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At least 1,117 records · Page 62Linked to original sources
PubMed2004

Trends in environmental lead exposure and troubled youth, 1960-1995: an age-period-cohort-characteristic analysis.

Beginning in the mid-1980s and extending into the early 1990s, the United States experienced a wave of increased youth violence and teenage pregnancy. Nevin (2000) proffers a cohort-based explanation that these trends can be attributed to corresponding trends in gasoline lead exposure during the youths' early years. He contends that the increased consumption of adversely impacted their intelligence levels (IQs). This decreased their intellectual ability, resulted in poor decisions made during their teen and young adult years, and in turn, led to disproportionally high level of criminal involvement and unwed pregnancies among this cohort. The present study evaluates Nevin's causal model by testing the connection between trends in lead exposure and youthful problem behavior with age-period-cohort-characteristic (APCC) models. Our research finds no support for this cohort explanation.

Adolescent↗
PubMed2002

In the Michigan Court of Appeals: In the Matter of AMB, a Minor.

HOLDING: In the accelerating rush to judgment that occurred here, a series of legal errors and missteps following a preliminary hearing compounded what was already an excruciatingly difficult and complex situation. The record strongly suggests that no one involved in the protective proceeding had ever communicated directly with baby AMB's parents and only the testifying doctor had ever seen AMB, an infant with life-threatening disabilities. Thus, a duly enacted statutory process designed to protect individual rights, to allow the intelligent exercise of these rights, and to assure balanced and considered decision making became, instead, the opposite. There was such a relentless disregard for basic principles of law that in its opinion, the Michigan Court of Appeals attempted to assure that this tragedy is never repeated in that state.

Abnormalities, Multiple↗
PubMed1994

When does childhood epilepsy become intractable? Indications and contraindications for epilepsy surgery in children

Surgery for intractable childhood epilepsy may be of great benefit; however, there is a natural tendency for many children with intractable epilepsy to gradually achieve seizure control over years of follow-up. This evolution is much more likely if intelligence is normal. Progressive deterioration in intelligence, school performance, or behavior may be more important in the decision for early surgery than are the seizures themselves. Indications for epilepsy surgery in infancy are most uncertain. Arguments for and against epilepsy surgery in children are presented.

Journal Article↗
PubMed2005

Transductive machine learning for reliable medical diagnostics.

In the past decades Machine Learning tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity, and specificity), they are mostly not being used in practice. One reason for this is that it is difficult to obtain an unbiased estimation of diagnose's reliability. We discuss how reliability of diagnoses is assessed in medical decision making and propose a general framework for reliability estimation in Machine Learning, based on transductive inference. We compare our approach with a usual (Machine Learning) probabilistic approach as well as with classical stepwise diagnostic process where reliability of diagnose is presented as its posttest probability. The proposed transductive approach is evaluated on several medical data sets from the UCI (University of California, Irvine) repository as well as on a practical problem of clinical diagnosis of the coronary artery disease. In all cases significant improvements over existing techniques are achieved.

Adult↗
PubMed1997

Formalisation for decision support in anaesthesiology.

This paper reports on research for decision support for anaesthesiologists at the University Hospital in Groningen, the Netherlands. Based on CAROLA, an existing automated operation documentation system, we designed a support environment that will assist in real-time diagnosis. The core of the work presented here consists of a knowledge base (containing anaesthesiological knowledge) and a diagnosis system. The knowledge base is specified in the logic-based formal specification language AFSL. This leads to a powerful and precise treatment of knowledge structuring and data abstraction.

Anesthesiology↗
PubMed1991

Neural network analysis of serial cardiac enzyme data. A clinical application of artificial machine intelligence.

There has been a recent resurgence of interest in the study and application of computerized neural networks within the broad field of artificial intelligence. These "intelligent machines" are modeled after biological nervous systems and are fundamentally different from the many computerized expert systems that previously have been introduced as clinical decision-making aids. The authors describe a neural network designed and trained to predict the probability of acute myocardial infarction (AMI) based on the analysis of paired sets of cardiac enzymes. The neural network predicted 24 of 24 (100%) AMIs and 27 of 29 (93%) No-AMIs when compared with a pathologist's interpretation of the patient's laboratory data (P less than 0.000001). The authors attempted to validate the network's diagnoses by two independent methods. When compared with echocardiogram and EKG for diagnosis of AMI, the neural network agreed with the cardiologist's interpretation in 12 of 14 (86%) AMIs and 1 of 3 (33%) No-AMIs, but the correlation was not statistically significant. Using autopsy outcome for validation, the neural network agreed with the anatomic evidence in 24 of 26 (92%) AMIs and 4 of 6 (67%) No-AMIs (P = 0.001). The authors conclude that neural networks can be successfully applied to the analysis of cardiac enzyme data and suggest that broader applications exist within the domain of clinical decision support.

Amyloidosis↗
PubMedDate not supplied

A knowledge-based information system for advice in the crisis management of the patient with burns.

A knowledge-based information system that has been designed to be used as an electronic advisor to guide in fluid resuscitation and in the management of the most frequently occurring complications during the first 48 hours after burn injury is described. The system was also developed for training physicians and nurses and may eventually be used for peer review of the management of patients in the burn unit. Ten data screens are used for entry of the administrative data, the clinical background, and the monitored data. The latter include tables for recording fluid therapy and laboratory results. The knowledge base consists of a series of heuristic decision rules that were formulated by a burn care expert and that express the Uppsala fluid resuscitation program to prevent burn shock. The data recorded for a patient are compared with the data in the knowledge base, and the appropriate conclusions are generated. The system's conclusions, the fluid and ventilation prescription, and other required patient management measures are then displayed as a report. The underlying reasoning for each case may be explored by means of the system's explanation facility. The system has been successfully validated by 125 hypothetic cases that represent typical situations of patients with severe burns.

Adult↗
PubMed2003

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Adolescent↗
PubMed2004

Optimization of rifamycin B fermentation in shake flasks via a machine-learning-based approach.

Rifamycin B is an important polyketide antibiotic used in the treatment of tuberculosis and leprosy. We present results on medium optimization for Rifamycin B production via a barbital insensitive mutant strain of Amycolatopsis mediterranei S699. Machine-learning approaches such as Genetic algorithm (GA), Neighborhood analysis (NA) and Decision Tree technique (DT) were explored for optimizing the medium composition. Genetic algorithm was applied as a global search algorithm while NA was used for a guided local search and to develop medium predictors. The fermentation medium for Rifamycin B consisted of nine components. A large number of distinct medium compositions are possible by variation of concentration of each component. This presents a large combinatorial search space. Optimization was achieved within five generations via GA as well as NA. These five generations consisted of 178 shake-flask experiments, which is a small fraction of the search space. We detected multiple optima in the form of 11 distinct medium combinations. These medium combinations provided over 600% improvement in Rifamycin B productivity. Genetic algorithm performed better in optimizing fermentation medium as compared to NA. The Decision Tree technique revealed the media-media interactions qualitatively in the form of sets of rules for medium composition that give high as well as low productivity.

Actinomycetales↗
PubMedDate not supplied

PDRD--a computer-based primary care decision support system.

This article identifies the current stage of development and assessment of computer-assisted decision support systems in the domain of general practice. Physician's Desk Reference and Database (PDRD), an electronic medical database, is presented, and a plan for assessment of PDRD is briefly discussed.

Artificial Intelligence↗
PubMed1998

Syllabic strength and lexical boundary decisions in the perception of hypokinetic dysarthric speech.

This investigation evaluated a possible source of reduced intelligibility in hypokinetic dysarthric speech, namely the mismatch between listeners' perceptual strategies and the acoustic information available in the dysarthric speech signal. A paradigm of error analysis was adopted in which listener transcriptions of phrases were coded for the presence and type of word boundary errors. Seventy listeners heard 60 phrases produced by speakers with hypokinetic dysarthria. The six-syllable phrases alternated strong and weak syllables and ranged in length from three to five words. Lexical boundary violations were defined as erroneous insertions or deletions of lexical boundaries that occurred either before strong or before weak syllables. A total of 1596 lexical boundary errors in the listeners' transcriptions was identified unanimously by three independent judges. The pattern of errors generally conformed with the predictions of the Metrical Segmentation Strategy hypothesis [Cutler and Norris, J. Exp. Psychol. 14, 113-121 (1988)] which posits that listeners attend to strong syllables to identify word onsets. However, the strength of adherence to this pattern varied across speakers. Comparison of acoustic evidence of syllabic strength to lexical boundary error patterns revealed a source of intelligibility deficit associated with this particular type of dysarthric speech pattern.

Adult↗
PubMed1985

A decision tree approach for the application of drug metabolism and kinetic studies to in vivo and in vitro toxicological and pharmacological testing.

The integration of toxicological and other biological findings with information on drug metabolism and pharmacokinetics is often very important for rational decision making in safety evaluation programmes. This goal is unlikely to be achieved by conducting a routine package of inflexibly defined drug metabolism and pharmacokinetic test protocols for each new chemical. Rather, an intelligent selection of experiments based on the known properties of a chemical is required. A series of decision trees are proposed which serve as an aide memoire in the choice of appropriate drug metabolism and pharmacokinetic experiments. These decision trees cover the physicochemical properties of a chemical, data on animal and human pharmacology and toxicology, and environmental information relevant to possible contamination. In many cases, drug metabolism and pharmacokinetic factors are an important prerequisite to the design of in vitro tests that are relevant to the in vivo situation. A scheme is provided to assist the identification of appropriate conditions for the in vitro testing of individual chemicals.

Animals↗
PubMedDate not supplied

Computerized brain atlases as decision support systems: a methodological approach.

This paper deals with the development of computerized brain atlases addressing both research and clinical needs. The authors analyze in detail the potential of these systems and discuss the capabilities and limitations of the digital atlases currently being developed around the world. The authors propose to reconsider the concept of a brain atlas, regarding both its content, and the way it has to be used and managed in order to set up more effective cooperation between the user and the system. Particular emphasis is placed on extensibility and reuse issues. which are critical in this rapidly evolving field. These orientations result from both the authors' experience and the analysis of current trends in the field of neuroimaging. The general methodology is illustrated with examples related to computer aided surgical planning.

Artificial Intelligence↗
PubMed1997

Representing and developing temporally abstracted knowledge as a means towards facilitating time modeling in medical decision-support systems.

The utilization of the appropriate level of temporal abstraction is an important aspect of time modeling. We discuss some aspects of the relation of temporal abstraction to important knowledge engineering parameters such as model correctness, ease of model specification, knowledge availability, query completeness, inference tractability, and semantic clarity. We propose that versatile and efficient time-modeling formalisms should encompass ways to represent and reason at more than one level of abstraction, and we discuss such a hybrid formalism. Although many research efforts have concentrated on the automation of specific temporal abstractions, much research needs to be done in understanding and developing provably optimal abstractions. We provide an initial framework for studying this problem in a manner that is independent of the particular problem domain and knowledge representation, and suggest several research challenges that appear worth pursuing.

Artificial Intelligence↗
PubMed2002

Government policies, industry/economics, social trends, and educational opportunities in 'women's decisions to work outside versus inside the home.

Research on the career decision processes of women has focused primarily on internal considerations such as mathematical ability, intelligence, and self-esteem; however, the external environment also has an influence on these processes. To date, the environmental factors have primarily been researched separately, if at all. They include government policies, industry/economics, social trends/expected sex roles, and educational opportunities. This pilot study investigated these four external factors simultaneously. In addition, a survey of literature on career decision indicated such research to be based on a limited pool of highly educated, management/professional women. This study also uses a wider group including housewives, blue collar, and pink collar women.

Adult↗
PubMed1991

Integrated approach for designing medical decision support systems with knowledge extracted from clinical databases by statistical methods.

In clinical research data is often studied by a particular method without previous analysis of quality or semantic contents which could link clinical database and data analytical (e.g. statistical) procedures. In order to avoid bias caused by this situation, we propose that the analysis of medical data should be divided into two main steps. In the first one we concentrate on conducting the quality, semantic and structure analyses. In the second step our aim is to build an appropriate dictionary of data analysis methods for further knowledge extraction. Methods like robust statistical techniques, procedures for mixed continuous and discrete data, fuzzy linguistic approach, machine learning and neural networks can be included. The results may be evaluated both using test samples and applying other relevant data-analytical techniques to the particular problem under the study.

Artificial Intelligence↗
PubMed1999

A development protocol for a diagnostic DSS.

A diagnostic decision support system (DSS) in medicine is an expert system that aids the physician in the determination of the diagnosis based on findings and test results. The DSS can be divided into 2 different types of components: the knowledge component and the information system component. Methods from software engineering, knowledge engineering and management are combined into a dynamic development cycle that allows stepwise update and refinement. The development of the knowledge component is based on knowledge engineering. In the starting phases, rapid prototyping is convenient to determine and evaluate the specification. The content of the knowledge base is frequently updated during its life time. For this purpose, a knowledge modelling protocol is supplied.

Artificial Intelligence↗
PubMed1995

Evaluation of a decision support system in a medical environment.

This paper describes the impact of a decision support system on the quality of recorded diagnoses and the completeness of medical records. The assumption is that for quality assessment purposes enough data have to be recorded in an electronic medical record so that diagnostic decisions can be justified. The hypotheses were tested that active decision support will lead to better quality recorded diagnoses and more complete medical records. Three groups of ten GPs were presented with 10 cases each. The GPs had to enter the data about these cases in a GP information system. One group of GPs was not supported. The second group was presented with the ICHPPC-II-Defined criteria that had to be fulfilled when a diagnosis was entered. In a third group, the GPs were asked those data that were needed to justify an entered diagnosis (active support). It could be shown that the last group of GPs entered better quality diagnoses than the other two groups. These latter groups also entered so little data that most of their diagnoses could not be justified. It is concluded from the study that only active decision support will lead to better diagnoses and a more complete medical record that can be used for quality assessment.

Artificial Intelligence↗