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Results for “generative artificial intelligence”

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PubMed2005

Applying informatics in tissue engineering.

OBJECTIVE: To facilitate tissue engineering strategies determination with informatics tools. METHODS: Firstly, tissue engineering experimental data were standardized and integrated into a centralized database; secondly, we used data mining tools (e.g. artificial neural networks and decision trees) to predict the outcomes of tissue engineering strategies; thirdly, a strategy design algorithm was developed, and its efficacy was validated with animal experiments; lastly, we constructed an online database and a decision support system for tissue engineering. RESULTS: The artificial neural networks and the decision trees respectively predicted the outcomes of tissue engineering strategies with the predictive accuracy of 95.14% and 85.26%. Following the strategies generated by computer, we cured 18 of the 20 experimental animals with a significantly lower cost than usual. CONCLUSION: Informatics is beneficial for realizing safe, effective and economical tissue engineering.

Artificial Intelligence↗

PubMed2002

Concept mapping: a tool to bridge the disciplinary divide.

The American College of Women's Health Physicians has been exploring an on-line educational tool-concept mapping-to facilitate the development of an interdisciplinary and woman-centered women's health curriculum, and to implement The Women's Health Care Competencies for Medical Students. By using an on-line concept map of the menstrual cycle, we have built upon a standard piece of curricula that describes a unique aspect of female physiology and transformed it into a knowledge framework that builds capacity. The concept map highlights relationships between concepts and across disciplines, connecting the competencies to enable meaningful learning so that a learner can adapt their knowledge to multiple settings, incorporate new learning, and generate new knowledge to grow the interdisciplinary field of women's health. The on-line format allows access from multiple sites and courses, and allows the curricula to grow organically over time without upsetting current curricular design.

Artificial Intelligence↗

PubMed2005

Less is more: towards an optimal universal description of protein folds.

MOTIVATION: Identification and characterization of protein structure regularities can reveal the mechanisms governing protein structure, function and evolution. Here we focus on an intermediate level of regularity. We have developed automated methods to systematically construct a dictionary of supersecondary structures that can be used as 'protein parts' to describe fold-sized structures. RESULTS: The dictionary was constructed by aligning representative structures of all known folds, clustering similar substructures and selecting the most descriptive substructures in a minimum description length fashion. We show that the dictionary is compact and descriptive, capable of describing a substantial fraction of all known protein folds. We performed simulations using independent sets of training and testing folds. Dictionaries generated using the training set had high coverage over the folds in the testing set, suggesting that dictionary entries reflect general features of protein structures and should be capable of describing novel protein folds.

Algorithms↗

PubMed2005

Automatic term list generation for entity tagging.

MOTIVATION: Many entity taggers and information extraction systems make use of lists of terms of entities such as people, places, genes or chemicals. These lists have traditionally been constructed manually. We show that distributional clustering methods which group words based on the contexts that they appear in, including neighboring words and syntactic relations extracted using a shallow parser, can be used to aid in the construction of term lists. RESULTS: Experiments on learning lists of terms and using them as part of a gene tagger on a corpus of abstracts from the scientific literature show that our automatically generated term lists significantly boost the precision of a state-of-the-art CRF-based gene tagger to a degree that is competitive with using hand curated lists and boosts recall to a degree that surpasses that of the hand-curated lists. Our results also show that these distributional clustering methods do not generate lists as helpful as those generated by supervised techniques, but that they can be used to complement supervised techniques so as to obtain better performance. AVAILABILITY: The code used in this paper is available from http://www.cis.upenn.edu/datamining/software_dist/autoterm/

Abstracting and Indexing↗

PubMed2005

STACS: new active contour scheme for cardiac MR image segmentation.

The paper presents a novel stochastic active contour scheme (STACS) for automatic image segmentation designed to overcome some of the unique challenges in cardiac MR images such as problems with low contrast, papillary muscles, and turbulent blood flow. STACS minimizes an energy functional that combines stochastic region-based and edge-based information with shape priors of the heart and local properties of the contour. The minimization algorithm solves, by the level set method, the Euler-Lagrange equation that describes the contour evolution. STACS includes an annealing schedule that balances dynamically the weight of the different terms in the energy functional. Three particularly attractive features of STACS are: 1) ability to segment images with low texture contrast by modeling stochastically the image textures; 2) robustness to initial contour and noise because of the utilization of both edge and region-based information; 3) ability to segment the heart from the chest wall and the undesired papillary muscles due to inclusion of heart shape priors. Application of STACS to a set of 48 real cardiac MR images shows that it can successfully segment the heart from its surroundings such as the chest wall and the heart structures (the left and right ventricles and the epicardium.) We compare STACS' automatically generated contours with manually-traced contours, or the "gold standard," using both area and edge similarity measures. This assessment demonstrates very good and consistent segmentation performance of STACS.

Algorithms↗

PubMed1994

Learning temporal sequences from examples in a local feedback neural network.

Statistical-mechanical techniques are used to study temporal sequence association in a local feedback neural network consisting of an input layer of context units and a single output unit. Each context unit has a feedback connection onto itself such that it accumulates an exponentially decaying moving average or trace of previous inputs to that unit. The formation of these traces allows the network to extract temporal information over an interval delta, where delta = 1/magnitude of ln gamma and gamma is the decay-rate of the moving average. The particular problem of learning a rule from examples is considered where the rule is generated by a teacher local feedback network. The replica method and other mean-field theory techniques are used to determine how the resulting generalization error of the network varies as a function of sequence length M and decay-rate gamma.

Artificial Intelligence↗

PubMed2001

Supporting medical decisions with vector decision trees.

The article presents the extension of a common decision tree concept to a multidimensional - vector - decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample. It has the functionality of many separate decision trees acting on a same set of training data and answering different questions. Vector decision tree is therefore simple in its form, is easy to use and analyse and can express some relationships between decisions not visible before. To explore and test the possibilities of this concept we developed a software tool--DecRain--for building vector decision trees using the ideas of evolutionary computing. Generated vector decision trees showed good results in comparison to classical decision trees. The concept of vector decision trees can be safely and effectively used in any decision making process.

Algorithms↗

PubMed2003

Combining medical informatics and bioinformatics toward tools for personalized medicine.

OBJECTIVES: Key bioinformatics and medical informatics research areas need to be identified to advance knowledge and understanding of disease risk factors and molecular disease pathology in the 21 st century toward new diagnoses, prognoses, and treatments. METHODS: Three high-impact informatics areas are identified: predictive medicine (to identify significant correlations within clinical data using statistical and artificial intelligence methods), along with pathway informatics and cellular simulations (that combine biological knowledge with advanced informatics to elucidate molecular disease pathology). RESULTS: Initial predictive models have been developed for a pilot study in Huntington's disease. An initial bioinformatics platform has been developed for the reconstruction and analysis of pathways, and work has begun on pathway simulation. CONCLUSIONS: A bioinformatics research program has been established at GE Global Research Center as an important technology toward next generation medical diagnostics. We anticipate that 21 st century medical research will be a combination of informatics tools with traditional biology wet lab research, and that this will translate to increased use of informatics techniques in the clinic.

Biomedical Research↗

PubMed1993

Medical diagnostic reasoning: epistemological modeling as a strategy for design of computer-based consultation programs.

The complexity of cognitive emulation of human diagnostic reasoning is the major challenge in the implementation of computer-based programs for diagnostic advice in medicine. We here present an epistemological model of diagnosis with the ultimate goal of defining a high-level language for cognitive and computational primitives. The diagnostic task proceeds through three different phases: hypotheses generation, hypotheses testing and hypotheses closure. Hypotheses generation has the inferential form of abduction (from findings to hypotheses) constrained under the criterion of plausibility. Hypotheses testing is achieved by a deductive inference (from generated hypotheses to expected findings), followed by an eliminative induction, constrained under the criterion of covering, which matches expected findings against patient's findings to select the best explanation. Hypotheses closure is a deductive-inductive type of inference very similar to the inferences operating in hypotheses testing. In this case induction matches the consequences of the generated hypotheses against the patient's characteristics or preferences under the criterion of utility. By using the language exploited in this epistemological model, it is possible to describe the cognitive tasks underlying the most influential knowledge-based diagnostic systems.

Artificial Intelligence↗

PubMed2002

A real-time EMG-driven virtual arm.

An EMG-driven virtual arm is being developed in our laboratories for the purposes of studying neuromuscular control of arm movements. The virtual arm incorporates the major muscles spanning the elbow joint and is used to estimate tension developed by individual muscles based on recorded electromyograms (EMGs). It is able to estimate joint moments and the corresponding virtual movements, which are displayed in real-time on a computer screen. In addition, the virtual arm offers artificial control over a variety of physiological and environmental conditions. The virtual arm can be used to examine how the neuromuscular system compensates for the partial or total loss of a muscle's ability to generate force as might result from trauma or pathology. The purpose of this paper is to describe the design objectives, fundamental components and implementation of our real-time, EMG-driven virtual arm.

Arm↗

PubMed1997

A comprehensive knowledge-based system for laboratory hematology.

The Coulter FACULTY knowledge-based systems, Professor Petrushka for peripheral blood interpretation, Professor Fidelio for flow cytometry immunophenotyping and Professor Belmonte for bone marrow reporting, have been installed in several hospitals in Spain, Portugal and the United Kingdom. In Spain and Portugal, the systems are part of the IZASA-Coulter CITOTECA workstation, which includes a video camera for capturing microscopic images and a networkable laboratory information system supporting color reports. At the Royal Hospitals Trust (St. Bartholomew's Hospital and The Royal London Hospital, London, UK), networked workstations are available and the system is used daily to generate bone marrow reports in the hematology laboratories. There have been considerable benefits from adopting Coulter FACULTY for bone marrow reporting, including faster turnaround time, improved quality of the reports and cost savings.

Artificial Intelligence↗

PubMed2006

A sensitivity-guided algorithm for automated determination of IMRT objective function parameters.

Optimizing intensity-modulated radiotherapy (IMRT) plans involves tradeoffs that balance normal-tissue objectives against each other and against tumor objectives. Adjusting the parameters that determine the appropriate contributions of individual anatomic structures to the objective functions through trial and error is time consuming and may not produce the best achievable plans. We have developed a sensitivity-guided parameter optimization (SGPO) method to assist in the automatic determination of parameters to drive the IMRT optimization to better achieve, or even exceed, specified planning goals. The method is based on the trade-off relationships among multiple objectives: In a globally optimal plan (or within a convex subspace of the plan objectives), any attempt to improve the achievement of goals for a structure will result in sacrificing the goals for at least one other structure. However, different objectives may have different sensitivities to the overall goal of an IMRT plan. For instance, changes in dose distribution, hence the subscore corresponding to an objective for a given normal structure, may minimally impact the target dose distribution. Stated differently, the target coverage is insensitive to the changes in dose distribution of the specific normal structure. A lung cancer treatment plan designed with the SGPO method was used to demonstrate that IMRT plans could be designed to favor a structure with the highest target sensitivity and spare the structures with the least target sensitivity without compromising the target coverage. Using one case each of prostate and paranasal sinus cancers, we also demonstrated that several alternative optimal solutions could be designed with the SGPO algorithm favoring different structures. Finally, we applied the method to eight oropharyngeal cancer cases to obtain objective function parameters that satisfied the Radiation Therapy Oncology Group RTOG-H-0022 protocol. The eight plans optimized using the computer-generated objective function parameters met the protocol's scoring criteria with no or only minor protocol violations. Our preliminary study indicates that the SGPO method may be an effective and practical way to improve IMRT planning.

Artificial Intelligence↗

PubMed1997

Supporting multi-level medical education with knowledge-based systems.

Knowledge-based systems for medicine have enjoyed minimal success in developing countries as end-user systems. The reasons for this are complex. As funding agencies understandably tend to err on the side of caution, and knowledge-based systems are still (despite an almost 40 year history) seen as a new and untried technology, few have been implemented. Of those which have, most are inappropriately simple and thus do not fit in with the real-life clinical environment. In contrast to the sophisticated systems in use in developed countries which reflect a mature technology, the use of knowledge-based systems in medicine in developing countries has primarily revolved around simple 'expert' systems, where the program functions more as a 'guru' than as a support function. We propose the more appropriate use of these systems as educational tools in medicine. In this discussion paper we describe a multi-level programme to support medical education, focusing on patient information systems involving natural language generation, decision-support systems as educational aids for primary health-care workers and model-based reasoning tools which allow exploratory learning for physicians in training. Throughout this paper we refer to Knowledge-Based Medical Education Systems as KBMES.

Artificial Intelligence↗

PubMed2026

Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.

The advent of high-throughput sequencing technologies has generated increasingly large and complex genomic datasets, necessitating analytical approaches capable of capturing high-dimensional and potentially nonlinear genetic interactions. This situation has significantly impacted the entire field of Genome-Wide Association Study (GWAS), whose primary goal is the identification of genomic traits and variants that are statistically associated with the risk of a disease. However, traditional GWAS methods may show reduced performance when applied to highly polygenic and nonlinear genetic architectures. Computational strategies from Artificial Intelligence (AI) and, in particular, from machine- and deep-learning may provide a powerful tool to overcome such limitations, especially by capturing nonlinear interactions and complex hidden regularities in large-scale data, which traditional GWAS approaches might overlook. To date, only a few approaches have been introduced and systematically assessed. In this review, we describe the main characteristics and limitations of standard statistical approaches for GWAS, the main uses of AI methods in computational genomics, and recent attempts to leverage AI strategies in GWAS. Particular attention will be devoted to key issues, such as the interpretability of methods and results, and the curse of dimensionality. More specifically, the review presents 30 methods designed to leverage AI in GWAS, as well as presenting a comprehensive set of evaluation metrics for their performance, also providing references to the most frequently used databases, and biobanks. Overall, this work may serve as a starting point for both dry- and wet-lab researchers, aiming to extract deeper insights from genomic data by moving beyond traditional linear additive assumptions, and leveraging large-scale datasets through AI-driven approaches.

Artificial intelligence↗

PubMedDate not supplied

Radiologic automated diagnosis (RAD).

RAD is a program currently being developed to interpret neuroimages. Given the clinical information usually available on the imaging request, RAD will analyze the scan directly from the data generated by the scanning machine to produce a differential diagnostic list explaining any lesions it discovers. RAD uses a computerized three-dimensional stereotaxic atlas of the nervous system as a model of normal structures in the analysis of scans.

Artificial Intelligence↗

PubMed1999

Protocol-based reasoning in diabetic patient management.

We propose a system for teleconsultation in Insulin Dependent Diabetes Mellitus (IDDM) management, accessible through the use of the net. The system is able to collect monitoring data, to analyze them through a set of tools, and to suggest a therapy adjustment in order to tackle the identified metabolic problems and to fit the patient's needs. The therapy revision has been implemented through the Episodic Skeletal Planning Methodi, it generates an advice and employs it to modify the current therapeutic protocol, presenting to the physician a set of feasible solutions, among which she can choose the new one.

Adult↗

PubMed2005

Face localization and authentication using color and depth images.

This paper presents a complete face authentication system integrating both two-dimensional (color or intensity) and three-dimensional (3-D) range data, based on a low-cost 3-D sensor, capable of real-time acquisition of 3-D and color images. Novel algorithms are proposed that exploit depth information to achieve robust face detection and localization under conditions of background clutter, occlusion, face pose alteration, and harsh illumination. The well-known embedded hidden Markov model technique for face authentication is applied to depth maps and color images. To cope with pose and illumination variations, the enrichment of face databases with synthetically generated views is proposed. The performance of the proposed authentication scheme is tested thoroughly on two distinct face databases of significant size. Experimental results demonstrate significant gains resulting from the combined use of depth and color or intensity information.

Algorithms↗

PubMedDate not supplied

Support vector machines (SVMs) for monitoring network design.

In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized error (risk) model, rather than just the mean square error over a training set. Due to Mercer's conditions on the kernels, the corresponding optimization problems are convex and hence have no local minima. In this paper, SVMs are illustratively used to reproduce the behavior of Monte Carlo-based flow and transport models that are in turn used in the design of a ground water contamination detection monitoring system. The traditional approach, which is based on solving transient transport equations for each new configuration of a conductivity field, is too time consuming in practical applications. Thus, there is a need to capture the behavior of the transport phenomenon in random media in a relatively simple manner. The objective of the exercise is to maximize the probability of detecting contaminants that exceed some regulatory standard before they reach a compliance boundary, while minimizing cost (i.e., number of monitoring wells). Application of the method at a generic site showed a rather promising performance, which leads us to believe that SVMs could be successfully employed in other areas of hydrology. The SVM was trained using 510 monitoring configuration samples generated from 200 Monte Carlo flow and transport realizations. The best configurations of well networks selected by the SVM were identical with the ones obtained from the physical model, but the reliabilities provided by the respective networks differ slightly.

Artificial Intelligence↗