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At least 865 records · Page 48Linked to original sources
PubMed1991

Distinguishing drug toxicity syndromes from medical diseases: a QMR computer-based approach.

Drug effects can mimic a wide variety of diseases. Experts note that adverse drug reactions (ADRs) have become the 'greatest imitator' of disease in clinical medicine. Quick Medical Reference (QMR) is a decision support system providing diagnostic data about more than 600 medical diseases. Currently, QMR contains only limited drug information. Just as physicians have difficulty diagnosing ADRs, QMR has similar problems in differentiating natural disease manifestations from drug toxicity syndromes. To remedy this problem, two prototype Drug Syndromes (DS), Carbamazepine Toxicity and Penicillin Toxicity, were incorporated into the QMR Knowledge Base (KB). Using detailed case reports, we demonstrated that a DS-augmented version of QMR was successful in discriminating these DS from the other diseases in QMR's KB. The addition of DS significantly improves QMR's diagnostic performance in cases in which some of the pathologic features are the consequence of drugs.

Aged↗
PubMed1993

The relationship between cognitive characteristics and decision making.

Team handball players (N = 118) underwent a number of cognitive tests to examine how much of their decision making (DM) ability, as measured through responses to game slides projected to them for 2 seconds under low and high exertion levels (i.e., walking and running), was accounted for by cognitive components. A stepwise multiple linear regression indicated that experience was the most pronounced predictor of DM capacity in both exertion conditions. In the walking condition, concentrational consistency, avoidance of concentrational mistakes, and short-term memory, together with experience, produced a multiple R of 0.48 with decision making. In the running condition, choice reaction time (CRT), intelligence, and short-term memory, together with experience, correlated 0.46 with DM. These differences in cognitive abilities, as predictors of DM under walking and running conditions, are discussed in terms of information processing models and other cognitive processes.

Attention↗
PubMed2000

Generalized radial basis function networks for classification and novelty detection: self-organization of optimal Bayesian decision.

By adding reverse connections from the output layer to the central layer it is shown how a generalized radial basis functions (GRBF) network can self-organize to form a Bayesian classifier, which is also capable of novelty detection. For this purpose, three stochastic sequential learning rules are introduced from biological considerations which pertain to the centers, the shapes, and the widths of the receptive fields of the neurons and allow ajoint optimization of all network parameters. The rules are shown to generate maximum-likelihood estimates of the class-conditional probability density functions of labeled data in terms of multivariate normal mixtures. Upon combination with a hierarchy of deterministic annealing procedures, which implement a multiple-scale approach, the learning process can avoid the convergence problems hampering conventional expectation-maximization algorithms. Using an example from the field of speech recognition, the stages of the learning process and the capabilities of the self-organizing GRBF classifier are illustrated.

Artificial Intelligence↗
PubMed1996

A graph-grammar approach to represent causal, temporal and other contexts in an oncological patient record.

The data of a patient undergoing complex diagnostic and therapeutic procedures do not only form a simple chronology of events, but are closely related in many ways. Such data contexts include causal or temporal relationships, they express inconsistencies and revision processes, or describe patient-specific heuristics. The knowledge of data contexts supports the retrospective understanding of the medical decision-making process and is a valuable base for further treatment. Conventional data models usually neglect the problem of context knowledge, or simply use free text which is not processed by the program. In connection with the development of the knowledge-based system THEMPO (Therapy Management in Pediatric Oncology), which supports therapy and monitoring in pediatric oncology, a graph-grammar approach has been used to design and implement a graph-oriented patient model which allows the representation of non-trivial (causal, temporal, etc.) clinical contexts. For context acquisition a mouse-based tool has been developed allowing the physician to specify contexts in a comfortable graphical manner. Furthermore, the retrieval of contexts is realized with graphical tools as well.

Adverse Drug Reaction Reporting Systems↗
PubMed2000

Staging of cervical cancer with soft computing.

This paper describes a way of designing a hybrid decision support system in soft computing paradigm for detecting the different stages of cervical cancer. Hybridization includes the evolution of knowledge-based subnetwork modules with genetic algorithms (GA's) using rough set theory and the Interactive Dichotomizer 3 (ID3) algorithm. Crude subnetworks obtained via rough set theory and the ID3 algorithm are evolved using GA's. The evolution uses a restricted mutation operator which utilizes the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conventional multilayer perceptron. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for extraction of logical rules and human interpretation of the inferencing procedure.

Algorithms↗
PubMed1994

[Computer-assisted validation system applied to hematology: Valab-hemato].

Validation of laboratory reports is the ultimate step before transmission of results to the clinician. The biologist checks the intrinsic consistency of the data as well as their possible medical value that is liable to lead to other investigations. Such a policy, when performed on all the data, is time-consuming, boring and uncertain. This step may be simplified by the use of a computerized expert system. The computer assisted validation system presented here concerns routine haematology data (Valab-haemato). Like its predecessor devoted to clinical chemistry (Valab-Biochem) it is based on the performance of a powerful inference engine which generates a decision-making tree for each report according to the data. This adaptability gives the system a capacity very close to human reasoning. In its haematology version the system deals with many variables including sex, age, origin of the patient (hospital ward), and the haematological data (blood cell count, differential, reticulocyte count, various information drawn from microscope examination of the blood smear as well as any report concerning the blood sample, erythrocyte sedimentation rate). Previous data are also taken into account, as well as the normal ranges, the values beyond which no result can be automatically validated and the delta-check. Some information definitely prevents validation of the results, others can be validated if they have been previously approved. Whereas the method of reasoning is fixed, all items are changeable in order to adapt the system to the type of activity of the laboratory.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗
PubMed2001

Artificial intelligence: the clinician of the future.

Human beings have long been fascinated with the idea of artificial intelligence. This fascination is fueled by popular films such as Stanley Kubrick's 2001: A Space Odyssey and Stephen Spielberg's recent film, AI. However intriguing artificial intelligence may be, Hubert and Spencer Dreyfus contend that qualities exist that are uniquely human--the qualities thought to be inaccessible to the computer "mind." Patricia Benner further investigated the qualities that guide clinicians in making decisions and assessments that are not entirely evidence-based or grounded in scientific data. Perhaps it is the intuitive nature of the human being that separates us from the machine. The state of artificial intelligence is described herein, along with a discussion of computerized clinical decision-making and the role of the human being in these decisions.

Artificial Intelligence↗
PubMed2001

Classifying vertical facial deformity using supervised and unsupervised learning.

OBJECTIVES: To evaluate the potential for machine learning techniques to identify objective criteria for classifying vertical facial deformity. METHODS: 19 parameters were determined from 131 lateral skull radiographs. Classifications were induced from raw data with simple visualisation, C5.0 and Kohonen feature maps; and using a Point Distribution Model (PDM) of shape templates comprising points taken from digitised radiographs. RESULTS: The induced decision trees enable a direct comparison of clinicians' idiosyncrasies in classification. Unsupervised algorithms induce models that are potentially more objective, but their blackbox nature makes them unsuitable for clinical application. The PDM methodology gives dramatic visualisations of two modes separating horizontal and vertical facial growth. Kohonen feature maps favour one clinician and PDM the other. Clinical response suggests that while Clinician 1 places greater weight on 5 of 6 parameters, Clinician 2 relies on more parameters that capture facial shape. CONCLUSIONS: While machine learning and statistical analyses classify subjects for vertical facial height, they have limited application in their present form. The supervised learning algorithm C5.0 is effective for generating rules for individual clinicians but its inherent bias invalidates its use for objective classification of facial form for research purposes. On the other hand, promising results from unsupervised strategies (especially the PDM) suggest a potential use for objective classification and further identification and analysis of ambiguous cases. At present, such methodologies may be unsuitable for clinical application because of the invisibility of their underlying processes. Further study is required with additional patient data and a wider group of clinicians.

Algorithms↗
PubMed2002

[Intelligent system to perform a diagnostic protocol for lymphatic invasion in laryngeal cancer].

Laryngeal carcinoma is the most frequent malignant tumour in head and neck. Node invasion is known to be one of the most important prognostic factors. The aim of this study has been to design an intelligent system to perform a diagnostic algorithm of metastasic neck nodes. 122 clinical reports of patients diagnosed of laryngeal carcinoma in our department have been reviewed. The compiled data have been: tumor site, T stage, N stage (clinical, after CT scan and post-surgery). The method used to design the intelligent system has been the ID3, which is able to generate a minimal decision tree. Palpation has been the variable that has given more information about node invasion. CT has proved to be more efficient in supraglottic tumours. ID3 method has shown to be useful in performing diagnostic algorithms, specially when the number of cases and diagnostic tests are high.

Adult↗
PubMedDate not supplied

Decision-theoretic refinement planning in medical decision making: management of acute deep venous thrombosis.

Decision-theoretic refinement planning is a new technique for finding optimal courses of action. The authors sought to determine whether this technique could identify optimal strategies for medical diagnosis and therapy. An existing model of acute deep venous thrombosis of the lower extremities was encoded for analysis by the decision-theoretic refinement planning system (DRIPS). The encoding represented 6,206 possible plans. The DRIPS planner used artificial intelligence techniques to eliminate 5,150 plans (83%) from consideration without examining them explicitly. The DRIPS system identified the five strategies that minimized cost and mortality. The authors conclude that decision-theoretic planning is useful for examining large medical-decision problems.

Algorithms↗
PubMed1991

Can reading disabilities be diagnosed without using intelligence tests?

Unlike conventional procedures, which use IQ in making diagnostic and eligibility decisions regarding learning disabilities, this demonstration study used listening comprehension and other reading-related tasks to make a differential diagnosis of reading disabilities. Tests of listening and reading comprehension were administered to 180 children from Grades 3 through 8. A regression equation was then derived to predict reading comprehension from listening comprehension. The regression equation was applied to the listening comprehension scores of seven children from Grades 3 to 8 who had reading difficulties, and their reading comprehension was predicted. Based on the discrepancy between their actual reading comprehension and the predicted reading comprehension, their reading difficulty was attributed to one of the following three factors: (a) poor decoding, (b) poor comprehension, or (c) a combination of poor decoding and poor comprehension. The validity of these diagnostic decisions was assessed by testing independently these children's word-decoding skill and reading speed. The results suggest that this diagnostic procedure has potential utility.

Adolescent↗
PubMed1993

Integrating decision support, based on the Arden Syntax, in a clinical laboratory environment.

A clinical decision support system prototype have been developed in the clinical laboratory environment. The knowledge base consists of Medical Logic Modules, written in the Arden Syntax, and the work describes how these modules can be written, evoked and executed in a system, that is integrated with a laboratory information system, and facilitate real time validation of laboratory data. Tools and methods for building a decision support system are described and design aspects, such as database access, system validation and platform independence, are discussed.

Artificial Intelligence↗
PubMed1991

An approach to evaluating the accuracy of DXplain.

DXplain is a computer-based decision support system which generates a differential diagnosis (ddx) from a given list of clinical manifestations (Barnett et al., J. Am. Med. Assoc. 258 (1987) 67-74). An approach was developed to evaluate the accuracy of the ddx's produced by DXplain. The first step involves the collection of 65 benchmark cases drawn from a variety of sources and authors. Despite their diverse origins, the cases share in common that they are all clinical cases upon which a consulting physician might be asked to produce a differential. This helps to ensure that the evaluation of the system will be done in an environment similar to that in which the system is actually used. In the second step, all cases are reviewed by five board-certified physicians (experts) as well as DXplain. For each case, the evaluators (experts and DXplain) produce a rank-ordered ddx list along with an indication of how strongly each disease was felt to be supported by the case findings. A scoring technique was devised which rewards concordance with the gold standard: a consensus of the evaluators' ddx lists. Each evaluator receives a score which is proportional to the degree of agreement achieved with the consensus on the ddx submitted. Preliminary results on a trial evaluation of 46 cases indicate that DXplain, on average, did well in agreeing with the consensus. Agreement was achieved both in regard to the specific diagnoses listed in the ddx and the degree to which the diseases were felt to be supported by the case findings. A discussion of some important issues in the evaluation of knowledge-based systems is undertaken.

Algorithms↗
PubMed1994

AI-based approach to automatic sleep classification.

The primary goal of this paper is to introduce the potential of artificial intelligence (AI) methods to researchers in sleep classification. AI provides learning procedures for the construction of a sleep classifier, prescribing how to combine the observed parameters and how to derive the corresponding decision thresholds. A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented.

Artificial Intelligence↗
PubMed2003

Towards an integrated approach to natural hazards risk assessment using GIS: with reference to bushfires.

This paper develops a GIS-based integrated approach to risk assessment in natural hazards, with reference to bushfires. The challenges for undertaking this approach have three components: data integration, risk assessment tasks, and risk decision-making. First, data integration in GIS is a fundamental step for subsequent risk assessment tasks and risk decision-making. A series of spatial data integration issues within GIS such as geographical scales and data models are addressed. Particularly, the integration of both physical environmental data and socioeconomic data is examined with an example linking remotely sensed data and areal census data in GIS. Second, specific risk assessment tasks, such as hazard behavior simulation and vulnerability assessment, should be undertaken in order to understand complex hazard risks and provide support for risk decision-making. For risk assessment tasks involving heterogeneous data sources, the selection of spatial analysis units is important. Third, risk decision-making concerns spatial preferences and/or patterns, and a multicriteria evaluation (MCE)-GIS typology for risk decision-making is presented that incorporates three perspectives: spatial data types, data models, and methods development. Both conventional MCE methods and artificial intelligence-based methods with GIS are identified to facilitate spatial risk decision-making in a rational and interpretable way. Finally, the paper concludes that the integrated approach can be used to assist risk management of natural hazards, in theory and in practice.

Conservation of Natural Resources↗
PubMed1998

Risk assessment and economic analysis for managing risks to human health from pathogenic microorganisms in the food supply.

Risk managers increasingly face having to justify their decisions in allocating limited resources. These decisions may include prioritizing hazards, determining appropriate levels of safety, and identifying and selecting optimal risk reduction strategies. These decisions require making choices among alternatives, choices that may be difficult because they invariably involve trade-offs. Integrating risk assessment and economic analyses can aid decision making by determining the benefits and costs of alternative actions. Risk assessment and economic analysis provide the measurement tools that will facilitate intelligent, informed, risk management and will enable effective and efficient resource allocation decisions.

Costs and Cost Analysis↗
PubMedDate not supplied

MENTOR: a Bayesian Model for prediction of mental retardation in newborns.

Mental retardation (MR) is a diagnosis that is made with extreme caution because of the many uncertainties in its etiology and prognosis. In fact, most physicians will delay the diagnosis for months or years so that substantial evidence is available to rule the diagnosis in or out. MENTOR is a Bayesian Model for the prediction of MR in newborns that provides probabilities for the full range of cognitive outcomes, ranging from MR to superior intelligence. Using the model to confirm clinical judgment could help physicians decide when to proceed with diagnostic tests. The physician and family could discuss the probabilities for MR, borderline, normal, and superior intelligence, given the child's status in infancy and base their decision about additional testing, in part, on this information.

Adolescent↗
PubMed2001

Database reusability in intelligent medical systems.

Reuse or reusability is not a specific, algorithmic, heuristic or only a simple set of guidelines. Database reuse means the use of an existing component--a database entity--in a new context, either elsewhere in the same system or in another system. According to different definitions, an intelligent system is a "power tool for thinking"; but on the other side it is only a kind of information system with built-in knowledge to support decisions made by human experts. Similar conclusions could be made for intelligent medical systems and introduce the database reusability in this environment with a purpose to increase the quality of an intelligent medical system. In the paper the problem of the database reusability will be presented more in detail, especially its integration in an intelligent medical system. Finally, the results of such integration and the benefits for the medicine will be discussed.

Artificial Intelligence↗