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Results for “generative artificial intelligence”

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PubMed2001

A clustering method based on rough sets and its application to knowledge discovery in the medical database.

This paper proposes a clustering method for nominal and numerical data based on Rough Sets and its application to knowledge discovery in the medical database. Classification is performed according to the indiscernibility relations defined on the basis of relative similarity between objects. The similarity is defined as a combination of two types of similarity measures: the Hamming distance for nominal attributes and the Mahalanobis distance for numerical attributes. Excessive generation of small category is suppressed by modifying similar equivalence relations into the same equivalence relation. An analysis of the meningoencephalitis diagnosis database was performed to validate this method. The result showed that this method could deal well with both types of attributes and discover the primary factors for diagnosis.

Artificial Intelligence↗

PubMed1993

Knowledge reuse: temporal-abstraction mechanisms for the assessment of children's growth.

Currently, many workers in the field of medical informatics realize the importance of knowledge reuse. The PROTEGE-II project seeks to develop and implement a domain-independent framework that allows system builders to create custom-tailored role-limiting methods from generic reusable components. These new role-limiting methods are used to create domain- and task-specific knowledge-acquisition tools with which an application expert can generate domain- and task-specific decision-support systems. One required set of reusable components embodies the problem-solving knowledge to generate temporal abstractions. Previously, members of the PROTEGE-II project have used these temporal-abstraction mechanisms to infer the presence of myelotoxicity in patients with AIDS. In this paper, we show that these mechanisms are reusable in the domain of assessment of children's growth.

Artificial Intelligence↗

PubMedDate not supplied

Development of proteomic patterns for detecting lung cancer.

Lung cancer is at present the number one cause of cancer death and no biomarker is available to detect early lung cancer in serum samples so far. The objective of this study is to find specific biomarkers for detection of lung cancer using Surface Enhanced Laser Desorption/Ionization (SELDI) technology. In this study, serum samples from 30 lung cancer patients and 51 age-and sex-matched healthy were analyzed by SELDI based ProteinChip reader, PBSII-C. The spectra were generated on WCX2 chips and protein peaks clustering and classification analyses were performed utilizing Biomarker Wizard and Biomarker Patterns software packages, respectively. Three protein peaks were automatically chosen for the system training and the development of a decision classification tree. The constructed model was then used to test an independent set of masked serum samples from 15 lung cancer patients and 31 healthy individuals. The analysis yielded a sensitivity of 93.3%, and a specificity of 96.7%. These results suggest that the serum is a capable resource for detection of specific lung cancer biomarkers. SELDI technique combined with an artificial intelligence classification algorithm can both facilitate the discovery of better biomarkers for lung cancer and provide a useful tool for molecular diagnosis in future.

Adenocarcinoma↗

PubMed2003

Back-propagation learning of infinite-dimensional dynamical systems.

This paper presents numerical studies of applying back-propagation learning to a delayed recurrent neural network (DRNN). The DRNN is a continuous-time recurrent neural network having time delayed feedbacks and the back-propagation learning is to teach spatio-temporal dynamics to the DRNN. Since the time-delays make the dynamics of the DRNN infinite-dimensional, the learning algorithm and the learning capability of the DRNN are different from those of the ordinary recurrent neural network (ORNN) having no time-delays. First, two types of learning algorithms are developed for a class of DRNNs. Then, using chaotic signals generated from the Mackey-Glass equation and the Rössler equations, learning capability of the DRNN is examined. Comparing the learning algorithms, learning capability, and robustness against noise of the DRNN with those of the ORNN and time delay neural network, advantages as well as disadvantages of the DRNN are investigated.

Algorithms↗

PubMed1998

Disease diagnosis validation in TROPIX using CBR.

TROPIX is a practical application project initially designed to help improve health care delivery in the rural/semi urban clinics and public hospitals in Nigeria due largely to limited laboratory facilities, medical doctors, and expertise. This paper is devoted to the use of case-based reasoning (CBR) paradigm in concert with statistical association-based reasoning (ABR) for disease diagnosis, validation and therapy selection components of the research. Essentially, tentative disease diagnosis arrived at by some classification method using similarity and dissimilarity aggregate functions, the matched vector functions (MVF), aided by the application of evidence ratio factors (ERF) for tied match cases is passed to the CBR model for validation by reusing past similar cases. The design and organization of the case-library using singular value decomposition (SVD) technique on the disease-attribute decision matrix to generate primary/secondary storage key clusters, as well as the use of domain-specific case-object properties that help to build a good case-base are described in some detail. The paper presents a disease case validation algorithm for appropriate data filtering and therapy selection enhancement from the new case-base.

Artificial Intelligence↗

PubMed2006

Real-time range acquisition by adaptive structured light.

The goal of this paper is to provide a "self-adaptive" system for real-time range acquisition. Reconstructions are based on a single frame structured light illumination. Instead of using generic, static coding that is supposed to work under all circumstances, system adaptation is proposed. This occurs on-the-fly and renders the system more robust against instant scene variability and creates suitable patterns at startup. A continuous trade-off between speed and quality is made. A weighted combination of different coding cues--based upon pattern color, geometry, and tracking--yields a robust way to solve the correspondence problem. The individual coding cues are automatically adapted within a considered family of patterns. The weights to combine them are based on the average consistency with the result within a small time-window. The integration itself is done by reformulating the problem as a graph cut. Also, the camera-projector configuration is taken into account for generating the projection patterns. The correctness of the range maps is not guaranteed, but an estimation of the uncertainty is provided for each part of the reconstruction. Our prototype is implemented using unmodified consumer hardware only and, therefore, is cheap. Frame rates vary between 10 and 25 fps, dependent on scene complexity.

Algorithms↗

PubMed2006

A genetic algorithm using hyper-quadtrees for low-dimensional K-means clustering.

The k-means algorithm is widely used for clustering because of its computational efficiency. Given n points in d-dimensional space and the number of desired clusters k, k-means seeks a set of k cluster centers so as to minimize the sum of the squared Euclidean distance between each point and its nearest cluster center. However, the algorithm is very sensitive to the initial selection of centers and is likely to converge to partitions that are significantly inferior to the global optimum. We present a genetic algorithm (GA) for evolving centers in the k-means algorithm that simultaneously identifies good partitions for a range of values around a specified k. The set of centers is represented using a hyper-quadtree constructed on the data. This representation is exploited in our GA to generate an initial population of good centers and to support a novel crossover operation that selectively passes good subsets of neighboring centers from parents to offspring by swapping subtrees. Experimental results indicate that our GA finds the global optimum for data sets with known optima and finds good solutions for large simulated data sets.

Algorithms↗

PubMedDate not supplied

Expressive facial animation synthesis by learning speech coarticulation and expression spaces.

Synthesizing expressive facial animation is a very challenging topic within the graphics community. In this paper, we present an expressive facial animation synthesis system enabled by automated learning from facial motion capture data. Accurate 3D motions of the markers on the face of a human subject are captured while he/she recites a predesigned corpus, with specific spoken and visual expressions. We present a novel motion capture mining technique that "learns" speech coarticulation models for diphones and triphones from the recorded data. A Phoneme-Independent Expression Eigenspace (PIEES) that encloses the dynamic expression signals is constructed by motion signal processing (phoneme-based time-warping and subtraction) and Principal Component Analysis (PCA) reduction. New expressive facial animations are synthesized as follows: First, the learned coarticulation models are concatenated to synthesize neutral visual speech according to novel speech input, then a texture-synthesis-based approach is used to generate a novel dynamic expression signal from the PIEES model, and finally the synthesized expression signal is blended with the synthesized neutral visual speech to create the final expressive facial animation. Our experiments demonstrate that the system can effectively synthesize realistic expressive facial animation.

Artificial Intelligence↗

PubMed2004

Reduction of false positives on the rectal tube in computer-aided detection for CT colonography.

PURPOSE: To eliminate false-positive (FP) polyp detections on the rectal tube (RT) in CT colonography (CTC) computer-aided detection (CAD). METHODS: We use a three-stage approach to detect the RT: detect the RT shaft, track the tube to the tip and label all the voxels that belong to the RT. We applied our RT detection algorithm on a CTC dataset consisting of 80 datasets (40 patients scanned in both prone and supine positions). Two different types of RTs were present, characterized by differences in shaft/bulb diameters, wall intensities, and shape of tip. RESULTS: The algorithm detected 90% of RT shafts and completely tracked 72% of them. We labeled all the voxels belonging to the completely tracked RTs (72%) and in 11 out of 80 (14%) cases the RT voxels were partially labeled. We obtained a 9.2% reduction of the FPs in the initial polyp candidates' population, and a 7.9% reduction of the FPs generated by our CAD system. None of the true-positive detections were mislabeled. CONCLUSIONS: The algorithm detects the RTs with good accuracy, is robust with respect to the two different types of RT used in our study, and is effective at reducing the number of RT FPs reported by our CAD system.

Algorithms↗

PubMedDate not supplied

A UMLS-based knowledge acquisition tool for rule-based clinical decision support system development.

Decision support systems in the medical field have to be easily modified by medical experts themselves. The authors have designed a knowledge acquisition tool to facilitate the creation and maintenance of a knowledge base by the domain expert and its sharing and reuse by other institutions. The Unified Medical Language System (UMLS) contains the domain entities and constitutes the relations repository from which the expert builds, through a specific browser, the explicit domain ontology. The expert is then guided in creating the knowledge base according to the pre-established domain ontology and condition-action rule templates that are well adapted to several clinical decision-making processes. Corresponding medical logic modules are eventually generated. The application of this knowledge acquisition tool to the construction of a decision support system in blood transfusion demonstrates the value of such a pragmatic methodology for the design of rule-based clinical systems that rely on the highly progressive knowledge embedded in hospital information systems.

Artificial Intelligence↗

PubMed2005

Application of string kernels in protein sequence classification.

INTRODUCTION: The production of biological information has become much greater than its consumption. The key issue now is how to organise and manage the huge amount of novel information to facilitate access to this useful and important biological information. One core problem in classifying biological information is the annotation of new protein sequences with structural and functional features. METHOD: This article introduces the application of string kernels in classifying protein sequences into homogeneous families. A string kernel approach used in conjunction with support vector machines has been shown to achieve good performance in text categorisation tasks. We evaluated and analysed the performance of this approach, and we present experimental results on three selected families from the SCOP (Structural Classification of Proteins) database. We then compared the overall performance of this method with the existing protein classification methods on benchmark SCOP datasets. RESULTS: According to the F1 performance measure and the rate of false positive (RFP) measure, the string kernel method performs well in classifying protein sequences. The method outperformed all the generative-based methods and is comparable with the SVM-Fisher method. DISCUSSION: Although the string kernel approach makes no use of prior biological knowledge, it still captures sufficient biological information to enable it to outperform some of the state-of-the-art methods.

Algorithms↗

PubMed1996

A knowledge-based model construction approach to medical decision making.

We present a framework for representing the probabilistic effects of actions and contingent treatment plans. Our language has a well-defined declarative semantics and we have developed an implemented algorithm (named BNG) that generates Bayesian networks (BN) to compute the posterior probabilities of queries. In this paper we address the problem of projecting a contingent treatment plan by automatically constructing a structure of interrelated BNs, which we call a BN-graph, and applying the available propagation procedures on it. To address the optimal plan generation, we base our approach on the observation that normally the target plan space has a well-defined structure. We provide a language to describe plan spaces which resembles a programming language with loops and conditionals. We briefly present the procedures for finding the optimal plan(s) from such specified plan spaces.

Acute Disease↗

PubMed2026

Reimagining research papers as interactive and reliable AI agents.

Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper's code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent's effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.

Journal Article↗

PubMed2005

A segmentation and reconstruction technique for 3D vascular structures.

In the context of stroke therapy simulation, a method for the segmentation and reconstruction of human vasculature is presented and evaluated. Based on CTA scans, semi-automatic tools have been developed to reduce dataset noise, to segment using active contours, to extract the skeleton, to estimate the vessel radii and to reconstruct the associated surface. The robustness and accuracy of our technique are evaluated on a vascular phantom scanned in different orientations. The reconstructed surface is compared to a surface generated by marching cubes followed by decimation and smoothing. Experiments show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. The reconstructed surface is suitable for real-time simulation, interactive navigation and visualization.

Algorithms↗

PubMed2005

A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients.

We developed a Bayesian network coding scheme for annotating biomedical content in layperson-oriented clinical genetics documents. The coding scheme supports the representation of probabilistic and causal relationships among concepts in this domain, at a high enough level of abstraction to capture commonalities among genetic processes and their relationship to health. We are using the coding scheme to annotate a corpus of genetic counseling patient letters as part of the requirements analysis and knowledge acquisition phase of a natural language generation project. This paper describes the coding scheme and presents an evaluation of intercoder reliability for its tag set. In addition to giving examples of use of the coding scheme for analysis of discourse and linguistic features in this genre, we suggest other uses for it in analysis of layperson-oriented text and dialogue in medical communication.

Artificial Intelligence↗

PubMed2003

Learning to generate articulated behavior through the bottom-up and the top-down interaction processes.

A novel hierarchical neural network architecture for sensory-motor learning and behavior generation is proposed. Two levels of forward model neural networks are operated on different time scales while parametric interactions are allowed between the two network levels in the bottom-up and top-down directions. The models are examined through experiments of behavior learning and generation using a real robot arm equipped with a vision system. The results of the learning experiments showed that the behavioral patterns are learned by self-organizing the behavioral primitives in the lower level and combining the primitives sequentially in the higher level. The results contrast with prior work by Pawelzik et al. [Neural Comput. 8 (1996) 340], Tani and Nolfi [From animals to animats, 1998], and Wolpert and Kawato [Neural Networks 11 (1998) 1317] in that the primitives are represented in a distributed manner in the network in the present scheme whereas, in the prior work, the primitives were localized in specific modules in the network. Further experiments of on-line planning showed that the behavior could be generated robustly against a background of real world noise while the behavior plans could be modified flexibly in response to changes in the environment. It is concluded that the interaction between the bottom-up process of recalling the past and the top-down process of predicting the future enables both robust and flexible situated behavior.

Algorithms↗

PubMed1997

Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries.

Machine learning techniques can be used to extract knowledge from data stored in medical databases. In our application, various machine learning algorithms were used to extract diagnostic knowledge which may be used to support the diagnosis of sport injuries. The applied methods include variants of the Assistant algorithm for top-down induction of decision trees, and variants of the Bayesian classifier. The available dataset was insufficient for reliable diagnosis of all sport injuries considered by the system. Consequently, expert-defined diagnostic rules were added and used as pre-classifiers or as generators of additional training instances for diagnoses for which only few training examples were available. Experimental results show that the classification accuracy and the explanation capability of the naive Bayesian classifier with the fuzzy discretization of numerical attributes were superior to other methods and estimated as the most appropriate for practical use.

Artificial Intelligence↗

PubMed2006

Multisurface proximal support vector machine classification via generalized eigenvalues.

A new approach to support vector machine (SVM) classification is proposed wherein each of two data sets are proximal to one of two distinct planes that are not parallel to each other. Each plane is generated such that it is closest to one of the two data sets and as far as possible from the other data set. Each of the two nonparallel proximal planes is obtained by a single MATLAB command as the eigenvector corresponding to a smallest eigenvalue of a generalized eigenvalue problem. Classification by proximity to two distinct nonlinear surfaces generated by a nonlinear kernel also leads to two simple generalized eigenvalue problems. The effectiveness of the proposed method is demonstrated by tests on simple examples as well as on a number of public data sets. These examples show the advantages of the proposed approach in both computation time and test set correctness.

Algorithms↗