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PubMedDate not supplied

Technical aspects of internet-based knowledge presentation in radiotherapy.

Three-dimensional radiotherapy planning is a complex and time-consuming optimization process which requires much experience. To simplify and to speed up the process of treatment planning as well as to exchange experience and therapeutic knowledge, the department of Medical Physics at the German Cancer Research Centre (DKFZ) in Heidelberg is developing an Internet-based 3D Radiotherapy planning and Information System (IRIS). IRIS designed internet-based client-server application, implemented using Java, CORBA and PVM. The concept of IRIS combines the functionality of an interactive tutorial with a discussion forum, teleconferencing tool and an atlas of dose distributions. Furthermore an integral knowledge-based system provides automatically generated, preoptimized treatment plans. This paper explains the technical design of the system and gives an overview of experiences gained by the technical realization of a first prototype using currently available internet technology. The prototype is currently running for testing in the intranet of DKFZ.

Artificial Intelligence↗
PubMed1988

Radiologist's responsibility in France when using contrast media.

French physicians must obtain their patient's consent before carrying out an action that interferes with the patient's corporal integrity. The doctor must use simple, approximate, intelligent, and honest words that help the patient to reach the indispensable decision. Proof of lack of consent lies with the patient. Three lawsuits dealing with contrast media use illustrate the application of the French general principles of medical responsibility and compliance with present standards of information for the patient.

Contrast Media↗
PubMed2006

Color object detection using spatial-color joint probability functions.

Object detection in unconstrained images is an important image understanding problem with many potential applications. There has been little success in creating a single algorithm that can detect arbitrary objects in unconstrained images; instead, algorithms typically must be customized for each specific object. Consequently, it typically requires a large number of exemplars (for rigid objects) or a large amount of human intuition (for nonrigid objects) to develop a robust algorithm. We present a robust algorithm designed to detect a class of compound color objects given a single model image. A compound color object is defined as having a set of multiple, particular colors arranged spatially in a particular way, including flags, logos, cartoon characters, people in uniforms, etc. Our approach is based on a particular type of spatial-color joint probability function called the color edge co-occurrence histogram. In addition, our algorithm employs perceptual color naming to handle color variation, and prescreening to limit the search scope (i.e., size and location) for the object. Experimental results demonstrated that the proposed algorithm is insensitive to object rotation, scaling, partial occlusion, and folding, outperforming a closely related algorithm based on color co-occurrence histograms by a decisive margin.

Algorithms↗
PubMed2004

A negentropy minimization approach to adaptive equalization for digital communication systems.

In this paper, we introduce and investigate a new adaptive equalization method based on minimizing approximate negentropy of the estimation error for a finite-length equalizer. We consider an approximate negentropy using nonpolynomial expansions of the estimation error as a new performance criterion to improve performance of a linear equalizer based on minimizing minimum mean squared error (MMSE). Negentropy includes higher order statistical information and its minimization provides improved converge, performance and accuracy compared to traditional methods such as MMSE in terms of bit error rate (BER). The proposed negentropy minimization (NEGMIN) equalizer has two kinds of solutions, the MMSE solution and the other one, depending on the ratio of the normalization parameters. The NEGMIN equalizer has best BER performance when the ratio of the normalization parameters is properly adjusted to maximize the output power(variance) of the NEGMIN equalizer. Simulation experiments show that BER performance of the NEGMIN equalizer with the other solution than the MMSE one has similar characteristics to the adaptive minimum bit error rate (AMBER) equalizer. The main advantage of the proposed equalizer is that it needs significantly fewer training symbols than the AMBER equalizer. Furthermore, the proposed equalizer is more robust to nonlinear distortions than the MMSE equalizer.

Algorithms↗
PubMed2006

Comments on "the 1993 DIMACS graph coloring challenge" and "energy function-based approaches to graph coloring".

Since all graphs in the 1993 DIMACS graph coloring challenge are undirected, each edge should be only counted once. However, in some files each edge is counted once, whereas in others each edge is counted twice; so a systematical check on the DIMACS challenge is made to eliminate the inconsistencies. Besides, the experimental results of a previous paper by Blas et al. counted each violated edges twice and neglected the inconsistencies in the DIMACS challenge. So the correct experimental results of a previous paper by Blas et al are also given.

Algorithms↗
PubMed2006

A functional-dependencies-based Bayesian networks learning method and its application in a mobile commerce system.

This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.

Algorithms↗
PubMed1997

Does diesel exhaust cause human lung cancer?

Recent reviews of epidemiological evidence on the relation between exposure to diesel exhaust (DE) and lung cancer risk have reached conflicting conclusions, ranging from belief that there is sufficient evidence to conclude that DE is a human lung carcinogen (California EPA, 1994) to conclusions that there is inadequate evidence to support a causal association between DE and human lung cancer (Muscat and Wynder, 1995). Individual studies also conflict, with both increases and decreases in relative risks of lung cancer mortality being cited with 95% statistical confidence. On balance, reports of elevated risk outnumber reports of reduced risk. This paper reexamines the evidence linking DE exposures to lung cancer risk. After briefly reviewing animal data and biological mechanisms, it surveys the relevant epidemiological literature and examines possible explanations for the discrepancies. These explanations emphasize the distinction between statistical associations, which have been found in many studies, and causal associations, which appear not to have been established. Methodological threats to valid causal inference are identified and new approaches for controlling them are proposed using recent techniques from artificial intelligence (AI) and computational statistics. These threats have not been adequately controlled for in previous epidemiological studies. They provide plausible noncausal explanations for the reported increases in relative risks, making it impossible to infer causality between DE exposure and lung cancer risk from these studies. A key contribution is to show how recent techniques developed in the AI-and-statistics literature can help clarify the causal interpretation of complex multivariate data sets used in epidemiological risk assessments. Applied to the key study of Garshick et al. (1988), these methods show that DE concentration has no positive causal association with occupational lung cancer mortality risk.

Animals↗
PubMed2001

Relation of measures of sleep-disordered breathing to neuropsychological functioning.

Sleep-disordered breathing (SDB) has been associated with neuropsychological (NP) deficits. The extent to which such effects are attributable to unmeasured confounders or selection biases, or are manifest across a range of SDB is unclear. The relationship of SDB with a broad range of NP functions was examined in 100 volunteers with a spectrum of SDB and without underlying comorbidity. Factor analysis suggested that the NP tests could be summarized as four constructs: declarative memory, signal discrimination, working memory, and set shifting. These factors plus vigilance were dependent variables. Independent variables were age, the respiratory disturbance index (RDI), a sleepiness score, the arousal index, and sleep-associated hypoxemia. Factors "declarative memory" (measuring 25% of the common variance, alpha = 0.95), "signal discrimination" (10% variance, alpha = 0.70), and "working memory" (9% variance, alpha = 0.52) were each significantly, linearly predicted by hypoxemia and/or the RDI, with no evidence for significant threshold effects. SDB measures accounted for 4-6% of the variance in NP constructs. In contrast, sleepiness best predicted vigilance. Thus, adverse exposures (hypoxemia or RDI) during sleep may negatively influence NP functions in a dose-response relationship, and, other than vigilance, these effects may not be directly attributable to sleepiness.

Attention↗
PubMed2004

Managing complex change in clinical study metadata.

In highly functional metadata-driven software, the interrelationships within the metadata become complex, and maintenance becomes challenging. We describe an approach to metadata management that uses a knowledge-base subschema to store centralized information about metadata dependencies and use cases involving specific types of metadata modification. Our system borrows ideas from production-rule systems in that some of this information is a high-level specification that is interpreted and executed dynamically by a middleware engine. Our approach is implemented in TrialDB, a generic clinical study data management system. We review approaches that have been used for metadata management in other contexts and describe the features, capabilities, and limitations of our system.

Artificial Intelligence↗
PubMed2004

Development of a pioneering clinical support system utilizing information technology.

Nowadays, evidence-based medicine has entered the mainstream of clinical judgement and the human genome has been completely decoded. Even the concept of individually designed medicine, that is, tailor-made medicine, is now being discussed. Due to their complexity, however, management methods for clinical information have yet to be established. We have conducted a study on a universal technique which enables one to select or produce by employing information processing technology clinical findings from various clinical information generated in vast quantity in day-to-day clinical practice, and to share such information and/or the results of analysis between two or more institutions. In this study, clinically useful findings have been successfully obtained by systematizing actual clinical information and genomic information obtained by an appropriate collecting and management method of information with due consideration to ethical issues. We report here these medical achievements as well as technological ones which will play a role in propagating such medical achievements.

Artificial Intelligence↗
PubMedDate not supplied

Expert systems for medical applications.

Expert systems, also known as intelligent knowledge based systems (IKBS), are computer programs which act as decision-support systems. They are currently being applied to a number of medical domains, most notably diagnosis and treatment planning. Their function is to assist the medical practitioner by giving ready access to the levels of skill shown by experts in a particular field. Much research effort has been expended but few systems have reached routine medical use. This paper presents a tutorial introduction to expert systems in medicine, explaining the basis of the technology, its current limitations and its prospective uses.

Expert Systems↗
PubMedDate not supplied

Evaluating and validating very large knowledge-based systems.

Most knowledge-based systems for use in medicine have been developed in response to specific problems such as the diagnosis of abdominal or chest pain in an accident and emergency department, or the diagnosis and treatment of meningitis. There is a role for a general decision support system capable of answering queries about any aspect of medicine, particularly in primary care. However, evaluating such a knowledge base requires more elaborate methodology than a simple iterative test and refine cycle. At the design stage an adequate knowledge base structure is required to allow focused modification of the knowledge base when errors are discovered. During the prolonged evaluation cycle the partially formed knowledge base must be tested with such techniques as validation checks for consistency and completeness and examination of characteristics of problem-solving procedures. Finally a variety of criteria that represent the performance, robustness, flexibility, predictability, validity, coverage, relevance and congruity of the knowledge base are needed for a full description of the system's worth. Two case studies from the Oxford System of Medicine project are provided as examples of this philosophy: validating specific medical facts and comparing two methods for aggregating reasoning for and against a decision option.

Artificial Intelligence↗