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At least 829 records · Page 46Linked to original sources
PubMed2006

Intelligent inferencing and haptic simulation for Chinese acupuncture learning and training.

This paper presents an intelligent virtual environment for Chinese acupuncture learning and training using state-of-the-art virtual reality technology. It is the first step toward developing a comprehensive virtual human model for studying Chinese medicine. Students can learn and practice acupuncture in the proposed 3-D interactive virtual environment that supports a force feedback interface for needle insertion. Thus, students not only "see" but also "touch" the virtual patient. With high performance computers, highly informative and flexible visualization of acupuncture points of various related meridian and collateral can be highlighted to guide the students during training. A computer-based expert system using our newly proposed intelligent fuzzy petri net is designed and implemented to train the students to treat different diseases using acupuncture. Such an intelligent virtual reality system can provide an interesting and effective learning environment for Chinese acupuncture.

Acupuncture↗
PubMedDate not supplied

Automated rule-based decision systems in forensic toxicology using expert knowledge: basic principles and practical applications.

This paper presents the basic principles and practical benefits of the application of expert systems (ES) and artificial intelligence (AI) to problem solving in forensic toxicology. We acknowledge the complexity and elegance of the theoretical substance and program algorithms of existing work in these disciplines, while simultaneously observing that many presentations of this material cloak the essential facts and concepts in unnecessary jargon and hyperbole. We attempt to remove the cloak without misrepresenting or oversimplifying the underlying structures. We first present a summary of the history, basic functions, technical fundamentals, and typical applications in three major categories of established ES/AI systems. We then assess the status of ES/AI in the forensic toxicology laboratory (FTL) with emphasis on potential applications. We conclude with an analysis of experiences with ESs in our laboratory where we have used an integrated expert system to reduce laboratory errors, detect internal inconsistencies in data, discover new substance abuse subpopulations, and reduce the frequency of sample reprocessing. We have minimized specimen processing time and instrument wear while maximizing technician efficiency and thus performing more tests for the same or reduced costs.

Artificial Intelligence↗
PubMedDate not supplied

Computerized detection of breast cancer with artificial intelligence and thermograms.

This paper shows the concurrent use of thermography and artificial neural networks (ANN) for the diagnosis of breast cancer, a disease that is growing in prominence in women all over the world. It has been reported that breast thermography itself could detect breast cancer up to 10 years earlier than the conventional golden methods such as mammography, in particular in the younger patient. However, the accuracy of thermography is dependent on many factors such as the symmetry of the breasts' temperature and temperature stability. A woman's body temperature is known to be stable in certain periods after menstruation and it was found that the accuracy of thermography in women whose thermal images are taken in a suitable period (5th - 12th and 21st day of menstruation) is higher (80%) than the total population of patients (73%). The stability of the body temperature will depend on physiological state. This paper examines the use of ANN to complement the infrared heat radiating from the surface of the body with other physiological data. Four backpropagation neural networks were developed and trained using the results from the Singapore General Hospital patients' physiological data and thermographs. Owing to the inaccuracies found in thermography and the low population size gathered for this project, the networks developed could only accurately diagnose about 61.54% of the breast cancer cases. Nevertheless, the basic neural network framework has been established and it has great potential for future development of an intelligent breast cancer diagnosis system. This would be especially useful to the teenagers and young adults who are unsuitable for mammography at a young age. An intelligent breast thermography-neural network will be able to give an accurate diagnosis of breast cancer and can make a positive impact on breast disease detection.

Adult↗
PubMed1992

Description of a computerized adverse drug event monitor using a hospital information system.

To improve the detection and characterization of adverse drug events (ADEs) in hospitalized patients, a computerized adverse drug event monitor was developed. Computer programs were written to allow for voluntary as well as automated detection of adverse drug events using the HELP hospital information system, a large integrated hospital database containing computerized patient medical records and a knowledge base allowing for automated medical decisions. Programs were created to allow simple computer entry of potential adverse drug events by physicians, pharmacists, and nurses. Automated detection of potential adverse drug events relied on signals such as sudden medication stop orders, "antidote" orders, and selected abnormal laboratory values. Each day a list of all potential adverse drug events from these sources was generated and a pharmacist reviewed the medical records and interviewed healthcare personnel associated with patients identified as having potential adverse drug events. This process allowed for characterization of the event, causality assessment, and follow-up of the resulting clinical course by the pharmacist. The permanent storage of these results in the computerized patient medical record permits their future retrieval to prevent adverse drug events during subsequent hospital care. The authors conclude that fully integrated hospital systems will permit the further development and evaluation of computer-assisted methods for the detection of adverse drug events in hospitalized patients.

Adverse Drug Reaction Reporting Systems↗
PubMed2003

A general architecture for intelligent tutoring of diagnostic classification problem solving.

We report on a general architecture for creating knowledge-based medical training systems to teach diagnostic classification problem solving. The approach is informed by our previous work describing the development of expertise in classification problem solving in Pathology. The architecture envelops the traditional Intelligent Tutoring System design within the Unified Problem-solving Method description Language (UPML) architecture, supporting component modularity and reuse. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model. We outline the advantages and limitations of this general approach, and describe it's implementation in SlideTutor - a developing Intelligent Tutoring System in Dermatopathology.

Artificial Intelligence↗
PubMed1995

Use of data abstraction methods to simplify monitoring.

I describe the Temporal Control System (TCS), a programming system designed for building intelligent temporal monitoring programs. The ICU data set provided as part of the 1994 AAAI Spring Symposium challenge is used to conduct several experiments. Empirical results from the ICU data set validate the scalable design of the TCS. The remaining experiments examine the computational problem of generating interval values from sample points through persistent assumptions. Using abstractions in combination with persistence assumptions makes the design of higher-level clinical reasoning programs simpler. Abstraction can be used to suppress clinically unimportant details, allowing an expert system to focus on the key information provided by clinical monitors. The TCS provides the framework for the implementation as well as a method of calculating the 'cost' of different approaches. To prevent the use of outdated information, it is often useful to limit the time span of a persistent interval. I show that such limitations can be very costly computationally and then show how the application of symbolic abstraction can help. Further performance improvements from switching from continuous to discrete step persistence are shown. These performance enhancing techniques have general applicability.

Abstracting and Indexing↗
PubMed1995

Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: application of PROTEGE-II to protocol-based decision support.

PROTEGE-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTEGE-II can be applied to the task of providing protocol-based decision support in the domain of treating HIV-infected patients. To apply PROTEGE-II, (1) we construct a decomposable problem-solving method called episodic skeletal-plan refinement, (2) we build an application ontology that consists of the terms and relations in the domain, and of method-specific distinctions not already captured in the domain terms, and (3) we specify mapping relations that link terms from the application ontology to the domain-independent terms used in the problem-solving method. From the application ontology, we automatically generate a domain-specific knowledge-acquisition tool that is custom-tailored for the application. The knowledge-acquisition tool is used for the creation and maintenance of domain knowledge used by the problem-solving method. The general goal of the PROTEGE-II approach is to produce systems and components that are reusable and easily maintained. This is the rationale for constructing ontologies and problem-solving methods that can be composed from a set of smaller-grained methods and mechanisms. This is also why we tightly couple the knowledge-acquisition tools to the application ontology that specifies the domain terms used in the problem-solving systems. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system.

Artificial Intelligence↗
PubMed1984

An expert system for the evaluation of liver functional assessment.

The paper describes an expert system for the assessment of the liver function. Since the system must act as an intelligent assistant for a general physician, a major emphasis has been laid upon its interactive capabilities. In particular, the user can ask the system how a given conclusion has been reached (explanation facilities) and can alter the normal operation flow. In the design of the system, the different significance and availability of the clinical data and the laboratory tests have been taken into account: The investigations about a given patient start from the clinical data, and only when there is some evidence of hepatopathy are the results of some laboratory tests requested. The final result of the investigations consists of the assessment of the liver function in terms of four aspects (biosynthesis, cholestasis, cytolysis, reactivity), each of which is assigned a linguistic value describing its impairment degree. The techniques adopted in the system are based on Artificial Intelligence methodologies augmented with linguistic terms handled according to the fuzzy set theory.

Diagnosis, Computer-Assisted↗
PubMed2006

Reading and spelling abilities in children with severe speech impairments and cerebral palsy at 6, 9, and 12 years of age in relation to cognitive development: a longitudinal study.

Development of literacy skills was studied in six children (one male, five females) with severe speech impairments and cerebral palsy (CP). These skills were related to intellectual development, phonological abilities, and short-term memory. Three of the children were diagnosed with dystonia, and three with diplegia. They had no, or severely restricted, independent mobility (Gross Motor Function Classification System Level IV for four children and Level V for two), and severe fine motor problems, including difficulty with pointing. As they had no intelligible speech, the Bliss system was the primary communication mode. Assessments were made at approximately 6, 9, and 12 years of age. The results revealed that the children had difficulties acquiring literacy skills, although intellectual level and phonological ability predicted otherwise. Positive development during the first 3 years was followed by an arrest. A conspicuous decrease in IQ points was also found. Thus, phonological ability does not seem to have the same predictive power for literacy development in children with severe speech impairments and CP as in typically developing children. Further studies are needed to clarify the role of phonological abilities, working memory, and strategies used in literacy acquisition in these children. Such studies might also clarify the importance of articulatory abilities in early literacy acquisition.

Adolescent↗
PubMed1996

Modifying an expert system construction to pattern recognition solution.

Medical expert systems are a successful field of applied artificial intelligence. We constructed an otoneurological expert system in our previous research, and in this study we consider its reasoning method. The reasoning process can be described as a modified nearest neighbour solution derived from pattern recognition. The expert system was tested and functions reliably.

Ear↗
PubMed1992

A computer-based decision support system for diagnostic histopathology of the breast.

Accurate histological diagnosis of breast lesions is essential for the appropriate management of the patient. However, the technique of histological typing is problematic due to the large number of histological patterns, often of a complex and variable nature, which occur in breast disease. The introduction of the Breast Screening Programme has increased the burden on pathologists, and emphasised the need for training. Problems arise because mammographic screening detects a greater proportion of special histological types, with their attendant difficulties of identification, when compared to clinically palpable lesions. A computer-based decision support tool has been developed to assist pathologists in the histological diagnosis of breast disease. Unlike conventional expert systems, which seek to recreate the problem-solving processes of an expert, this system has been designed to act as an intelligent assistant to the pathologist. The system represents knowledge in the form of 'disease profiles', and utilises a novel inference model based upon the mathematical technique of hypergraphs. Initial trials with this system have demonstrated that a high level of diagnostic accuracy can be achieved.

Breast Diseases↗
PubMed1999

Automatic enrichment of the unified medical language system starting from the ADM knowledge base.

The Unified Medical Language System (UMLS) project aims to provide a repository of terms, concepts and relationships from several medical classifications. This work describes the possibility to enrich automatically with meaningful links the UMLS database by using description of diseases from another knowledge base, in our case ADM (Aide au Diagnostic Medical). In spite of the constraints and the difficulties to qualify the interconcept links, the results show that it is possible to find and create new links from a french knowledge database to the UMLS one. One of the interests of this work is that the automated learning of the connections could be used with others knowledge databases like expert system databases.

Algorithms↗
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↗
PubMed2006

Knowledge acquisition for computation of semantic distance between WHO-ART terms.

Computation of semantic distance between adverse drug reactions terms may be an efficient way to group related medical conditions in pharmacovigilance case reports. Previous experience with ICD-10 on a semantic distance tool highlighted a bottleneck related to manual description of formal definitions in large terminologies. We propose a method based on acquisition of formal definitions by knowledge extraction from UMLS and morphosemantic analysis. These formal definitions are expressed with SNOMED International terms. We provide formal definitions for 758 WHO-ART terms: 321 terms defined from UMLS, 320 terms defined using morphosemantic analysis and 117 terms defined after expert evaluation. Computation of semantic distance (e.g. k-nearest neighbours) was implemented in J2EE terminology services. Similar WHO-ART terms defined by automated knowledge acquisition and ICD terms defined manually show similar behaviour in the semantic distance tool. Our knowledge acquisition method can help us to generate new formal definitions of medical terms for our semantic distance terminology services.

Adverse Drug Reaction Reporting Systems↗
PubMed1965

BRAIN SIZE.

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Anatomy, Comparative↗