Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Intelligent Systems”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,603 records · Page 89Linked to original sources
PubMed2000

A model for optimal sequential decisions applied to liver transplantation.

The present investigation aims at the construction of a sequential decision rule derived from a decision-theoretic model. An optimal clinical management strategy based on patient risk assessment is to be found. A novel decision theoretic cost model has been selected for this clinical classification task: costs were determined by specifying a minimum acceptable sensitivity and specificity of the overall procedure. Non-linear optimisation combined with a robust partial classification method is used to find the earliest possible decision step where a final decision can be made subject to these quality restrictions. The probabilities needed in the model are estimated from data provided by a clinical study on liver transplantation patients. Decision steps for the decision-theoretic model were chosen before and after donor organ assessment, and postoperatively in the intensive care unit. Clinical parameters acquired in between decision steps were combined to scores obtained from artificial neural networks (ANNs). The encouraging results show the applicability of the model in the clinical setting.

Artificial Intelligence↗
PubMed2000

Healthcare knowledge acquisition: an ontology-based approach using the extensible markup language (XML).

The healthcare enterprise requires a great deal of knowledge to maintain premium efficiency in the delivery of quality healthcare. We employ Knowledge Management based knowledge acquisition strategies to procure 'tacit' healthcare knowledge from experienced healthcare practitioners. Situational, problem-specific Scenarios are proposed as viable knowledge acquisition and representation constructs. We present a healthcare Tacit Knowledge Acquisition Info-structure (TKAI) that allows remote healthcare practitioners to record their tacit knowledge. TKAI employs (a) ontologies for standardisation of tacit knowledge and (b) XML to represent scenario instances for their transfer over the Internet to the server-side Scenario-Base and for the global sharing of acquired tacit healthcare knowledge.

Artificial Intelligence↗
PubMed2000

Health expert's tacit knowledge acquisition and representation using specialised healthcare scenarios.

The abundance and transient nature to healthcare knowledge has rendered it difficult to acquire with traditional knowledge acquisition methods. In this paper, we propose a Knowledge Management approach, through the use of scenarios, as a mean to acquire and represent tacit healthcare knowledge. This proposition is based on the premise that tacit knowledge is best manifested in atypical situations. We also provide an overview of the representational scheme and novel acquisition mechanism of scenarios.

Artificial Intelligence↗
PubMed2001

[Drug vectorization or how to modulate tissular and cellular distribution of biologically active compounds].

Drug vectorization has undergone considerable development over the last few years. This review focuses on the intravenous route of administration. Colloid formulations allow a modulation of drug tissue distribution. Using liposomes and nanoparticles with unmodified surfaces, drugs can be targeted to macrophages of the reticulum endothelium system. When the liposomes or nanoparticles are covered with hydrophilic or flexible polymers, the vascular phase can be favored in order, for example, to facilitate selective extravasation at a tumor site. Therapeutic applications of these systems are presented. The development of "intelligent" vectors capable of modulating intracellular distribution of an active compounds is an equally interesting approach, for example pH-sensitive liposomes or nanoparticles decorated with folic acid capable of targeting intracellular cytoplasm.

Animals↗
PubMed2001

Monitoring quality of health care with relational patterns.

This paper describes the extraction of relational patterns from hospital discharge data for the purpose of monitoring the quality of health care. We discuss what relational patterns are and how they support the extraction of meaningful patterns from data that are expressive, comprehensive, and easy to interpret. We demonstrate how relational patterns can be applied to identify poor practices embedded in hospitalization processes, for instance, help trigger subsequent inquiries and support decision-making processes.

Algorithms↗
PubMed2001

Using questions and interests to guide data mining for medical quality management.

The healthcare sector is currently facing both the economic necessity and the technical opportunity of a data based approach to quality management. Against this background, a process model for such a data based medical quality management is proposed and intelligent data mining methods are applied to patient data. Intelligent data mining incorporates advantages of both knowledge acquisition from data and from experts. A controlled language for business questions is presented which abstracts from database and data mining terminology to allow high-level interaction. Objective and subjective interestingness of results is measured and used to filter and sort.

Artificial Intelligence↗
PubMed2002

Speech recognition: how good is good enough?

Since its infancy in the early 1990s, the technology of speech recognition has undergone a rapid evolution. Not only has the reliability of the programming improved dramatically, the return on investment has become increasingly compelling. The author describes some of the latest health care applications of speech-recognition technology, and how the next advances will be made in this area.

Artificial Intelligence↗
PubMed2003

Perspectives on incorporating human neurobehavioral end points in risk assessments.

Three topics are addressed: (1) measurement issues (e.g., the reliability and validity of neurobehavioral test scores), (2) general principles of assessment, including test selection, and (3) interpretation of scores. Psychological tests generally perform as well as medical tests in terms of reliability and validity. Test manuals, assessment textbooks, and psychologists are useful resources to the risk assessor. The variety of different tests employed in neurobehavioral studies complicates interstudy comparisons. In addition, tests that ostensibly assess the same general domain of function might assess somewhat different abilities within that domain. Although a uniform battery for use in all studies seems desirable, the battery appropriate for a specific study depends on study goals, knowledge about the mechanism(s) of neurotoxicity, nature of the study population, and pattern of exposure. Exposure-related neurobehavioral deficits are generally indicators of "altered function" rather than of "clinical disease." Limiting concern to end points corresponding to clinical disease might not be appropriate. Many neurobehavioral diagnoses are phenomenological and a neurotoxicant might cause a unique pattern of deficits for which no label has been created. The concern that a small shift in the central tendency of a distribution of test scores has no significance for the individual should be reexamined in light of the prevention paradox, formulated on the basis of epidemiologic studies of chronic disease. Poor performance on a neurobehavioral test does not necessarily map clearly onto underlying behavioral or neural substrate. The absence of such linkages, given current knowledge about brain-behavior relationships, should not reduce confidence in neurobehavioral end points. Use of neurobehavioral test scores involves considerations that differ little from those that the risk assessor routinely addresses in using end points commonly used in research on other topics in environmental epidemiology.

Environmental Pollutants↗
PubMed2003

Automatic section segmentation of medical reports.

Automated segmentation of medical reports can significantly enhance the productivity of the healthcare departments. While many algorithms have been developed for document summarization, passage retrieval, and story segmentation of news feeds, much less effort has been devoted to parsing of medical documents. We present an algorithm specifically developed for medical applications. The algorithm consists of two components. First, a rule-based algorithm is used to detect the sections that contain labels. It utilizes a knowledge base of commonly employed heading labels and linguistic cues seen within training examples. The second part of the algorithm handles the detection of unlabeled sections. It uses a combination of lexical pattern recognition and a classifier based on an expectation model for a particular class of medical reports. The proposed method was evaluated on three test corpora containing a total of 129,303 report sections. The detection rates for labeled and unlabeled sections for individual corpus ranged from 97.4% to 99.4% and from 96.5% to 99.0%, respectively. The rule-based approach is particularly effective for medical reports due to inherently structured nature of these documents.

Algorithms↗