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At least 883 records · Page 49Linked to original sources
PubMed2003

Sonic intelligence as a virtual therapeutic environment.

This paper reports on the results of a research project, on comparing one virtual collaborative environment with a first-person visual immersion (first-perspective interaction) and a second one where the user interacts through a sound-kinetic virtual representation of himself (avatar), as a stress-coping environment in real-life situations. Recent developments in coping research are proposing a shift from a trait-oriented approach of coping to a more situation-specific treatment. We defined as real-life situation a target-oriented situation that demands a complex coping skills inventory of high self-efficacy and internal or external "locus of control" strategies. The participants were 90 normal adults with healthy or impaired coping skills, 25-40 years of age, randomly spread across two groups. There was the same number of participants across groups and gender balance within groups. All two groups went through two phases. In Phase I, Solo, one participant was assessed using a three-stage assessment inspired by the transactional stress theory of Lazarus and the stress inoculation theory of Meichenbaum. In Phase I, each participant was given a coping skills measurement within the time course of various hypothetical stressful encounters performed in two different conditions and a control group. In Condition A, the participant was given a virtual stress assessment scenario relative to a first-person perspective (VRFP). In Condition B, the participant was given a virtual stress assessment scenario relative to a behaviorally realistic motion controlled avatar with sonic feedback (VRSA). In Condition C, the No Treatment Condition (NTC), the participant received just an interview. In Phase II, all three groups were mixed and exercised the same tasks but with two participants in pairs. The results showed that the VRSA group performed notably better in terms of cognitive appraisals, emotions and attributions than the other two groups in Phase I (VRSA, 92%; VRFP, 85%; NTC, 34%). In Phase II, the difference again favored the VRSA group against the other two. These results indicate that a virtual collaborative environment seems to be a consistent coping environment, tapping two classes of stress: (a) aversive or ambiguous situations, and (b) loss or failure situations in relation to the stress inoculation theory. In terms of coping behaviors, a distinction is made between self-directed and environment-directed strategies. A great advantage of the virtual collaborative environment with the behaviorally enhanced sound-kinetic avatar is the consideration of team coping intentions in different stages. Even if the aim is to tap transactional processes in real-life situations, it might be better to conduct research using a sound-kinetic avatar based collaborative environment than a virtual first-person perspective scenario alone. The VE consisted of two dual-processor PC systems, a video splitter, a digital camera and two stereoscopic CRT displays. The system was programmed in C++ and VRScape Immersive Cluster from VRCO, which created an artificial environment that encodes the user's motion from a video camera, targeted at the face of the users and physiological sensors attached to the body.

Adaptation, Psychological↗
PubMed2006

Two items of evidence, no putative source: an inference problem in forensic intelligence.

Intelligence analysts commonly associate cases on the basis of similarities found in compared characteristics of scientific evidence. The present paper studies some of the inferential difficulties associated with such operations. An analysis is proposed that breaks down the reasoning process into inference to common source, and inference to case linkage. The former requires an approach to the difficulty associated with evaluating the similarities of items of evidence from different cases with no putative source being available. The latter requires consideration to be given to the relevance of evidence. Throughout the paper, probability theory is used to describe the nature of the proposed inferences. Graphical models are also introduced with the aim of providing further insight into the dependence and independence relationships assumed to hold among the various propositions considered. Notions from decision theory are used to discuss ways in which intelligence analysts may assist investigators in deciding whether or not cases should be considered as linked.

Bayes Theorem↗
PubMed1982

Children's temperament and teachers' decisions.

Findings from a series of studies of the educational implications of children's temperament patterns are summarized. This research has been guided by three hypotheses: (1) that there are real individual differences among children in behavioural styles or temperament; (2) that individual variations in children's patterns of temperament influence the nature of their interpersonal interactions; and (3) that perceived variations in temperament become especially powerful influences on adults' decisions when children are handicapped or at risk. Based on these assumptions, the study of temperament has followed two primary lines of research. in the first we have attempted to delineate the hypothesized link between perceived temperament variations and teachers' educational decisions. In the second we have attempted to determine the influence of perceived temperament variations on children's personal-social competence within intervention settings. Findings support a relationship between children's temperament and their achievement and adjustment in school. Temperament was related to measures of children's academic performance and to teachers' perceptions of other aspects of children's school adjustment. Further, teachers' ratings of children's temperament were related to their classroom management decisions. The results suggest that teachers' responses to children in the classroom are mediated by their perceptions of the children's temperament.

Child↗
PubMed1998

Does products liability litigation threaten picture archiving and communication systems and/or telemedicine?

Numerous writers have commented on barriers to the growth and progress of telemedicine. Among these barriers are reimbursement concerns, professional liability exposure, licensing restrictions, hospital credentialing questions, and other problems. A legal threat not generally described in the literature to date is the possibility that products liability claims could be brought against manufacturers and distributors of hardware, software, and peripherals used in providing telemedicine services. Several of these concerns extend to picture archiving and communication systems (PACS), including, of course, teleradiology. This article considers that possibility in the context of several potential plaintiffs' theories, discusses currently applicable law, and proposes approaches to diminishing the magnitude and severity of this potential threat.

Artificial Intelligence↗
PubMed2007

Atypical lexical/semantic processing in high-functioning autism spectrum disorders without early language delay.

Although autism is associated with impaired language functions, the nature of semantic processing in high-functioning pervasive developmental disorders (HFPDD) without a history of early language delay has been debated. In this study, we aimed to examine whether the automatic lexical/semantic aspect of language is impaired or intact in these population. Eleven individuals with Asperger's Disorder (AS) or HFPDD-Not Otherwise Specified (NOS) and age-, IQ-, and gender-matched typically developing individuals performed a semantic decision task in four conditions using an indirect priming paradigm. Semantic priming effects were found for near-semantically related word pairs in the controls, whereas this was not the case in the AS or HFPDDNOS participants. This finding suggests similarities in the underlying semantic processing of language across PDD subtypes.

Adolescent↗
PubMed1994

Encoding a post-operative coronary artery bypass surgery care plan in the Arden Syntax.

The Arden Syntax for medical logic modules (Arden) was used to test the feasibility of encoding large, complex care plans. The critical portions of an existing paper-based care plan for the management of patients following coronary artery bypass graft (CABG) surgery were encoded in Arden and an X-windows user-interface was developed. The Arden Syntax proved adequate for encoding all of the necessary functions of the care plan. The limitations of the current Arden Syntax and possible additions to Arden are discussed.

Artificial Intelligence↗
PubMed1995

Mapping clause of Arden Syntax with HL7 and ASTM E 1238-88 standard.

The Arden Syntax is a standard description syntax for modular medical knowledge. The purpose of the Arden Syntax is to allow the construction of medical knowledge bases out of elementary medical logic modules (MLMs) that may be contributed and shared by different institutions. The format of the input data is not defined in the Arden Syntax, but left to the user. Every input and output must be rewritten for the local data access definition before an MLM can be used. It is suggested that by using the Health Level Seven (HL7) interface definition to define the communication and data transfer between the MLMs and the medical data base the shareability of MLMs can be enhanced.

Artificial Intelligence↗
PubMed1996

Building intelligent alarm systems by combining mathematical models and inductive machine learning techniques Part 2--sensitivity analysis.

In an earlier study an approach was described to generate intelligent alarm systems for monitoring ventilation of patients via mathematical simulation and machine learning. However, ventilator settings were not varied. In this study we investigated whether an alarm system could be created with which a satisfactory classification performance could be obtained under a wide variety of ventilator settings, by varying inspiratory to expiratory time (I:E) ratio, tidal volume and respiratory rate. In a first experiment three patient data sets were modeled, each with a different I:E ratio. A part of each data set was used to construct an alarm system for each I:E ratio. The remaining part was used to test the performance of the alarm systems. The three training sets were also combined to construct one alarm system, which was tested with the three test sets. Finally, all alarm systems were tested with data generated by a patient simulator. Similar experiments were performed for the tidal volume and the respiratory rate. It was concluded that an optimally functioning alarm system should contain a library of rule sets, one for each set of ventilator settings. A second best alternative is to take all possible settings into consideration when constructing the training set. Classification performance of the trees that were trained with multiple ventilator settings ranged from 98 to 100% for all test sets. When tested with the independent patient simulator data the classification performance of these trees ranged from 80 to 100%.

Airway Resistance↗
PubMed2005

The nature of expertise: a review.

This paper reviews the complex topic of expertise. It begins with an explanation for the range of interest and viewpoints, and moves on to a discussion of the nature and study of expertise. The diversity in definitions, domains, disciplines, and the impact of these factors on approaches to investigation, are offered as possible explanations for some of the differences that appear to run through the literature. Specific attention is given to issues that may concern ergonomists interested in understanding expertise from the perspective of work in complex and dynamic settings. We suggest a move away from traditional novice-expert experimental designs to study of work in a naturalistic way, studying tasks and activities that are sufficiently challenging so that real expertise can be elicited.

Artificial Intelligence↗
PubMed2005

A framework for distributed mediation of temporal-abstraction queries to clinical databases.

OBJECTIVE: The specification and creation of a distributed system that integrates medical knowledge bases with time-oriented clinical databases; the goal is to answer complex temporal queries regarding both raw data and its abstractions, such as are often required in medical applications. METHODS: (1) Specification, design, and implementation of a generalized access method to a set of heterogeneous clinical data sources, by using a virtual medical-record interface and by mapping the local terms to a set of standardized medical vocabularies; (2) specification of a generalized interface to a set of knowledge sources; (3) specification and implementation of a service, called ALMA that computes complex time-oriented medical queries that include both raw data and abstractions derivable from it; (4) design and implementation of a mediator, called IDAN, that answers raw-data and abstract queries by integrating the appropriate clinical data with the relevant medical knowledge and uses the computation service to answer the queries; (5) an expressive language that enables definition of time-dependent medical queries, which are referred to the mediator; (6) evaluation of the effect of the system, when combined with a new visual interface, called KNAVE-II, on the speed and accuracy of answering a set of complex queries in an oncology sub domain, by a group of clinicians, compared to answering these queries using paper or an electronic spreadsheet. RESULTS: We have implemented the full IDAN architecture. The IDAN/KNAVE-II combination significantly increased the accuracy and speed of answering complex queries about both the data and their abstractions, compared to the standard tools. CONCLUSION: The implemented architecture proves the feasibility of the distributed integration of medical knowledge sources with clinical data of heterogeneous sources. The results suggest that the proposed IDAN modular architecture has potential significance for supporting the automation of clinical tasks such as diagnosis, monitoring, therapy, and quality assessment.

Artificial Intelligence↗
PubMed2006

Reducing multiclass cancer classification to binary by output coding and SVM.

Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.

Artificial Intelligence↗
PubMed2005

The oncological nurse assistant: a web-based intelligent oncological nurse advisor.

When a person gets a cancer diagnosis the need for medical guidance often appears. In Norway, one of the providers of medical guidelines is the Norwegian Cancer Association where oncological nurses assist people with a cancer diagnosis or their relatives. The nurses search through both national and internal guidebooks and web pages. The input to this process is mostly discharge letters. The whole process is time consuming. To serve more patients, PaSent, a web-based intelligent oncological nurse advisor, has been developed. Through using discharge letters as input to our neural network-based information retrieval system PaSent, we have been able to provide relevant medical information to the patient as well as to the health personnel themselves. The PaSent search method uses predefined knowledge about the context, paired with the vocabulary of the input document, to compute a relevance measure for a potential result document. The system has been validated by oncological nurses and medical doctors. In the reported experiments, the PaSent system is able to recommend literature, in the top section of the search result list, that our judges also found highly relevant.

Artificial Intelligence↗
PubMed1998

IDEM: a Web application of case-based reasoning in histopathology.

Different software engineering and artificial intelligence methods can be used to design Internet retrieval of prototypical medical images. We used the case-based reasoning (CBR) approach to provide an 'intelligent' access to a collection of illustrated medical cases through the Internet. This paper presents a Web interface for the CBR system IDEM (image and diagnosis from examples in medicine) in the domain of breast pathology. Thanks to the definition of a similarity measure between the descriptions of cases we propose a flexible querying of the case-base and a quantitative browsing among cases through similarity links. The resemblance rates provided by the system argue for the quality and the relevancy of the retrieved data. The flexibility of the querying process is robust to missing information and could be adapted to a daily practice. The CBR approach is a promising method for a clinical relevant and an efficient retrieval of reference images and diagnosis clues through Internet.

Artificial Intelligence↗
PubMed1998

An Internet-based ontology editor for medical appropriateness criteria.

Appropriateness criteria and practice guidelines seek to promote the cost-effectiveness use of medical interventions, and can be most useful when integrated with computer-based patient records and order-entry systems. Building an abstract model (ontology) of appropriateness criteria can require considerable effort among investigators at geographically dispersed institutions. To facilitate the construction and maintenance of ontologies for clinical appropriateness criteria, the author developed an Internet-based system for viewing and editing the knowledge model. The system, called NEON (Network-based Editor for ONtologies), uses the World Wide Web as a platform-independent user interface. NEON allows users to edit the indexing terms and the semantic network that form the ontology for a set of appropriateness criteria. Ontologies built using the system can be imported and exported using an open, internationally standardized format based on the Standard Generalized Markup Language (SGML).

Artificial Intelligence↗
PubMed2001

Managing uncertainty in diagnosis of acute coronaric ischemia.

The paper presents the reasoning mechanism of COR, a knowledge-based system (KBS) able to provide support for the diagnosis of coronaric ischemia by integrating the interpretation of chest pain, 12-lead ECG, and bio-marker concentrations. Chest pain features are collected interactively through a questionnaire. The ECG signal is acquired in SCP format. Any set of bio-markers can be considered. Data input is incremental and possibly incomplete. Reasoning is based on revised uncertainty calculus, which allows a formal treatment of verbally expressed uncertainty concerning both input data and diagnostic rules. Each diagnosis is supplemented by a linguistic label, expressing the plausibility of the disease identified, given the symptoms observed.

Artificial Intelligence↗
PubMed2002

Driving in young adults with attention deficit hyperactivity disorder: knowledge, performance, adverse outcomes, and the role of executive functioning.

Past studies find that attention deficit hyperactivity disorder (ADHD) creates a higher risk for adverse driving outcomes. This study comprehensively evaluated driving in adults with ADHD by comparing 105 young adults with the disorder (age 17-28) to 64 community control (CC) adults on five domains of driving ability and a battery of executive function tasks. The ADHD group self-reported significantly more traffic citations, particularly for speeding, vehicular crashes, and license suspensions than the CC group, with most of these differences corroborated in the official DMV records. Cognitively, the ADHD group was less attentive and made more errors during a visual reaction task under rule-reversed conditions than the CC group. The ADHD group also obtained lower sceres on a test of driving rules and decision-making but not on a simple driving simulator. Both self- and other-ratings showed the CC group employed safer routine driving habits than the ADHD group. Relationships between the cognitive and driving measures and the adverse outcomes were limited or absent, calling into question their use in screening ADHD adults for driving risks. Several executive functions also were significantly yet modestly related to accident frequency and total traffic violations after controlling for severity of ADHD. These results are consistent with earlier studies showing significant driving problems are associated with ADHD. This study found that these driving difficulties were not a function of comorbid oppositional defiant disorder, depression, anxiety, or frequency of alcohol or illegal drug use. Findings to date argue for the development of interventions to reduce driving risks among adults with ADHD.

Accidents, Traffic↗
PubMedDate not supplied

Discovering H-bonding rules in crystals with inductive logic programming.

In the domain of crystal engineering, various schemes have been proposed for the classification of hydrogen bonding (H-bonding) patterns observed in 3D crystal structures. In this study, the aim is to complement these schemes with rules that predict H-bonding in crystals from 2D structural information only. Modern computational power and the advances in inductive logic programming (ILP) can now provide computational chemistry with the opportunity for extracting structure-specific rules from large databases that can be incorporated into expert systems. ILP technology is here applied to H-bonding in crystals to develop a self-extracting expert system utilizing data in the Cambridge Structural Database of small molecule crystal structures. A clear increase in performance was observed when the ILP system DMax was allowed to refer to the local structural environment of the possible H-bond donor/acceptor pairs. This ability distinguishes ILP from more traditional approaches that build rules on the basis of global molecular properties.

Artificial Intelligence↗
PubMed2003

Juveniles' competence to stand trial: a comparison of adolescents' and adults' capacities as trial defendants.

Abilities associated with adjudicative competence were assessed among 927 adolescents in juvenile detention facilities and community settings. Adolescents' abilities were compared to those of 466 young adults in jails and in the community. Participants at 4 locations across the United States completed a standardized measure of abilities relevant for competence to stand trial (the MacArthur Competence Assessment Tool--Criminal Adjudication) as well as a new procedure for assessing psychosocial influences on legal decisions often required of defendants (MacArthur Judgment Evaluation). Youths aged 15 and younger performed more poorly than young adults, with a greater proportion manifesting a level of impairment consistent with that of persons found incompetent to stand trial. Adolescents also tended more often than young adults to make choices (e.g., about plea agreements) that reflected compliance with authority, as well as influences of psychosocial immaturity. Implications of these results for policy and practice are discussed, with an emphasis on the development of legal standards that recognize immaturity as a potential predicate of incompetence to stand trial.

Adolescent↗