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Results for “generative artificial intelligence”

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PubMed2001

The graphical presentation of decision support information in an intelligent anaesthesia monitor.

This contribution examines the graphical presentation of decision support information generated by an intelligent monitor, named SENTINEL, developed for use during anaesthesia. Clinicians make diagnoses in real-time during operations by examining clinically significant trends in multiple signals. SENTINEL attempts to mimic this decision process by using a system of fuzzy trend templates. SENTINEL's implementation of fuzzy trend templates is capable of providing the dual fuzzy measures of belief and plausibility, which are derived from the theory of evidence. It is thus capable of generating fairly rich diagnostic decision support information. However, for SENTINEL to be effective, the visual presentation of this information must be intuitive to the anaesthetist, who may not be familiar with the theory of evidence. This paper discusses techniques that are being evaluated to meet the requirements of the SENTINEL anaesthesia monitor. Specifically, the paper presents methods for highlighting clinically significant trends in physiological (or derived) signals by superimposing a coloured band on the signal that reflects fuzzy output from the intelligent monitor. This paper also discusses the intuitive graphical presentation of binary diagnostic fuzzy measures, including their further interpretation and presentation as crisp "alarm" and "warning" conditions.

Anesthesia, General↗

PubMed2000

Intelligent database generated occupational questionnaire system.

Obtaining an adequate occupational history requires special expertise to "ask the right questions" that are relevant to a particular patient's specific health conditions and potential exposures. This article describes a way to systematically accomplish this by means of a computer system that can overcome limited availability of necessary clinical occupational health expertise. The Intelligent Questionnaire system is a computer-based system for generating case-specific questionnaires about the influence of work on respiratory disease. Intelligent Questionnaire includes three databases: Questions, Responses, and Calls (clues to identify questions). The Questionnaire also arranges questions in a logical manner and provides a customized data entry screen for each subject. This approach provides primary practitioners with expertise on a case-by-case basis. It also facilitates occupational health surveillance because it allows acquiring detailed case-specific information in a systematic fashion. A computer-based system can facilitate obtaining occupational histories with high specificity and consistency without depending on general availability of a human occupational health clinical expertise.

Artificial Intelligence↗

PubMed2005

Automatic intrinsic DNA curvature computation from AFM images.

Critical information on several biological processes such as DNA-protein interactions and DNA transcription can be derived from analysis of DNA curvature. Under thermal perturbation, the curvature is composed of static and dynamic contributions, thus, can be described as the sum of intrinsic curvature and a fluctuation contribution. Without considering thermal agitations, the DNA curvature is reducible to the intrinsic component, which is a function of the DNA nucleotide sequence only. In this paper, we present an automated algorithm to determine the DNA intrinsic curvature profiles and the molecular spatial orientations in Atomic Force Microscope images. The algorithm allows to reconstruct the intrinsic curvature profile by filtering the thermal contribution. It detects fragment orientation on atomic force microscope images without labels with a percentage of correct molecular-orientation detection of 96.79% in computer-generated benchmarks, for molecules with a high curvature peak. The automated algorithm reconstructs the intrinsic curvature profile of DNA molecules with a mean square error of 3.8122 x 10(-4) rads over a profile with a central peak value of 0.196 rads, and 6.1 x 10(-3) rads over a curvature profile with two symmetric peaks of about 0.08 rads. Moreover, it correctly detects the location of the peaks in the molecules with a deviation of about 1% of molecule length.

Algorithms↗

PubMed2006

Automated search for arthritic patterns in infrared spectra of synovial fluid using adaptive wavelets and fuzzy C-means analysis.

Analysis of synovial fluid by infrared (IR) clinical chemistry requires expert interpretation and is susceptible to subjective error. The application of automated pattern recognition (APR) may enhance the utility of IR analysis. Here, we describe an APR method based on the fuzzy C-means cluster adaptive wavelet (FCMC-AW) algorithm, which consists of two parts: one is a FCMC using the features from an M-band feature extractor adopting the adaptive wavelet algorithm and the second is a Bayesian classifier using the membership matrix generated by the FCMC. A FCMC-cross-validated quadratic probability measure (FCMC-CVQPM) criterion is used under the assumption that the class probability density is equal to the value of the membership matrix. Therefore, both values of posterior probabilities and selection criterion MFQ can be obtained through the membership matrix. The distinctive advantage of this method is that it provides not only the 'hard' classification of a new pattern, but also the confidence of this classification, which is reflected by the membership matrix.

Algorithms↗

PubMed2005

General C-means clustering model.

Partitional clustering is an important part of cluster analysis. Based on various theories, numerous clustering algorithms have been developed, and new clustering algorithms continue to appear in the literature. It is known that Occam's razor plays a pivotal role in data-based models, and partitional clustering is categorized as a data-based model. However, no relation had previously been discovered between Occam's razor and partitional clustering, as we discuss in this paper. The three main contributions of this paper can be summarized as follows: 1) According to a novel definition of the mean, a unifying generative framework for partitional clustering algorithms, called a general c-means clustering model (GCM), is presented and studied. 2) Based on the local optimality test of the GCM, the connection between Occam's razor and partitional clustering is established for the first time. As its application, a comprehensive review of the existing objective function-based clustering algorithms is presented based on GCM. 3) Under a common assumption about partitional clustering, a theoretical guide for devising and implementing clustering algorithm is discovered. These conclusions are verified by numerical experimental results.

Algorithms↗

PubMed2001

Information needs of clinical teams: analysis of questions received by the Clinical Informatics Consult Service.

OBJECTIVES: To examine the types of questions received by Clinical Informatics Consult Service (CICS) librarians from clinicians on rounds and to analyze the number of clearly differentiated viewpoints provided in response. DESIGN: Questions were retrieved from an internal database, the CICS Knowledge Base, and analyzed for redundancy by subject analysis. The unique questions were classified into ten categories by subject. Treatment-related questions were analyzed for the number of viewpoints represented in the librarian's response. RESULTS: The CICS Knowledge Base contained 476 unique questions and 71 redundant questions. Among the unique queries, the top two categories accounted for 67%: treatment (36%) and disease description (31%). Within the treatment-related subset, 138 questions (59%) required representation of more than one viewpoint in the librarian's response. DISCUSSION: Questions generated by clinicians frequently require comprehensive, critical appraisal of the medical literature, a need that can be filled by librarians trained in such techniques. This study demonstrates that many questions require representation of more than one viewpoint to answer completely. Moreover, the redundancy rate underscores the need for resources like the CICS Knowledge Base. By critically analyzing the medical literature, CICS librarians are providing a time-saving and valuable service for clinicians and charting new territory for librarians.

Artificial Intelligence↗

PubMed1991

Combining physiologic models and symbolic methods to interpret time-varying patient data.

This paper describes a methodology for representing and using medical knowledge about temporal relationships to infer the presence of clinical events that evolve over time. The methodology consists of three steps: (1) the incorporation of patient observations into a generic physiologic model, (2) the conversion of model states and predictions into domain-specific temporal abstractions, and (3) the transformation of temporal abstractions into clinically meaningful descriptive text. The first step converts raw observations to underlying model concepts, the second step identifies temporal features of the fitted model that have clinical interest, and the third step replaces features represented by model parameters and predictions into concepts expressed in clinical language. We describe a program, called TOPAZ, that uses this three-step methodology. TOPAZ generates a narrative summary of the temporal events found in the electronic medical record of patients receiving cancer chemotherapy. A unique feature of TOPAZ is its use of numeric and symbolic techniques to perform different temporal reasoning tasks. Time is represented both as a continuous process and as a set of temporal intervals. These two temporal models differ in the temporal ontology they assume and in the temporal concepts they encode. Without multiple temporal models, this diversity of temporal knowledge could not be represented.

Adult↗

PubMed2000

Knowledge-based scoring function to predict protein-ligand interactions.

The development and validation of a new knowledge-based scoring function (DrugScore) to describe the binding geometry of ligands in proteins is presented. It discriminates efficiently between well-docked ligand binding modes (root-mean-square deviation <2.0 A with respect to a crystallographically determined reference complex) and those largely deviating from the native structure, e.g. generated by computer docking programs. Structural information is extracted from crystallographically determined protein-ligand complexes using ReLiBase and converted into distance-dependent pair-preferences and solvent-accessible surface (SAS) dependent singlet preferences for protein and ligand atoms. Definition of an appropriate reference state and accounting for inaccuracies inherently present in experimental data is required to achieve good predictive power. The sum of the pair preferences and the singlet preferences is calculated based on the 3D structure of protein-ligand binding modes generated by docking tools. For two test sets of 91 and 68 protein-ligand complexes, taken from the Protein Data Bank (PDB), the calculated score recognizes poses generated by FlexX deviating <2 A from the crystal structure on rank 1 in three quarters of all possible cases. Compared to FlexX, this is a substantial improvement. For ligand geometries generated by DOCK, DrugScore is superior to the "chemical scoring" implemented into this tool, while comparable results are obtained using the "energy scoring" in DOCK. None of the presently known scoring functions achieves comparable power to extract binding modes in agreement with experiment. It is fast to compute, regards implicitly solvation and entropy contributions and produces correctly the geometry of directional interactions. Small deviations in the 3D structure are tolerated and, since only contacts to non-hydrogen atoms are regarded, it is independent from assumptions of protonation states.

Artificial Intelligence↗

PubMed1991

Modeling all dialogue system participants to generate empathetic responses.

A dialogue system between an expert system and its users is described which combines two recent hypotheses. First, that the dialogue system should explicitly model both the person directly interacting with the dialogue system (the agent) and the person reasoned about by the expert system (the patient) in order to communicate meaningfully with both people. Second, that a dialogue system can model the domain-related beliefs, preferences and concerns of both its users and generate responses empathetic to both. This dialogue system is called SERUM, standing for 'System for Empathetic Responses with User Models.' SERUM generates natural-language responses about attribute values of domain objects, via three transformations. First, the system converts properties of the agent and patient, and domain knowledge, into a pragmatic objective like empathy. Second, SERUM converts the pragmatic objectives into surface structure cues like object emphasis and level of technicality. Finally, SERUM converts the surface structure cues to realize text that is natural, appropriately technical and emotionally empathetic. SERUM is demonstrated in describing tests and treatments for lung disease in AIDS patients, a sensitive domain where empathetic responses may be needed.

Algorithms↗

PubMed1989

A neural network architecture for preattentive vision.

Recent results towards development of a neural network architecture for general-purpose preattentive vision are summarized. The architecture contains two parallel subsystems, the boundary contour system (BCS) and the feature contour system (FCS), which interact together to generate a representation of form-and-color-and-depth. Emergent boundary segmentation within the BCS and featural filling-in within the FCS are herein emphasized within a monocular setting. Applications to the analysis of boundaries, textures, and smooth surfaces are described, as is a model for invariant brightness perception under variable illumination conditions. The theory shows how suitably defined parallel and hierarchical interactions overcome computational uncertainties that necessarily exist at early processing stages. Some of the psychophysical and neurophysiological data supporting the theory's predictions are mentioned.

Artificial Intelligence↗

PubMed2000

Analytical model for the effects of learning on spike count distributions.

The spike count distribution observed when recording from a variety of neurons in many different conditions has a fairly stereotypical shape, with a single mode at zero or close to a low average count, and a long, quasi-exponential tail to high counts. Such a distribution has been suggested to be the direct result of three simple facts: the firing frequency of a typical cortical neuron is close to linear in the summed input current entering the soma, above a threshold; the input current varies on several timescales, both faster and slower than the window used to count spikes; and the input distribution at any timescale can be taken to be approximately normal. The third assumption is violated by associative learning, which generates correlations between the synaptic weight vector on the dendritic tree of a neuron, and the input activity vectors it is repeatedly subject to. We show analytically that for a simple feed-forward model, the normal distribution of the slow components of the input current becomes the sum of two quasi-normal terms. The term important below threshold shifts with learning, while the term important above threshold does not shift but grows in width. These deviations from the standard distribution may be observable in appropriate recording experiments.

Action Potentials↗

PubMed2006

A control system for a flexible spine belly-dancing humanoid.

Recently, there has been a lot of interest in building anthropomorphic robots. Research on humanoid robotics has focused on the control of manipulators and walking machines. The contributions of the torso towards ordinary movements (such as walking, dancing, attracting mates, and maintaining balance) have been neglected by almost all humanoid robotic researchers. We believe that the next generation of humanoid robots will incorporate a flexible spine in the torso. To meet the challenge of controlling this kind of high-degree-of-freedom robot, a new control architecture is necessary. Inspired by the rhythmic movements commonly exhibited in lamprey locomotion as well as belly dancing, we designed a controller for a simulated belly-dancing robot using the lamprey central pattern generator. Experimental results show that the proposed lamprey central pattern generator module could potentially generate plausible output patterns, which could be used for all the possible spine motions with minimized control parameters. For instance, in the case of planar spine motions, only three input parameters are required. Using our controller, the simulated robot is able to perform complex torso movements commonly seen in belly dancing as well. Our work suggests that the proposed controller can potentially be a suitable controller for a high-degree-of-freedom, flexible spine humanoid robot. Furthermore, it allows us to gain a better understanding of belly dancing by synthesis.

Animals↗

PubMed2000

Comparing assessment of appropriateness of diagnostic tests between a human expert and an automated reminder system.

This paper describes the validation of the GRIF automated reminder system. The reminder system has been developed to influence diagnostic test ordering of General Practitioners (GPs). It generates critical comments on the basis of accepted guidelines. A retrospective random selection of 253 request forms has been taken. We compared the comments of a human expert to the comments of the reminder system. A panel of two independent reviewers judged the requested tests based on the strict interpretation of the guidelines. The sensitivity, specificity and 'predictive values' of the comments of the reminder system and the human expert were calculated using the judgement of the two reviewers as 'gold standard'.

Artificial Intelligence↗

PubMed1995

Third generation electronic medical record knowledge based perspectives.

There is a need to develop better electronic medical records. One possible solution is to put more and more 'routine' medical knowledge into systems handling medical records. In this paper, we analyze the current state-of-the-art of knowledge-based medical record handling; we mainly consider the work of Rector et al. [1]. We offer a more detailed 'four level' knowledge level model compared to the 'two level' model of Rector. The EMR of the future might be approached with a top-down method, using the above mentioned 'four level' model.

Artificial Intelligence↗

PubMed2002

Fragment generation and support vector machines for inducing SARs.

We present a new approach to the induction of SARs based on the generation of structural fragments and support vector machines (SVMs). It is tailored for bio-chemical databases, where the examples are two-dimensional descriptions of chemical compounds. The fragment generator finds all fragments (i.e. linearly connected atoms) that satisfy user-specified constraints regarding their frequency and generality. In this paper, we are querying for fragments within a minimum and a maximum frequency in the dataset. After fragment generation, we propose to apply SVMs to the problem of inducing SARs from these fragments. We conjecture that the SVMs are particularly useful in this context, as they can deal with a large number of features. Experiments in the domains of carcinogenicity and mutagenicity prediction show that the minimum and the maximum frequency queries for fragments can be answered within a reasonable time, and that the predictive accuracy obtained using these fragments is satisfactory. However, further experiments will have to confirm that this is a viable approach to inducing SARs.

Artificial Intelligence↗

PubMed1995

Knowledge-based medical image analysis and representation for integrating content definition with the radiological report.

Technology breakthroughs in high-speed, high-capacity, and high performance desk-top computers and workstations make the possibility of integrating multimedia medical data to better support clinical decision making, computer-aided education, and research not only attractive, but feasible. To systematically evaluate results from increasingly automated image segmentation it is necessary to correlate them with the expert judgments of radiologists and other clinical specialists interpreting the images. These are contained in increasingly computerized radiological reports and other related clinical records. But to make automated comparison feasible it is necessary to first ensure compatibility of the knowledge content of images with the descriptions contained in these records. Enough common vocabulary, language, and knowledge representation components must be represented on the computer, followed by automated extraction of image-content descriptions from the text, which can then be matched to the results of automated image segmentation. A knowledge-based approach to image segmentation is essential to obtain the structured image descriptions needed for matching against the expert's descriptions. We have developed a new approach to medical image analysis which helps generate such descriptions: a knowledge-based object-centered hierarchical planning method for automatically composing the image analysis processes. The problem-solving steps of specialists are represented at the knowledge level in terms of goals, tasks, and domain objects and concepts separately from the implementation level for specific representations of different image types, and generic analysis methods. This system can serve as a major functional component in incrementally building and updating a structured and integrated hybrid information system of patient data.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗

PubMed1992

Development of a knowledge base for diagnostic reasoning in cardiology.

This paper reports on a formative evaluation of the diagnostic capabilities of the Heart Failure Program, which uses a probability network and a heuristic hypothesis generator. Using 242 cardiac cases collected from discharge summaries at a tertiary care hospital, we compared the diagnoses of the program to diagnoses collected from cardiologists using the same information as was available to the program. With some adjustments to the knowledge base, the Heart Failure Program produces appropriate diagnoses about 90% of the time on this training set. The main reasons for the inappropriate diagnoses of the remaining 10% include inadequate reasoning with temporal relations between cause and effect, severity relations, and independence of acute and chronic diseases.

Artificial Intelligence↗

PubMed2005

A hybrid method for relation extraction from biomedical literature.

PURPOSE: Over recent years, there has been a growing interest in extracting entities and relations from biomedical literature. There are a vast number of systems and approaches being proposed to extract biological relations, but none of them achieves satisfactory results. These methodologies are either parsing-based or pattern-based, which are not competent to handle the grammatical complexities of biomedical texts, or too complicated to be adapted. It is well known that appositive, coordinative propositions and such grammatical structures are extremely common in biomedical texts, particularly in full texts. However, these problems are still untouched for most of researchers. METHODS: In this paper, we have proposed a new approach, which is hybrid with both shallow parsing and pattern matching, to extract relations between proteins from scientific papers of biomedical themes. In the method, appositive and coordinative structures are interpreted based on the shallow parsing analysis, with both syntactic and semantic constraints. Then long sentences are splitted into sub-ones, from which relations are extracted by a greedy pattern matching algorithm, along with automatically generated patterns. RESULTS: Our approach is experimented to extract protein-protein interactions from full biomedical texts, and has achieved an average F-score of 80% on individual verbs, and 66% on all verbs. With the help of shallow parsing analysis, pattern matching is improved remarkably. Compared with the traditional pattern matching algorithm, our approach achieves about 7% improvement of both precision and F-score. In contrast to other systems, our approach achieves performance comparable to the best. A demo system has been available at http://spies.cs.tsinghua.edu.cn.

Abstracting and Indexing↗