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Results for “artificial intelligence”

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PubMed1996

A computerized diagnostic system for the interpretation of umbilical artery blood flow velocity waveforms.

OBJECTIVE: Development of an artificial intelligent diagnostic system for the interpretation of umbilical artery blood flow velocity waveform measurements. STUDY DESIGN: Study design comprised several stages including data acquisition, image processing and analysis, training of artificial neural network and testing the predictive value of the system. The clinical material was handled in two groups. The training group consisted of 952 umbilical artery blood flow velocity waveform images of 174 normal pregnancies with normal outcome, while the testing group was composed of 138 images derived from 20 normal pregnancies with normal outcome and 68 images of 16 high risk pregnancies with poor outcome. All subjects were evaluated by Doppler ultrasonography and umbilical artery blood flow velocity waveform images were transferred to the computer environment by means of a special data acquisition system. Automated image processing and analysis were performed to derive indices such as A/B ratio, resistance index, pulse related index, area ratio of wave and angle of coincident slopes. We have used a supervised artificial neural network (back propagation learning algorithm) to develop an intelligent diagnostic system which is called the BOLU system. RESULTS: This version of the system was trained with the umbilical artery blood flow velocity waveform images of normal pregnancies. Thus, the BOLU system decides whether the tested image is normal for a given gestational week or not. The specificity and sensitivity of this system were estimated to be 98.6% and 51.5% respectively. CONCLUSION: We have developed an artificial intelligent diagnostic system for the interpretation of umbilical artery blood flow velocity waveform measurements. Waveform indices were obtained automatically by image processing and analysis. The predictive value of the system was found to be satisfactory.

Blood Flow Velocity↗

PubMed1994

Creating a knowledge base of biological research papers.

Intelligent text-oriented tools for representing and searching the biological research literature are being developed, which combine object-oriented databases with artificial intelligence techniques to create a richly structured knowledge based of Materials and Methods sections of biological research papers. A knowledge model of experimental processes, biological and chemical substances, and analytical techniques is described, based on the representation techniques of taxonomic semantic nets and knowledge frames. Two approaches to populating the knowledge base with the contents of biological research papers are described: natural language processing and an interactive knowledge definition tool.

Algorithms↗

PubMed2003

Bioscience and analytical thinking machines.

Is science being limited by the restricted vision of the analytical scientist? Adrian Stevenson from the Institute of Biotechnology at University of Cambridge argues that human 3D visualisation abilities to find patterns are inadequate and that artificial intelligence may hold the vital key to understanding information dense biological systems.

Animals↗

PubMed2000

Human nervous system function emulator.

This paper describes a modular, extensible, open-systems design for a multiprocessor network which emulates the major functions of the human nervous system. Interchangeable hardware/software components, a socketed software bus with plug-and-play capability and self diagnostics are included. The computer hardware is based on IEEE P996.1 bus cards. Its operating system utilizes IEEE 1275 standard software. Object oriented design techniques and programming are featured. A machine-independent high level script-based command language was created for this project. Neural anatomical structures which were emulated include the cortex, brainstem, cerebellum, spinal cord, autonomic and peripheral nervous systems. Motor, sensory, autoregulatory, and higher cognitive artificial intelligence, behavioral and emotional functions are provided. The author discusses how he has interfaced this emulator to machine vision, speech recognition/speech synthesis, an artificial neural network and a dexterous hand to form an android robotic platform.

Artificial Intelligence↗

PubMed1987

Computer technology: state of the art and future trends.

Computer technology and, more broadly, information technology, are bringing about a fundamental transformation in our society from an industrial economy to an information economy. A review of the short history and present state of information technology identifies two major undercurrents: the miniaturization of computer components, which has produced a millionfold increase in the complexity possible in a single chip of silicon, and the integration of four previously separate areas of information technology: computation, communication, databases and the user interface. Microelectronics, computer networks, data storage and user amenities are the basic technologies that support these four areas and stimulate their progress. Future trends in speech recognition, voice synthesis, artificial intelligence, expert systems, computational imaging and scientific workstations are also examined.

Artificial Intelligence↗

PubMedDate not supplied

Using new reasoning technology in chemical information systems.

Unreliability of numerical data causes difficulties in computer systems for decision-making, risk assessment, and similar activities. Much human judgment is non-numerical and able to make useful evaluations of alternatives under uncertainty. The Logic of Argumentation (LA) offers a basis for computerized support of decision-making in the absence of numerical data, and it is being used in a project on carcinogenic risk assessment, StAR. There are potential applications of LA in other artificial intelligence systems in chemistry, such as for synthesis planning.

Artificial Intelligence↗

PubMed1989

Construction of the diagnostic encyclopedia workstation. Computerizing pathology for the pathologist.

The combination of a personal computer and a laser-vision disc player is an adroit tool for storing and retrieving textual information in combination with pictorial information. This paper describes such a system (a "diagnostic encyclopedia workstation"), which provides information to the pathologist engaged in daily diagnostic practice. The system contains a considerable amount of descriptive textual information that might be useful in making diagnoses in histopathology, along with many illustrations; the text is presented in a natural language (English). The descriptive information is divided into 18 categories, including such topics as histology, macroscopy, immunopathology and clinical data; each topic has a separate display in the system. Also present are two types of "decision rules" for making diagnoses (confirmative criteria for a diagnosis under consideration and exclusive criteria for many other diagnoses) and a classifying structure. The present version of this system contains information on about 100 diagnoses in ovarian pathology, illustrated with about 3,000 color slides from about 140 cases. The information and pictures are immediately available. Further characteristics of this system are its flexibility, accessibility and user friendliness; it has been structured so that components of artificial intelligence may be added later.

Artificial Intelligence↗

PubMed2007

How to make an autonomous robot as a partner with humans: design approach versus emergent approach.

In this paper, we discuss what factors are important to realize an autonomous robot as a partner with humans. We believe that it is important to interact with people without boring them, using verbal and non-verbal communication channels. We have already developed autonomous robots such as AIBO and QRIO, whose behaviours are manually programmed and designed. We realized, however, that this design approach has limitations; therefore we propose a new approach, intelligence dynamics, where interacting in a real-world environment using embodiment is considered very important. There are pioneering works related to this approach from brain science, cognitive science, robotics and artificial intelligence. We assert that it is important to study the emergence of entire sets of autonomous behaviours and present our approach towards this goal.

Artificial Intelligence↗

PubMed2000

An intelligent diabetes software prototype: predicting blood glucose levels and recommending regimen changes.

Maintaining optimal blood glucose (BG) control is difficult for type 1 diabetes mellitus (T1DM) patients when typical daily regimens of food, insulin and exercise are altered. Artificial intelligence (AI) systems consisting of treatment algorithms calibrated through large datasets of patient specific information may offer a solution. Such a system can predict BG level changes resulting from regimen disturbances and recommend regimen changes for compensation. A software prototype based on neural network, fuzzy logic, and expert system concepts was developed and evaluated to determine feasibility and efficacy of a patient specific prediction model. BG data are the primary driver for adapting existing functions to patient specific prediction algorithms. Mean absolute percent error (MAPE) between actual and predicted BG values from inputs of daily insulin, food, and exercise information for an T1DM test subject was 10.5% using a calibrated model. The prototype is limited by the requirement for a rigid testing schedule, human error and situational circumstances such as alcohol consumption, illness, infection, stress, and significant hormonal imbalances. No significant conclusions regarding model validity can be drawn due to limited evaluation process and subject sample size, although the prototype has demonstrated viability as a learning tool for diabetes patients. Increased impetus for further development of this prototype and similar AI models may materialize when more effective diagnostic and data capture tools become available to reduce testing and improve accuracy of the model with more input data.

Algorithms↗

PubMedDate not supplied

Human meaning of medical informatics: reflections on its future and trends.

The philosophy underlying medical informatics, and indeed information systems in general, is discussed. The need for integrating concepts is considered, and particular emphasis is placed on the avoidance of fragmentation and overspecialization. Human and artificial intelligence are compared and contrasted. It is shown that human intellectual activity cannot be reduced to a set of formal computations. The main emphasis of this paper is that the unique properties of human intelligence should not be devalued or ignored in attempts to promote machine systems in unappropriate areas.

Artificial Intelligence↗

PubMed2004

Dynamic ion channel activation scheduling in patch clamp on a chip.

In 2002, Fertig et al. made a remarkable invention: the first successful demonstration of a patch clamp on a chip--a planar quartz-based biological chip that contains up to several hundred ion channels. This patch-clamp chip can be used in massively parallel screens for ion channel activity, thereby providing a high-throughput screening tool for drug discovery efforts. In this paper, we propose computationally efficient dynamic stochastic scheduling algorithms for activating individual ion channels in the patch-clamp chip. By formulating the ion channel activation scheduling problem as a partially observed Markov decision process with a multiarmed bandit structure, near-optimal dynamic scheduling for activation of the individual channels is achieved to optimize the information gained from the patch-clamp chip. Numerical examples using state-of-the-art algorithms developed recently in artificial intelligence and operations research are presented to illustrate these dynamic ion channel (macromolecule) activation scheduling algorithms.

Algorithms↗

PubMed2001

Machine learning for medical diagnosis: history, state of the art and perspective.

The paper provides an overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence. The paper is not intended to provide a comprehensive overview but rather describes some subareas and directions which from my personal point of view seem to be important for applying machine learning in medical diagnosis. In the historical overview, I emphasize the naive Bayesian classifier, neural networks and decision trees. I present a comparison of some state-of-the-art systems, representatives from each branch of machine learning, when applied to several medical diagnostic tasks. The future trends are illustrated by two case studies. The first describes a recently developed method for dealing with reliability of decisions of classifiers, which seems to be promising for intelligent data analysis in medicine. The second describes an approach to using machine learning in order to verify some unexplained phenomena from complementary medicine, which is not (yet) approved by the orthodox medical community but could in the future play an important role in overall medical diagnosis and treatment.

Artificial Intelligence↗

PubMed1999

An evaluation of intelligent prognostic systems for colorectal cancer.

In this paper we describe attempts at building a robust model for predicting the length of survival of patients with colorectal cancer. The aim of the research, reported in this paper, is to study the effective utilisation of artificial intelligence techniques in the medical domain. We suggest that an important research objective of proponents of intelligent prognostic systems must be to evaluate the additionality that AI techniques can bring to an already well-established field of medical prognosis. Towards this end, we compare a number of different AI techniques that lend themselves to the task of predicting survival in colorectal cancer patients. We describe the pros and cons of each of these methods using the usual metrics of accuracy and perspicuity. We then present the notion of intelligent hybrid systems and evaluate the role that they may potentially play in developing robust prognostic models. In particular we evaluate a hybrid system that utilises the k Nearest Neighbour technique in conjunction with Genetic Algorithms. We describe a number of innovations used within this hybrid paradigm used to build the prognostic model. We discuss the issue of censored patients and how this issue can be tackled within the various models used. In keeping with our objective of studying the additionality that AI techniques bring to building prognostic models, we use Cox's regression as a standard and compare each AI technique with it, attempting to discover their capabilities in enhancing prognostic methods in medicine. In doing so we address two main questions--which model fits the data best?, and are the results obtained by the various AI techniques significantly different from those of Cox's regression? We conclude this paper by discussing future enhancements to the work presented and lessons learned from the study to date.

Age Factors↗

PubMed1999

RVG-ui: a sensor to rival direct-exposure intra-oral x-ray film.

The RVG-ui is a solid-state system for dental x-ray imaging combining a charge-coupled device (CCD) of small pixel size (19.5 microns) with a caesium iodide (CsI) scintillator. It features two sizes of sensor with receptive areas that approach the size of No. 1 and No. 2 periapical x-ray films. The spatial resolution of the RVG-ui exceeds 20 lp/mm, rivaling direct-exposure intra-oral x-ray film when the latter is optimally exposed and processed. The recording latitude of the RVG-ui for clinically acceptable images is 25:1, making exposure error unlikely. In features such as speed of image acquisition, reduction in radiation dose, retrieval of data, and organization and storage of images, the RVG-ui system surpasses traditional x-ray film. Furthermore, it is possible to make accurate length measurements very quickly, and to apply an artificial intelligence program to determine the probability of proximal dental caries in enamel and dentin.

Artificial Intelligence↗

PubMed2005

Modeling human behaviors and reactions under dangerous environment.

This paper describes the framework of a real-time simulation system to model human behavior and reactions in dangerous environments. The system utilizes the latest 3D computer animation techniques, combined with artificial intelligence, robotics and psychology, to model human behavior, reactions and decision making under expected/unexpected dangers in real-time in virtual environments. The development of the system includes: classification on the conscious/subconscious behaviors and reactions of different people; capturing different motion postures by the Eagle Digital System; establishing 3D character animation models; establishing 3D models for the scene; planning the scenario and the contents; and programming within Virtools Dev. Programming within Virtools Dev is subdivided into modeling dangerous events, modeling character's perceptions, modeling character's decision making, modeling character's movements, modeling character's interaction with environment and setting up the virtual cameras. The real-time simulation of human reactions in hazardous environments is invaluable in military defense, fire escape, rescue operation planning, traffic safety studies, and safety planning in chemical factories, the design of buildings, airplanes, ships and trains. Currently, human motion modeling can be realized through established technology, whereas to integrate perception and intelligence into virtual human's motion is still a huge undertaking. The challenges here are the synchronization of motion and intelligence, the accurate modeling of human's vision, smell, touch and hearing, the diversity and effects of emotion and personality in decision making. There are three types of software platforms which could be employed to realize the motion and intelligence within one system, and their advantages and disadvantages are discussed.

Artificial Intelligence↗

PubMed2000

An intelligent emissions controller for fuel lean gas reburn in coal-fired power plants.

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NOx) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NOx reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach. Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NOx emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.

Air Pollutants, Occupational↗

PubMed1997

Optimisation in radiotherapy. II: Programmed and inversion optimisation algorithms.

This is the second article in a three part examination of optimisation in radiotherapy. The previous article established the bases of optimisation in radiotherapy, and the formulation of the optimisation problem. This paper outlines several algorithms that have been used in radiotherapy, for searching for the best irradiation strategy within the full set of possible strategies. Two principle classes of algorithm are considered--those associated with mathematical programming which employ specific search techniques, linear programming-type searches or artificial intelligence--and those which seek to perform a numerical inversion of the optimisation problem, finishing with deterministic iterative inversion.

Algorithms↗

PubMed1997

Emergent models of supple dynamics in life and mind.

The dynamical patterns in mental phenomena have a characteristic suppleness--a looseness or softness that persistently resists precise formulation--which apparently underlies the frame problem of artificial intelligence. This suppleness also undermines contemporary philosophical functionalist attempts to define mental capacities. Living systems display an analogous from of supple dynamics. However, the supple dynamics of living systems have been captured in recent artificial life models, due to the emergent architecture of those models. This suggests that analogous emergent models might be able to explain supple dynamics of mental phenomena. These emergent models of the supple mind, if successful, would refashion the nature of contemporary functionalism in the philosophy of mind.

Animals↗