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At least 1,333 records · Page 74Linked to original sources
PubMed1994

[Communication and noise. Speech intelligibility of airplane pilots with and without active noise compensation].

Noise exposure measurements were performed with pilots of the German Federal Navy during flight situations. The ambient noise levels during regular flight were maintained at levels above a 90 dB A-weighted level. This noise intensity requires wearing ear protection to avoid sound-induced hearing loss. To be able to understand radio communication (ATC) in spite of a noisy environment, headphone volume must be raised above the noise of the engines. The use of ear plugs in addition to the headsets and flight helmets is only of limited value because personal ear protection affects the intelligibility of ATC. Whereas speech intelligibility of pilots with normal hearing is affected to only a smaller degree, pilots with pre-existing high-frequency hearing losses show substantial impairments of speech intelligibility that vary in proportion to the hearing deficit present. Communication abilities can be reduced drastically, which in turn can affect air traffic security. The development of active noise compensation devices (ANC) that make use of the "anti-noise" principle may be a solution to this dilemma. To evaluate the effectiveness of an ANC-system and its influence on speech intelligibility, speech audiometry was performed with a German standardized test during simulated flight conditions with helicopter pilots. Results demonstrate the helpful effect on speech understanding especially for pilots with noise-induced hearing losses. This may help to avoid pre-retirement professional disability.

Adult↗
PubMed1999

Not resting on its laurels, Sentara taps data analysis tool to fine-tune pathways.

Artificial intelligence data analysis leads health care system to fine-tune and improve new clinical pathways. Sentara Health Care in Norfolk, VA, developed a host of new clinical protocols in recent years, but instead of sitting back and enjoying initial improved outcomes and cost savings, it went the extra mile to enhance those pathways using a sophisticated data analysis tool.

Artificial Intelligence↗
PubMed2004

ISYMOD: a knowledge warehouse for the identification, assembly and analysis of bacterial integrated systems.

MOTIVATION: Complex biological functions emerge from interactions between proteins in stable supra-molecular assemblies and/or through transitory contacts. Most of the time protein partners of the assemblies are composed of one or several domains which exhibit different biochemical functions. Thus the study of cellular process requires the identification of different functional units and their integration in an interaction network; such complexes are referred to as integrated systems. In order to exploit with optimum efficiency the increased release of data, automated bioinformatics strategies are needed to identify, reconstruct and model such systems. For that purpose, we have developed a knowledge warehouse dedicated to the representation and acquisition of bacterial integrated systems involved in the exchange of the bacterial cell with its environment. RESULTS: ISYMOD is a knowledge warehouse that consistently integrates in the same environment the data and the methods used for their acquisition. This is achieved through the construction of (1) a domain knowledge base (DKB) devoted to the storage of the knowledge about the systems, their functional specificities, their partners and how they are related and (2) a methodological knowledge base (MKB) which depicts the task layout used to identify and reconstruct functional integrated systems. Instantiation of the DKB is obtained by solving the tasks of the MKB, whereas some tasks need instances of the DKB to be solved. AROM, an object-based knowledge representation system, has been used to design the DKB, and its task manager, AROMTasks, for developing the MKB. In this study two integrated systems, ABC transporters and two component systems, both involved in adaptation processes of a bacterial cell to its biotope, have been used to evaluate the feasibility of the approach.

ATP-Binding Cassette Transporters↗
PubMed2004

The affirmation of self: a new perspective on the immune system.

The fundamental concepts of autopoiesis, which emphasize the circular organization underlying both living organisms and cognition, have been criticized on the grounds that since they are conceived as a tight logical chain of definitions and implications, it is often not clear whether they are indeed a scientific theory or rather just a potential scientific vocabulary of doubtful utility to working scientists. This article presents the deployment of the concepts of autopoiesis in the field of immunology, a discipline where working biologists themselves spontaneously have long had recourse to "cognitive" metaphors: "recognition"; a "repertoire" of recognized molecular shapes; "learning" and "memory"; and, most striking of all, a "self versus non-self" distinction. It is shown that in immunology, the concepts of autopoiesis can be employed to generate clear novel hypotheses, models demonstrating these ideas, testable predictions, and novel therapeutic procedures. Epistemologically, it is shown that the self-non-self distinction, while quite real, is misleadingly named. When a real mechanism for generating this distinction is identified, it appears that the actual operational distinction is between (a) a sufficiently numerous set of initial antigens, present from the start of ontogeny, in conditions that allow for their participation in the construction of the system's organization and operation, and (b) single antigens that are first presented to the system after two successive phases of maturation. To call this a self-non-self distinction obscures the issue by presupposing what it ought to be the job of scientific investigation to explain.

Artificial Intelligence↗
PubMed1991

Mnemonic coding system for clinical data entry into laboratory computers: its effect on quality and efficiency.

A simplified coding method for entering the clinical details found on pathology request cards was developed. The method uses a basic four letter code, derived from the initial character of the first four words in a clinical detail, being expanded to four characters with letters from the final word if the number of words is less than four. Rules were devised to cope with common medical terminology. In excess of 90% of clinical details on request cards are readily input by clerical staff using our coding system, and 8% of clinical details are used intelligently by the computer in scheduling further tests or automatically commenting on results. A carefully designed coding system such as the one outlined above could greatly facilitate input of clinical detail without the penalty of reduced throughput.

Clinical Laboratory Information Systems↗
PubMed1993

An evaluation of explanations of probabilistic inference.

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.

Bayes Theorem↗
PubMed2006

Case-based reasoning in the health sciences: What's next?

OBJECTIVES: This paper presents current work in case-based reasoning (CBR) in the health sciences, describes current trends and issues, and projects future directions for work in this field. METHODS AND MATERIAL: It represents the contributions of researchers at two workshops on case-based reasoning in the health sciences. These workshops were held at the Fifth International Conference on Case-Based Reasoning (ICCBR-03) and the Seventh European Conference on Case-Based Reasoning (ECCBR-04). RESULTS: Current research in CBR in the health sciences is marked by its richness. Highlighted trends include work in bioinformatics, support to the elderly and people with disabilities, formalization of CBR in biomedicine, and feature and case mining. CONCLUSION: CBR systems are being better designed to account for the complexity of biomedicine, to integrate into clinical settings and to communicate and interact with diverse systems and methods.

Artificial Intelligence↗
PubMed1992

An evaluation of explanations of probabilistic inference.

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results, to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.

Anesthesia↗
PubMed1995

Evaluation of a belief-network-based reminder system that learns from utility feedback.

PRETRIEVE is a belief-network-based, unsolicited information-retrieval system that performs machine learning based on user feedback. We report here on the document-ordering and document-retrieval performance of PRETRIEVE. We developed a test collection of 410 judgments of document utility in a simulated medical order-entry context. We characterized the validity of these judgments, which were elicited from domain experts, by measuring interrater and intrarater reproducibility. We developed a measure of the quality of document orderings similar to the ROC-curve analysis used to evaluate document-retrieval systems. We found that the ordering performance of the PRETRIEVE system was (1) substantially better than random, (2) somewhat less than ideal, and (3) superior to that of versions of the PRETRIEVE system that used relevance feedback instead of utility feedback. Under a set of assumptions, which we make explicit, we found that the documents retrieved by a version of PRETRIEVE that modeled time cost were of higher utility than those retrieved by a similar rule-based system.

Artificial Intelligence↗
PubMed2004

An efficient and effective region-based image retrieval framework.

An image retrieval framework that integrates efficient region-based representation in terms of storage and complexity and effective on-line learning capability is proposed. The framework consists of methods for region-based image representation and comparison, indexing using modified inverted files, relevance feedback, and learning region weighting. By exploiting a vector quantization method, both compact and sparse (vector) region-based image representations are achieved. Using the compact representation, an indexing scheme similar to the inverted file technology and an image similarity measure based on Earth Mover's Distance are presented. Moreover, the vector representation facilitates a weighted query point movement algorithm and the compact representation enables a classification-based algorithm for relevance feedback. Based on users' feedback information, a region weighting strategy is also introduced to optimally weight the regions and enable the system to self-improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.

Abstracting and Indexing↗
PubMed2003

MedScan, a natural language processing engine for MEDLINE abstracts.

MOTIVATION: The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications. RESULTS: We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.

Abstracting and Indexing↗
PubMed1997

Assessment of IT-based solutions in healthcare: trends in today's practice and justification of tomorrow's approach.

State-of-the-Art with respect to assessment of IT-based solutions in Healthcare is discussed with a user-oriented perspective on system development and assessment. Based on this and other basic conditions reported in the literature, requirements for a methodology for user-driven, formative assessment during the entire life-cycle of the IT-based system are synthesised.

Artificial Intelligence↗
PubMed2026

Targeted Nanoparticle Delivery CRISPR/Cas9: overcoming biological barriers, enhancing stability, and improving therapeutic precision.

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) has emerged as a promising gene-editing platform for genetic disorders; however, its in vivo application remains limited by low delivery efficiency and biological barriers. Many CRISPR payloads fail to reach target sites due to extracellular degradation, immune clearance, and intracellular trafficking limitations. This review examines the interplay between biological barriers and nanoparticle engineering strategies for CRISPR/Cas9 delivery. A barrier-oriented engineering approach is proposed as a central framework, encompassing ligand-based surface modification for enhanced targeting and uptake, improved circulation stability via PEGylation and biomimetic coatings, and optimized payload release through endosomal escape strategies. Stimulus-responsive nanoparticle systems further enable spatiotemporal control over payload release. Nuclear targeting strategies, including optimization of nuclear localization signals (NLS) and exploitation of endogenous trafficking pathways, are highlighted as key factors for improving genome-level editing efficiency. Despite these advances, major challenges-including limited intracellular delivery efficiency, insufficient targeting precision, and safety concerns-continue to hinder clinical translation. Future directions highlight artificial intelligence-driven nanoparticle design, personalized delivery systems, and next-generation CRISPR platforms. Overall, an integrated, barrier-oriented engineering strategy is essential for advancing CRISPR/Cas9 delivery toward clinical applications, ultimately advancing global good health and well-being.

CRISPR/Cas9↗
PubMed2004

Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers.

Information technology can support the implementation of clinical research findings in practice settings. Technology can address the quality gap in health care by providing automated decision support to clinicians that integrates guideline knowledge with electronic patient data to present real-time, patient-specific recommendations. However, technical success in implementing decision support systems may not translate directly into system use by clinicians. Successful technology integration into clinical work settings requires explicit attention to the organizational context. We describe the application of a "sociotechnical" approach to integration of ATHENA DSS, a decision support system for the treatment of hypertension, into geographically dispersed primary care clinics. We applied an iterative technical design in response to organizational input and obtained ongoing endorsements of the project by the organization's administrative and clinical leadership. Conscious attention to organizational context at the time of development, deployment, and maintenance of the system was associated with extensive clinician use of the system.

Academic Medical Centers↗
PubMed2001

Patient monitoring using personal area networks of wireless intelligent sensors.

A wearable device for monitoring multiple physiological signals (polysomnograph) usually includes multiple wires connecting sensors and the monitoring device. In order to integrate information from intelligent sensors, all devices must be connected to a Personal Area Network (PAN). This system organization is unsuitable for longer and continuous monitoring, particularly during the normal activity. For instance, monitoring of athletes and computer assisted rehabilitation commonly involve unwieldy wires to arms and legs that restrain normal activity. We propose a wireless PAN of intelligent sensors as a system architecture of choice, and present a new design of wireless personal area network with physiological sensors for medical applications. Intelligent wireless sensors perform data acquisition and limited processing. Individual sensors monitor specific physiological signals (such as EEG, ECG, GSR, etc.) and communicate with each other and the personal server. Personal server integrates information from different sensors and communicates with the rest of telemedical system as a standard mobile unit. We present our prototype implementation of Wireless Intelligent SEnsor (WISE) based on a very low power consumption microcontroller and a DSP-based personal server. In future we expect all components of WISE integrated in a single chip for use in a variety of new medical applications and sophisticated human computer interfaces. Existing growth of wireless infrastructure will allow a range of new telemedical applications that will significantly improve the quality of health care.

Computer Communication Networks↗