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At least 991 records · Page 55Linked to original sources
PubMed1993

Arden Syntax as a standard for knowledge bases in the clinical chemistry laboratory.

Arden Syntax, a standard specification for defining and sharing modular health knowledge bases, is introduced in the clinical chemistry laboratory. A decision support system is constructed and integrated with a laboratory information system for validation of test results with the delta check method. Adopting the Arden Syntax makes it easier to share knowledge bases between laboratories. Tools for handling Arden Syntax knowledge bases and methods for decision support system implementation and integration with a laboratory information system are available today.

Artificial Intelligence↗
PubMed2004

GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines.

The Guideline Interchange Format (GLIF) is a model for representation of sharable computer-interpretable guidelines. The current version of GLIF (GLIF3) is a substantial update and enhancement of the model since the previous version (GLIF2). GLIF3 enables encoding of a guideline at three levels: a conceptual flowchart, a computable specification that can be verified for logical consistency and completeness, and an implementable specification that is intended to be incorporated into particular institutional information systems. The representation has been tested on a wide variety of guidelines that are typical of the range of guidelines in clinical use. It builds upon GLIF2 by adding several constructs that enable interpretation of encoded guidelines in computer-based decision-support systems. GLIF3 leverages standards being developed in Health Level 7 in order to allow integration of guidelines with clinical information systems. The GLIF3 specification consists of an extensible object-oriented model and a structured syntax based on the resource description framework (RDF). Empirical validation of the ability to generate appropriate recommendations using GLIF3 has been tested by executing encoded guidelines against actual patient data. GLIF3 is accordingly ready for broader experimentation and prototype use by organizations that wish to evaluate its ability to capture the logic of clinical guidelines, to implement them in clinical systems, and thereby to provide integrated decision support to assist clinicians.

Artificial Intelligence↗
PubMed2002

Efficiency and safety of chemotherapy plans for children: CATIPO--a nationwide approach.

Chemotherapy is an important component of childhood cancer treatment. Due to the intensity of this therapy, an error in calculating the dosage of the cytostatic drugs would have severe consequences. Therefore, a computer-aided therapy planning system in pediatric oncology (CATIPO) was introduced into routine use in approximately 20 clinics across Germany. The system consists of a knowledge acquisition module for acquiring knowledge about chemotherapy protocols and a decision support module for producing a patient-specific therapy plan. The main benefits of the system are the reduction of errors and saving time during the development of a particular therapy plan. CATIPO's success is based on the enormous demand for this kind of decision support and its development in tight cooperation with future users.

Adolescent↗
PubMed1999

A probabilistic approach to improved antibiotic therapy.

PTA is a decision-theoretic expert system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the intensive care unit. Its underlying probabilistic network model includes knowledge for diagnosing pneumonia on the basis of the likelihood of tracheobronchial-tree colonisation by pathogens, and symptoms and signs actually present in the patient. Optimal antibiotic therapy is selected by balancing the expected efficacy of treatment, which is related to the likelihood of pathogen-specific pneumonias, against costs and side effects of treatment. In this article, the model underlying the system and results of a preliminary evaluation are described.

Anti-Bacterial Agents↗
PubMed2003

Beta edge strands in protein structure prediction and aggregation.

It is well established that recognition between exposed edges of beta-sheets is an important mode of protein-protein interaction and can have pathological consequences; for instance, it has been linked to the aggregation of proteins into a fibrillar structure, which is associated with a number of predominantly neurodegenerative disorders. A number of protective mechanisms have evolved in the edge strands of beta-sheets, preventing the aggregation and insolubility of most natural beta-sheet proteins. Such mechanisms are unfavorable in the interior of a beta-sheet. The problem of distinguishing edge strands from central strands based on sequence information alone is important in predicting residues and mutations likely to be involved in aggregation, and is also a first step in predicting folding topology. Here we report support vector machine (SVM) and decision tree methods developed to classify edge strands from central strands in a representative set of protein domains. Interestingly, rules generated by the decision tree method are in close agreement with our knowledge of protein structure and are potentially useful in a number of different biological applications. When trained on strands from proteins of known structure, using structure-based (Dictionary of Secondary Structure in Proteins) strand assignments, both methods achieved mean cross-validated, prediction accuracies of approximately 78%. These accuracies were reduced when strand assignments from secondary structure prediction were used. Further investigation of this effect revealed that it could be explained by a significant reduction in the accuracy of standard secondary structure prediction methods for edge strands, in comparison with central strands.

Algorithms↗
PubMed2000

Clinical decision-support systems for intensive care units using case-based reasoning.

The artificial intelligence approach used in this work focusses on case-based reasoning techniques for the estimation of medical outcomes and resource utilization. The systems were designed with a view to help medical and nursing personnel to assess patient status, assist in making a diagnosis, and facilitate the selection of a course of therapy. The initial prototype provided information on the closest-matching patient cases to the newest patient admission in an adult intensive care unit (ICU). The system was subsequently re-designed for use in a neonatal ICU. The results of a short clinical pilot evaluation performed in both adult and neonatal units are reported and have led to substantial improvement of the prototype. Future work will include longer-term clinical trials for both adult and neonatal ICUs, once all the software changes have been made to both prototypes in response to the comments of the users made during the preliminary evaluations. To date, the results are very encouraging and physician interest in the potential clinical usefulness of these two systems remains high, and particularly so in the new testing environment in Ottawa.

Adult↗
PubMed1994

Development, evaluation and validation of an intelligent system for the management of labour.

Over the past 4 years our group has developed a prototype intelligent system which applies captured expert knowledge to support clinical decision-making during labour. This chapter presents a review of the system and the progress made to date. The system classifies the same features from the CTG as experienced clinicians using numerical algorithms and a small neural network. This hybrid approach has been shown to obtain a comparable performance with experts. The CTG information, together with the patient information and labour events, are collectively passed to an expert system for processing. The expert system interprets this combined data using a database of over 400 rules which are used to recommend action. Importantly, as the knowledge is rule-based, it allows the system to explain the reasoning which led it to recommend a certain action. In this way, the clinician is not expected to blindly follow the system's recommendations but can reach an informed judgement in the same way they might by discussing the case with an experienced informed colleague. After two internal evaluations had found the system obtained a performance comparable with local experts, an extensive external validation was undertaken. This study involved 17 experts from 16 leading centres within the UK. Each expert and the system reviewed 50 cases twice, at least one month apart which contained those CTGs considered most difficult to interpret selected from a database of 2400 high-risk labours. This study found that the majority of experts agreed well and were consistent in their management of the cases. The system obtained a performance that was indistinguishable from the experts, except it was more consistent, even when used by an engineer with little knowledge of labour management. This study demonstrates the potential for intelligent systems to transform the cardiotocograph from a difficult-to-use, ineffective recorder of fetal heart rate, to an interactive and effective decision support tool capable of raising the skills of staff.

Algorithms↗
PubMed2000

Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression.

The estimate of a multivariate risk is now required in guidelines for cardiovascular prevention. Limitations of existing statistical risk models lead to explore machine-learning methods. This study evaluates the implementation and performance of a decision tree (CART) and a multilayer perceptron (MLP) to predict cardiovascular risk from real data. The study population was randomly splitted in a learning set (n = 10,296) and a test set (n = 5,148). CART and the MLP were implemented at their best performance on the learning set and applied on the test set and compared to a logistic model. Implementation, explicative and discriminative performance criteria are considered, based on ROC analysis. Areas under ROC curves and their 95% confidence interval are 0.78 (0.75-0.81), 0.78 (0.75-0.80) and 0.76 (0.73-0.79) respectively for logistic regression, MLP and CART. Given their implementation and explicative characteristics, these methods can complement existing statistical models and contribute to the interpretation of risk.

Artificial Intelligence↗
PubMed2000

From a urinalysis strategy to an evaluated urine protein expert system.

Urine single protein analysis has developed into a routine method for the screening and monitoring of kidney diseases. In order to support clinical decision making by an interpretative report, a urine protein expert system (UPES) has been developed. Based on a database containing more than 500 excretion patterns, a modular knowledge base was extracted in production rules and implemented in a modern expert system shell. The resulting interpretation system has been thoroughly verified and validated. After the need of interpretation of the complex findings had been documented in a survey, its usability in routine and its knowledge representation was evaluated in 11 hospitals. A user conference confirmed a high quality level of the reports proposed by UPES. It revealed that the problem of automatic data transfer as well as the common definition of diagnostic terms by laboratorians and clinicians play a crucial role for the use of knowledge-based systems in laboratory medicine.

Artificial Intelligence↗
PubMed2005

Neurocognitive skills moderate urban male adolescents' responses to preventive intervention materials.

The present experiment was designed to determine whether individual variation in neurobiological mechanisms associated with substance abuse risk moderated effects of a brief preventive intervention on social competency skills. This study was conducted in collaboration with the ongoing preventive intervention study at Johns Hopkins University Prevention Intervention Research Center (JHU PIRC) within the Baltimore City Public Schools. A subsample (N = 120) of male 9th grade students was recruited from the larger JHU study population. Approximately half of the participants had a current or lifetime diagnosis of CD while the other half had no diagnosis of CD or other reported problem behaviors. Measures of executive cognitive function (ECF), emotional perception and intelligence were administered. In a later session, participants were randomly assigned to either an experimental or control group. The experimental group underwent a facilitated session using excerpted materials from a model preventive intervention, Positive Adolescent Choices Training (PACT), and controls received no intervention. Outcomes (i.e., social competency skills) were assessed using virtual reality vignettes involving behavioral choices as well as three social cognition questionnaires. Poor cognitive and emotional performance and a diagnosis of CD predicted less favorable change in social competency skills in response to the prevention curriculum. This study provides evidence for the moderating effects of neurocognitive and emotional regulatory functions on ability of urban male youth to respond to preventive intervention materials.

Adolescent↗
PubMed1989

Knowledge engineering for clinical consultation programs: modeling the application area.

Developers of computer-based decision-support tools frequently adopt either pattern recognition or artificial intelligence techniques as the basis for their programs. Because these developers often choose to accentuate the differences between these alternative approaches, the more fundamental similarities are frequently overlooked. The principal challenge in the creation of any clinical consultation program - regardless of the methodology that is used - lies in creating a computational model of the application domain. The difficulty in generating such a model manifests itself in symptoms that workers in the expert systems community have labeled "the knowledge-acquisition bottleneck" and "the problem of brittleness". This paper explores these two symptoms and shows how the development of consultation programs based on pattern-recognition techniques is subject to analogous difficulties. The expert systems and pattern recognition communities must recognize that they face similar challenges, and must unite to develop methods that assist with the process of building of models of complex application tasks.

Decision Support Techniques↗
PubMed2026

A comprehensive review of AI innovations for tackling antimicrobial resistance.

Antimicrobial resistance (AMR) represents a major global public health concern, rendering available antimicrobials ineffective and leading to infections that are difficult to treat. Artificial intelligence (AI) has been increasingly applied across the AMR continuum, including resistance prediction, rapid diagnostics, new antimicrobial discovery, drug repurposing, antimicrobial surveillance, and clinical decision support. In this review, we aim to highlight recent developments in the use of artificial intelligence (AI) to address antimicrobial resistance (AMR). In addition, we review computational methods that help interpret genomic, phenomic, clinical, and epidemiological data to support the development of treatment strategies and novel antimicrobial agents. The key issues addressed include data quality, model interpretability, external validation, regulatory requirements, privacy, and fairness. While AI is not a complete solution to AMR, it can certainly strengthen the global AMR response by complementing key areas of AMR such as antimicrobial stewardship, infection prevention, laboratory diagnostics, and global surveillance.

Antimicrobial resistance (AMR)↗
PubMed2005

Decision tree based information integration for automated protein classification.

We propose a novel technique for automatically generating the SCOP classification of a protein structure with high accuracy. We achieve accurate classification by combining the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier. Our technique, based on decision trees, is rooted in machine learning which shows that by judicially employing component classifiers, an ensemble classifier can be constructed to outperform its components. We use two sequence- and three structure-comparison tools as component classifiers. Given a protein structure and using the joint hypothesis, we first determine if the protein belongs to an existing category (family, superfamily, fold) in the SCOP hierarchy. For the proteins that are predicted as members of the existing categories, we compute their family-, superfamily-, and fold-level classifications using the consensus classifier. We show that we can significantly improve the classification accuracy compared to the individual component classifiers. In particular, we achieve error rates that are 3-12 times less than the individual classifiers' error rates at the family level, 1.5-4.5 times less at the superfamily level, and 1.1-2.4 times less at the fold level.

Algorithms↗
PubMed1990

Application of artificial intelligence techniques to a well defined clinical problem: jaundice diagnosis.

Jaundice is a very common medical condition, in which pathophysiological knowledge has been quite well assessed and clinical features are usually well known. However, incorrect conclusions are sometimes reached in medical practice which can lead to serious implications. Thus, jaundice diagnosis appears as one of the medical situations which might be substantially improved by computer assistance. The present study is aimed at describing and discussing in what way a computer program supporting medical decision making in jaundiced patients can be developed on the basis of advanced Artificial Intelligence methods. To this extent methodological problems concerned with the organization and the formalization of medical knowledge have been outlined with some detail. The resulting expert system is expected to become a well assessed and potentially useful tool for both medical decision making and medical education.

Algorithms↗
PubMed2004

Automated protein classification using consensus decision.

We propose a novel technique for automatically generating the SCOP classification of a protein structure with high accuracy. High accuracy is achieved by combining the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier. Our technique is rooted in machine learning which shows that by judicially employing component classifiers, an ensemble classifier can be constructed to outperform its components. We use two sequence- and three structure-comparison tools as component classifiers. Given a protein structure, using the joint hypothesis, we first determine if the protein belongs to an existing category (family, superfamily, fold) in the SCOP hierarchy. For the proteins that are predicted as members of the existing categories, we compute their family-, superfamily-, and fold-level classifications using the consensus classifier. We show that we can significantly improve the classification accuracy compared to the individual component classifiers. In particular, we achieve error rates that are 3-12 times less than the individual classifiers' error rates at the family level, 1.5-4.5 times less at the superfamily level, and 1.1-2.4 times less at the fold level.

Algorithms↗
PubMedDate not supplied

Local irritation/corrosion testing strategies: extending a decision support system by applying self-learning classifiers.

Procedures have been established and tested for the extension of a decision support system (DSS) for the prediction of the local irritation/corrosion potential of chemicals by using self-learning classifiers. The different approaches (decision trees, distances examinations in a multidimensional space, k-nearest-neighbour method) have been implemented, tested and evaluated independently. A combination of all of the established extension approaches was also developed and tested. Self-learning classifiers are constructed "automatically" by a computer, i.e. they are not derived by a human expert, and thus they can be constructed with minimal effort. The classifiers presented here extend the existing DSS in a manner that increased significantly the predictive power of the extended system. However, automatically calculated results of self-learning classifiers are produced by a machine, and a machine is incapable of explaining the toxicological relevance of the results obtained. Thus, these results must be accepted, despite an inability to prove their reliability. Only the mathematical correctness of the method and the prediction rates for suitable test cases can lend some credibility to predictions produced by a computer calculating on a self-learning basis. This may not be adequate for regulatory hazard assessment purposes.

Algorithms↗