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PubMedDate not supplied

Systems for the year 2000: the case for an integrated database.

Analysis of the requirements for information management in the various segments of the health care delivery system indicates that the needs of the various user groups overlap, arguing that an integrated information system should be shared between groups. Trends in health care policy suggest that the health care database should be global in scope, not limited to one health care facility. The design of an integrated health care information system that permits local autonomy requires a new approach to systems architecture.

Artificial Intelligence↗
PubMed1990

[A diagnostic expert system].

The introduction of new technologies in the field of electronics has influenced the development of technical equipment over the last few years. The progressive miniaturization of integrated circuits makes possible an expansion of the spectrum of functions offered by this equipment. This also applies to medical technology. These more complex units call for new methods of fault detection and diagnosis. In addition to analytical redundancy, tools developed by the artificial intelligence research community, such as expert systems, are becoming more and more important for fault diagnosis. On the basis of a realized diagnosis expert system the possibilities as well as the limits of such system are discussed. Also, possible future developments of artificial intelligence, like machine learning, are considered.

Artificial Intelligence↗
PubMed2004

Medical expert systems developed in j.MD, a Java based expert system shell: application in clinical laboratories.

Growing complexity of diagnostic tests, combined with increased workload, stringent laboratory accreditation demands, continuous shortening of turn-around-time and budget restrictions have forced laboratories to automate most of their iterative tasks. Introduction of artificial intelligence by means of expert systems has gained an important place in this automation process. Different parts of clinical laboratory activity can benefit from their implementation and the present project deals with one aspect, namely the clinical interpretation of diagnostic tests. This paper describes how j.MD, a new Java based expert system shell, was used to reprogram the expert system for interpretation of amylase isoenzyme patterns that has been in use for many years in our laboratory, and that was originally programmed in Pro.MD, a Prolog based expert system shell. One of the most important advantages of the j.MD system is its bidirectional link with the laboratory information system. This project shows how expert systems for the interpretation of complex diagnostic tests that demand specific expertise can become an integrated part of the automated clinical chemistry lab.

Alkaline Phosphatase↗
PubMed1996

The expert surgical assistant. An intelligent virtual environment with multimodal input.

Virtual Reality has made computer interfaces more intuitive but not more intelligent. This paper shows how an expert system can be coupled with multimodal input in a virtual environment to provide an intelligent simulation tool or surgical assistant. This is accomplished in three steps. First, voice and gestural input is interpreted and represented in a common semantic form. Second, a rule-based expert system is used to infer context and user actions from this semantic representation. Finally, the inferred user actions are matched against steps in a surgical procedure to monitor the user's progress and provide automatic feedback. In addition, the system can respond immediately to multimodal commands for navigational assistance and/or identification of critical anatomical structures. To show how these methods are used we present a prototype sinus surgery interface. The approach described here may easily be extended to a wide variety of medical and non-medical training applications by making simple changes to the expert system database and virtual environment models. Successful implementation of an expert system in both simulated and real surgery has enormous potential for the surgeon both in training and clinical practice.

Artificial Intelligence↗
PubMedDate not supplied

Evaluation of decision support systems in medicine.

Evaluation deals with the measurement or judgement of system characteristics and with comparison of these with the frame of reference. Evaluation of medical decision support systems is important because these systems are planned to support human decision making in tasks where information from different sources is combined to support clinicians' decisions concerning diagnosis, therapy planning and monitoring of the disease and treatment processes. As the field of decision support systems is still relatively unexplored, standards or generally accepted methodologies are not yet available for evaluation. Evaluation of medical decision support systems should be approached from the perspectives of knowledge acquisition, system development life-cycle and user-system integrated environment.

Artificial Intelligence↗
PubMed1994

Integrating consultation and semi-automatic knowledge acquisition in a prototype-based architecture: experiences with dysmorphic syndromes.

The paper describes an application of cognitive theories of Tversky and Rosch to prototype similarity of dysmorphic syndromes cases. The knowledge-based system supports diagnostic consultation and research in dysmorphic syndromes. It has been used routinely for many years. The knowledge base is semi-automatically generated from known cases of an outpatient clinic. Some results of the evaluation process of the system's achievements are shown. General conclusions based on the experience with this successful system are discussed.

Artificial Intelligence↗
PubMed1968

Intelligence has three facets. There are numerous intellectual abilities, but they fall neatly into a rational system.

In this limited space I have attempted to convey information regarding progress in discovering the nature of human intelligence. By intensive factor-analytic investigation, mostly within the past 20 years, the multifactor picture of intelligence has grown far beyond the expectations of those who have been most concerned. A comprehensive, systematic theoretical model known as the "structure of intellect" has been developed to put rationality into the picture. The model is a cubical affair, its three dimensions representing ways in which the abilities differ from one another. Represented are: five basic kinds of operation, four substantive kinds of information or "contents," and six formal kinds of information or "products," respectively. Each intellectual ability involves a unique conjunction of one kind of operation, one kind of content, and one kind of product, all abilities being relatively independent in a population, but with common joint involvement in intellectual activity. This taxonomic model has led to the discovery of many abilities not suspected before. Although the number of abilities is large, the 15 category constructs provide much parsimony. They also provide a systematic basis for viewing mental operations in general, thus suggesting new general psychological theory. The implications for future intelligence testing and for education are numerous. Assessment of intellectual qualities should go much beyond present standard intelligence tests, which seriously neglect important abilities that contribute to problem-solving and creative performance in general. Educational philosophy, curriculum-building, teaching procedures, and examination methods should all be improved by giving attention to the structure of intellect as the basic frame of reference. There is much basis for expecting that various intellectual abilities can be improved in individuals, and the procedures needed for doing this should be clear.

Cognition↗
PubMedDate not supplied

Patient monitoring systems: criteria for evaluation & selection.

Patient monitoring is more than just the acquisition and accumulation of physiological data. In order for it to be efficacious in patient diagnosis and treatment, the professional staff must be continuously involved in protocol development, evaluation, and modification. Given the constraints of these tasks, of cost limitations, and of staff interest and expertise, the clinical engineer should develop a rationale for making judgments about patient monitoring systems at his particular site. This rationale should include: evaluation of design criteria from medical, nursing, and engineering standpoints; consideration of the considerable and increasing effect of computers on monitoring; and the intelligent appraisal of technique and system development performed at other sites.

Bibliographies as Topic↗
PubMed1998

Expert systems in anesthesiology.

There are only a limited number of computer-based systems designed to support anesthesiologists in the operating room. This is evident from the very small number of publications on this topic. These systems may be classified by the functions they perform and include: intelligent anesthesia workstations (with current data acquisition, conditioning and analysis subsystems), systems to detect critical conditions in patients (including expert systems in smart alarm capacity), anesthesia management systems (for planning and management) and drug administration systems. Drug administration systems may be subdivided into open-loop and closed-loop systems. The techniques applied for design of such systems vary extensively. There are traditional rule-based expert systems and probability-based systems, and more recently developed artificial intelligence methods, such as neural networks and fuzzy logic. Computers are valuable tools that have the potential to assist anesthesiologists in carrying out cumbersome and monotonous processes. Future efforts may result in the development of sophisticated systems capable of assuming more responsibilities and of reducing human workload and stress.

Journal Article↗
PubMed2005

Evaluation of statistical association measures for the automatic signal generation in pharmacovigilance.

Pharmacovigilance aims at detecting the adverse effects of marketed drugs. It is generally based on the spontaneous reporting of events thought to be the adverse effects of drugs. Spontaneous Reporting Systems (SRSs) supply huge databases that pharmacovigilance experts cannot exhaustively exploit without data mining tools. Data mining methods; i.e., statistical association measures in conjunction with signal generation criteria, have been proposed in the literature but there is no consensus regarding their applicability and efficiency, especially since such methods are difficult to evaluate on the basis of actual data. The objective of this paper is to evaluate association measures on simulated datasets obtained with SRS modeling. We compared association measures using the percentage of false positive signals among a given number of the most highly ranked drug-event combinations according to the values of the association measures. By considering 150 drugs and 100 adverse events, these percentages of false positives, among the 500 most highly ranked drug-event couples, vary from 1.1% to 53.4% (averages over 1000 simulated datasets). As the measures led to very different results, we could identify which measures appeared to be the most relevant for pharmacovigilance.

Adverse Drug Reaction Reporting Systems↗
PubMed2004

An automated tissue preclassification approach for telepathology: implementation and performance analysis.

Telepathology is generally defined as the use of telecommunications technologies in the practice of anatomic or surgical pathology. In the usual telepathology scenario, a remotely located pathologist views images of tissues samples in order to render a diagnosis of the biopsy. Some telepathology systems involve interactive remote control of a microscope-based imaging system which delivers diagnostic quality imagery to the remote pathologist. The usefulness of such interactive systems depends on minimizing the end-to-end delays involved in controlling the robotic microscope, manipulating the tissue sample, and acquiring and transmitting the high-resolution image. An approach to minimizing end-to-end delay involves adding "intelligence" to the image acquisition system so that it can gather, classify, rank, and transmit diagnostically useful images in a semiautonomous fashion. In this research, we develop image analysis and ranking techniques which can improve the end-to-end performance of a robotic telepathology imaging system. Our semiautonomous image collection system uses morphological techniques to extract seed points for suspicious regions, a novel region growing algorithm to segment the regions of interest, and heuristically motivated expert system ranking techniques to select diagnostically relevant "next-step" image acquisitions. Diagnostic relevance of our segmentation and ranking algorithms is established via subjective and objective testing of the system. In subjective testing, pathologists Agree or Strongly Agree that all segmented regions are diagnostically relevant with probability greater than 0.75. In objective testing, 84% of "next-step" images acquired by our algorithms coincide with the areas most likely to be chosen by a pathologist.

Artificial Intelligence↗
PubMed2005

ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text.

ABNER (A Biomedical Named Entity Recognizer) is an open source software tool for molecular biology text mining. At its core is a machine learning system using conditional random fields with a variety of orthographic and contextual features. The latest version is 1.5, which has an intuitive graphical interface and includes two modules for tagging entities (e.g. protein and cell line) trained on standard corpora, for which performance is roughly state of the art. It also includes a Java application programming interface allowing users to incorporate ABNER into their own systems and train models on new corpora.

Algorithms↗
PubMed2005

An intelligent control method based on fuzzy logic for a robotic testing system for the human spine.

In previous biomechanical studies of the human spine, we implemented a hybrid controller to investigate load-displacement characteristics. We found that measurement errors in both position and force caused the controller to be less accurate than predicted. As an alternative to hybrid control, a fuzzy logic controller (FLC) has been developed and implemented in a robotic testing system for the human spine. An FLC is a real-time expert system that can emulate part of a human operator's knowledge by using a set of action rules. The FLC provides simple but robust solutions that cover a wide range of system parameters and can cope with significant disturbances. It can be viewed as a heuristic and modular way of defining a nonlinear, table-based control system. In this study, an FLC is developed which uses the force difference and the change in force difference as the input parameters, and the displacement as the output parameter. A rule-table based on these parameters is designed for the controller Experiments on a physical model composed of springs demonstrate the improved performance of the proposed method.

Algorithms↗
PubMed1999

Concepts, contexts and expert systems.

This paper describes problems identified in our attempts to develop an expert system for management of urinary tract infections. We found three aspects which we believe are important to consider when developing such systems. The objective of our future work will be to evaluate the impact of these problems on expert system development and usage.

Artificial Intelligence↗
PubMed1998

Extending contemporary decision support system designs to patient-oriented systems.

Decision support systems for patients can benefit from adopting knowledge engineering-based architectures. In this paper, we describe how decision support systems for patients differ from decision support systems for health professionals and knowledge engineering principles that can be used to improve the efficiency of developing patient support systems. We discuss a five-step process model for patient-computer dialogue and its incorporation into an architecture based on knowledge engineering ontologies. The architecture's components are grouped into transient and persistent application layers that support a general framework for patient decision support. The implementation of the object-based model using a relational database management system is also discussed.

Artificial Intelligence↗