health hazards of chemicals. These techniques are particularly useful when it is necessary to evaluate molecules that have not been synthesized as yet, or for which there is little or no toxicological information known. With the help of MULTICASE, an artificial intelligence program capable of uncovering the relationship between the presence of specific substructures in a molecule and its toxicity, and TOX II, a program capable of identifying the existence of such substructures in a new molecule, it is now possible to predict with a reasonable degree of certainty whether a new molecule will be toxic. TOX II will uncover any functionality previously found to be related to toxicity in any organic molecule. The evaluation is extensive and may include its automatically generated metabolites. The scope of TOX II is vast as more than 70 toxicological endpoints can be evaluated.

Artificial Intelligence↗

PubMed1992

Applications of rule-induction in the derivation of quantitative structure-activity relationships.

Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.

Algorithms↗

PubMed2003

A generalized locomotion CPG architecture based on oscillatory building blocks.

Neural oscillation is one of the most extensively investigated topics of artificial neural networks. Scientific approaches to the functionalities of both natural and artificial intelligences are strongly related to mechanisms underlying oscillatory activities. This paper concerns itself with the assumption of the existence of central pattern generators (CPGs), which are the plausible neural architectures with oscillatory capabilities, and presents a discrete and generalized approach to the functionality of locomotor CPGs of legged animals. Based on scheduling by multiple edge reversal (SMER), a primitive and deterministic distributed algorithm, it is shown how oscillatory building block (OBB) modules can be created and, hence, how OBB-based networks can be formulated as asymmetric Hopfield-like neural networks for the generation of complex coordinated rhythmic patterns observed among pairs of biological motor neurons working during different gait patterns. It is also shown that the resulting Hopfield-like network possesses the property of reproducing the whole spectrum of different gaits intrinsic to the target locomotor CPGs. Although the new approach is not restricted to the understanding of the neurolocomotor system of any particular animal, hexapodal and quadrupedal gait patterns are chosen as illustrations given the wide interest expressed by the ongoing research in the area.

Algorithms↗

PubMed2002

Clinical potential of proteomics in the diagnosis of ovarian cancer.

The need for specific and sensitive markers of ovarian cancer is critical. Finding a sensitive and specific test for its detection has an important public health impact. Currently, there are no effective screening options available for patients with ovarian cancer. CA-125, the most widely used biomarker for ovarian cancer, does not have a high positive predictive value and it is only effective when used in combination with other diagnostic tests. However, pathologic changes taking place within the ovary may be reflected in biomarker patterns in the serum. Combination of mass spectra generated by new proteomic technologies, such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and artificial-intelligence-based informatic algorithms, have been used to discover a small set of key protein values and discriminate normal from ovarian cancer patients. Serum proteomic pattern analysis might be applied ultimately in medical screening clinics, as a supplement to the diagnostic work-up and evaluation.

Biomarkers, Tumor↗

PubMed2026

The Rise of Plant Pan-Genomes: From Genome Variation to Predictive Breeding.

Plant pan-genomics is entering a new phase beyond genome variation discovery, requiring a shift from cataloguing genomic diversity toward understanding how variation generates biological function and breeding value. Here, we propose that the future of plant pan-genomics will be shaped by three conceptual transitions. First, structural variation (SV), presence-absence variation (PAV), and haplotype diversity should be interpreted not merely as genomic differences, but as regulatory components that influence gene networks, chromatin organization, and complex traits. Second, the expansion from species-level pan-genomes to genus-level super pan-genomes provides an evolutionary framework for uncovering adaptive genetic modules preserved in wild relatives and overlooked during domestication. Third, integrating pan-genomes with pan-omics, three-dimensional genome analyses, and artificial intelligence will enable the transformation of genomic variation into predictive models for crop improvement. We further propose that the ultimate value of pan-genomes lies not in generating increasingly complete genome collections, but in establishing a mechanistic bridge between genome diversity, biological function, and breeding decisions. This transition will move crop improvement from empirical selection toward rational genome design, where evolutionary diversity can be systematically interpreted, predicted, and engineered.

Journal Article↗

PubMed1990

Knowledge-based prediction of protein structures.

We propose a knowledge-based approach to the prediction of protein structures in cases where there is no sequence-homology to proteins with known spatial structure. Using methods from Artificial Intelligence we attempt to take into account long-range interactions within the prediction process. This allows not only the assignment of secondary but also of supersecondary structure elements. In particular, the patterns used as conditions of prediction rules are generated by learning methods from information contained in the Protein Data Base. Patterns on higher levels of the protein structure hierarchy are used as constraints to reduce the combinatorial search space. These patterns may also be used to search for specified structure motifs by interactive retrieval.

Artificial Intelligence↗

PubMedDate not supplied

An artificial intelligence approach to DNA sequence feature recognition.

The ultimate goal of the Human Genome project is to extract the biologically relevant information recorded in the estimated 100,000 genes encoded by the 3 x 10(9) bases of the human genome. This necessitates development of reliable computer-based methods capable of analysing and correctly identifying genes in the vast amounts of DNA-sequence data generated. Such tools may save time and labour by simplifying, for example, screening of cDNA libraries. They may also facilitate the localization of human disease genes by identifying candidate genes in promising regions of anonymous DNA sequence.

Artificial Intelligence↗

PubMed1976

Towards the simulation of clinical cognition. Taking a present illness by computer.

Remarkably little is known about the cognitive processes which are employed in the solution of clinical problems. This paucity of information is probably accounted for in large part by the lack of suitable analytic tools for the study of the physician's thought processes. Here we report on the use of the computer as a laboratory for the study of clinical cognition. Our experimental approach has consisted of several elements. First, cognitive insights gained from the study of clinicians' behavior were used to develop a computer program designed to take the present illness of a patient with edema. The program was then tested with a series of prototypical cases, and the present illnesses generated by the computer were compared to those taken by the clinicians in our group. Discrepant behavior on the part of the program was taken as a stimulus for further refinement of the evolving cognitive theory of the present illness. Corresponding refinements were made in the program, and the process of testing and revision was continued until the program's behavior closely resembled that of the clinicians. The advances in computer science that made this effort possible include "goal-directed" programming, pattern-matching and a large associative memory, all of which are products of research in the field known as "artificial intelligence". The information used by the program is organized in a highly connected set of associations which is used to guide such activities as checking the validity of facts, generating and testing hypotheses, and constructing a coherent picture of the patient. As the program pursues its interrelated goals of information gathering and diagnosis, it uses knowledge of diseases and pathophysiology, as well as "common sense", to dynamically assemble many small problem-solving strategies into an integrated history-taking process. We suggest that the present experimental approach will facilitate accomplishment of the long-term goal of disseminating clinical expertise via the computer.

Computers↗

PubMed1996

The effects of variable biome distribution on global climate.

In projecting climatic adjustments to anthropogenically elevated atmospheric carbon dioxide, most global climate models fix biome distribution to current geographic conditions. Previous biome maps either remain unchanging or shift without taking into account climatic feedbacks such as radiation and temperature. We develop a model that examines the albedo-related effects of biome distribution on global temperature. The model was tested on historical biome changes since 1860 and the results fit both the observed temperature trend and order of magnitude change. The model is then used to generate an optimized future biome distribution that minimizes projected greenhouse effects on global temperature. Because of the complexity of this combinatorial search, an artificial intelligence method, the genetic algorithm, was employed. The method is to adjust biome areas subject to a constant global temperature and total surface area constraint. For regulating global temperature, oceans are found to dominate continental biomes. Algal beds are significant radiative levers as are other carbon intensive biomes including estuaries and tropical deciduous forests. To hold global temperature constant over the next 70 years this simulation requires that deserts decrease and forested areas increase. The effect of biome change on global temperature is revealed as a significant forecasting factor.

Algorithms↗

PubMed2026

Metal-Organic Framework-Based and Metal-Organic Framework-Derived Nanomaterials for Cancer Theranostics and Antibacterial Applications: Advances, Challenges, and Perspectives.

Metal-organic frameworks (MOFs), constructed through coordination-driven self-assembly of metal ions/clusters and organic linkers, have emerged as a uniquely versatile class of porous nanomaterials with broad biomedical potential. Despite substantial clinical progress, both oncological treatment and antimicrobial intervention remain constrained by inadequate tumor-targeting selectivity, multidrug resistance, immunosuppressive tumor microenvironments, and the global proliferation of antibiotic-resistant pathogens, limitations that conventional nanocarrier platforms have addressed only in part. MOF-based and MOF-derived nanomaterials, distinguished by tunable pore architecture, structurally and compositionally adaptable metal nodes, high surface areas, and stimulus-responsive degradability, offer a rational framework for overcoming these barriers. This review systematically examines the synthetic strategies underlying MOF-based and MOF-derived nanomaterials, including pyrolysis, chemical etching, composite modification, and functional group introduction, and their structural determinants of performance. In cancer theranostics, we critically evaluate their roles as multimodal imaging contrast agents, stimulus-responsive drug delivery carriers, and platforms for combination therapies encompassing photodynamic, photothermal, chemodynamic, and immunomodulatory modalities. In antibacterial applications, we analyze the mechanistic basis of MOF-based and MOF-derived activity, including physical membrane disruption, reactive oxygen species-mediated oxidative stress, and sustained metal ion release, alongside strategies targeting biofilm formation and antibiotic resistance. Multifunctional platforms that concurrently integrate cancer theranostic and antibacterial capabilities are further discussed. This review also addresses the principal barriers to clinical translation, encompassing large-scale manufacturing, long-term biosafety, and regulatory approval, and proposes future directions incorporating artificial intelligence-assisted design and materials genomics, underscoring the transformative potential of MOF-based and MOF-derived nanomaterials as next-generation precision nanomedicines. This review establishes a unified mechanistic framework grounded in the intrinsic physicochemical properties of MOF-derived nanomaterials, systematically integrating their applications in cancer theranostics and antibacterial therapy. Critically, it bridges fundamental advances with translational reality by incorporating a rigorous assessment of regulatory pathways, scalable manufacturing constraints, and clinical implementation barriers, and offers a comprehensive, practice-oriented reference for the rational design and responsible translation of MOF-based and MOF-derived nanomaterials.

Theranostic Nanomedicine↗