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At least 667 records · Page 37Linked to original sources
PubMed2005

Interpreting procedures from descriptive guidelines.

Errors in clinical practice guidelines may translate into errors in real-world clinical practice. The best way to eliminate these errors is to understand how they are generated, thus enabling the future development of methods to catch errors made in creating the guideline before publication. We examined the process by which a medical expert from the American College of Physicians (ACP) created clinical algorithms from narrative guidelines, as a case study. We studied this process by looking at intermediate versions produced during the algorithm creation. We identified and analyzed errors that were generated at each stage, categorized them using Knuth's classification scheme, and studied patterns of errors that were made over the set of algorithm versions that were created. We then assessed possible explanations for the sources of these errors and provided recommendations for reducing the number of errors, based on cognitive theory and on experience drawn from software engineering methodologies.

Artificial Intelligence↗
PubMed2005

A review on the integration of artificial intelligence into coastal modeling.

With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

Artificial Intelligence↗
PubMed2004

GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures.

Bank failure prediction is an important issue for the regulators of the banking industries. The collapse and failure of a bank could trigger an adverse financial repercussion and generate negative impacts such as a massive bail out cost for the failing bank and loss of confidence from the investors and depositors. Very often, bank failures are due to financial distress. Hence, it is desirable to have an early warning system (EWS) that identifies potential bank failure or high-risk banks through the traits of financial distress. Various traditional statistical models have been employed to study bank failures [J Finance 1 (1975) 21; J Banking Finance 1 (1977) 249; J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073]. However, these models do not have the capability to identify the characteristics of financial distress and thus function as black boxes. This paper proposes the use of a new neural fuzzy system [Foundations of neuro-fuzzy systems, 1997], namely the Generic Self-organising Fuzzy Neural Network (GenSoFNN) [IEEE Trans Neural Networks 13 (2002c) 1075] based on the compositional rule of inference (CRI) [Commun ACM 37 (1975) 77], as an alternative to predict banking failure. The CRI based GenSoFNN neural fuzzy network, henceforth denoted as GenSoFNN-CRI(S), functions as an EWS and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. The interaction between the selected features is captured in the form of highly intuitive IF-THEN fuzzy rules. Such easily comprehensible rules provide insights into the possible characteristics of financial distress and form the knowledge base for a highly desired EWS that aids bank regulation. The performance of the GenSoFNN-CRI(S) network is subsequently benchmarked against that of the Cox's proportional hazards model [J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073], the multi-layered perceptron (MLP) and the modified cerebellar model articulation controller (MCMAC) [IEEE Trans Syst Man Cybern: Part B 30 (2000) 491] in predicting bank failures based on a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed-bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the GenSoFNN-CRI(S) network as a bank failure classification and EWS is encouraging.

Accidents↗
PubMed2006

Modular learning models in forecasting natural phenomena.

Modular model is a particular type of committee machine and is comprised of a set of specialized (local) models each of which is responsible for a particular region of the input space, and may be trained on a subset of training set. Many algorithms for allocating such regions to local models typically do this in automatic fashion. In forecasting natural processes, however, domain experts want to bring in more knowledge into such allocation, and to have certain control over the choice of models. This paper presents a number of approaches to building modular models based on various types of splits of training set and combining the models' outputs (hard splits, statistically and deterministically driven soft combinations of models, 'fuzzy committees', etc.). An issue of including a domain expert into the modeling process is also discussed, and new algorithms in the class of model trees (piece-wise linear modular regression models) are presented. Comparison of the algorithms based on modular local modeling to the more traditional 'global' learning models on a number of benchmark tests and river flow forecasting problems shows their higher accuracy and transparency of the resulting models.

Artificial Intelligence↗
PubMed1997

Effective data validation of high-frequency data: time-point-, time-interval-, and trend-based methods.

Real-time systems for monitoring and therapy planning, which receive their data from on-line monitoring equipment and computer-based patient records, require reliable data. Data validation has to utilize and combine a set of fast methods to detect, eliminate, and repair faulty data, which may lead to life-threatening conclusions. The strength of data validation results from the combination of numerical and knowledge-based methods applied to both continuously-assessed high-frequency data and discontinuously-assessed data. Dealing with high-frequency data, examining single measurements is not sufficient. It is essential to take into account the behavior of parameters over time. We present time-point-, time-interval-, and trend-based methods for validation and repair. These are complemented by time-independent methods for determining an overall reliability of measurements. The data validation benefits from the temporal data-abstraction process, which provides automatically derived qualitative values and patterns. The temporal abstraction is oriented on a context-sensitive and expectation-guided principle. Additional knowledge derived from domain experts forms an essential part for all of these methods. The methods are applied in the field of artificial ventilation of newborn infants. Examples from the real-time monitoring and therapy-planning system VIE-VENT illustrate the usefulness and effectiveness of the methods.

Artificial Intelligence↗
PubMed1998

A pragmatic implementation of medical temporal reasoning for clinical medicine.

In most development tools for knowledge-based systems object-property-value-triples are the basic ontological representation. We have extended this approach for temporal reasoning by replacing the values with time-objects. A time-object is a combination of a value with a time-point or a time-interval. By preserving the object-property-value-triples basic artificial intelligence techniques can be applied without modifications in the temporal case. The greater complexity of temporal inferences is compensated by a blackboard control architecture enabling small knowledge-sources. The control of the strategic reasoning is in accordance with the select and test model. A prototype of a knowledge-based advisor for the acute radiation syndrome has been implemented and partially evaluated. Due to the affected proliferative tissues it is a strongly time-oriented medical domain.

Acute Disease↗
PubMed1997

Evaluation of automatically learned intelligent alarm systems.

In this contribution it is investigated whether a combination of mathematical simulation and inductive machine learning can replace the usual knowledge elicitation techniques. To test this a domain was selected for which knowledge based systems had a high performance: intelligent alarm systems. A mathematical model of a breathing circuit and ventilated patient was implemented in PSpice. Airway pressure, gas flows and CO2 concentration were simulated with this model, during normal functioning of the breathing circuit and during several mishaps, for a wide range of simulated patients. With an inductive machine learning program, classification trees were created from the simulated patient data. The classification trees described each breathing circuit mishap in terms of changes in signal feature values with respect to the normal situation and were implemented as alarm system knowledge bases. The alarm systems were tested with data measured at 17 mechanically ventilated animals. During ventilation of the animals several mishaps were introduced. For each animal, 93-100% of all mishaps could be detected correctly by the alarm systems. The false alarm rate ranged on average from one false alarm per h to one false alarm every 2.5 h. It was concluded that the suggested approach to knowledge elicitation was successful.

Animals↗
PubMed1999

The impact of a quasi-market on sexually transmitted disease services in the UK.

This article reports the results of a study of the impact of quasi-market reforms on sexually transmitted disease (STD) services in one UK health region. An internal or quasi-market was introduced into UK health care in the 1991 reforms of the National Health Service (NHS). Health authorities (HAs) and general practitioner fundholders were given major new responsibilities for purchasing (later called commissioning) health services. The NHS quasi-market was designed to address recurrent difficulties in acute health services by promoting efficiency and consumer choice. The arrangements for commissioning STD services are important because these diseases are major threats to public health and HAs face a number of constraints in bringing about service changes through market mechanisms. In the UK, STD services are provided on a self-referral and confidential basis; patients experience STDs as stigmatizing and often have low expectations of service and little desire for involvement in commissioning decisions. HAs have only limited routine intelligence about STD services and little or no choice of local providers. This study adopted a qualitative case-study approach to examine HA commissioning of STD services. The study found that the introduction of the NHS quasi-market did not equip HAs with mechanisms for bringing about change in STD service provision or STD-related health outcomes. The findings are consistent with other recent studies of HA commissioning and provide further cumulative evidence of the limits to HA leverage in the NHS quasi-market. The study concludes that the commissioning of STD services is likely to remain a low priority in the new NHS structures based on primary care groups.

Communicable Disease Control↗
PubMed2003

Model selection methodology in supervised learning with evolutionary computation.

The expressive power, powerful search capability, and the explicit nature of the resulting models make evolutionary methods very attractive for supervised learning applications in bioinformatics. However, their characteristics also make them highly susceptible to overtraining or to discovering chance relationships in the data. Identification of appropriate criteria for terminating evolution and for selecting an appropriately validated model is vital. Some approaches that are commonly applied to other modelling methods are not necessarily applicable in a straightforward manner to evolutionary methods. An approach to model selection is presented that is not unduly computationally intensive. To illustrate the issues and the technique two bioinformatic datasets are used, one relating to metabolite determination and the other to disease prediction from gene expression data.

Algorithms↗
PubMed2000

Guideline-based careflow systems.

This paper describes a methodology for achieving an efficient implementation of clinical practice guidelines. Three main steps are illustrated: knowledge representation, model simulation and implementation within a health care organisation. The resulting system can be classified as a 'guideline-based careflow management system'. It is based on computational formalisms representing both medical and health care organisational knowledge. This aggregation allows the implementation of a guideline, not only as a simple reminder, but also as an 'organiser' that facilitates health care processes. As a matter of fact, the system not only suggests the tasks to be performed, but also the resource allocation. The methodology initially comprehends a graphical editor, that allows an unambiguous representation of the guideline. Then the guideline is translated into a high-level Petri net. The resources, both human and technological necessary for performing guideline-based activities, are also represented by means of an organisational model. This allows the running of the Petri net for simulating the implementation of the guideline in the clinical setting. The purpose of the simulation is to validate the careflow model and to suggest the optimal resource allocation before the careflow system is installed. The final step is the careflow implementation. In this phase, we show that the 'workflow management' technology, widely used in business process automation, may be transferred to the health care setting. This requires augmenting the typical workflow management systems with the flexibility and the uncertainty management, typical of the health care processes. For illustrating the proposed methodology, we consider a guideline for the management of patients with acute ischemic stroke.

Artificial Intelligence↗
PubMed2000

Intelligent analysis of clinical time series: an application in the diabetes mellitus domain.

This paper describes the application of a method for the intelligent analysis of clinical time series in the diabetes mellitus domain. Such a method is based on temporal abstractions and relies on the following steps: (i) 'pre-processing' of raw data through the application of suitable filtering techniques: (ii) 'extraction' from the pre-processed data of a set of abstract episodes (temporal abstractions); and (iii) 'post-processing' of temporal abstractions; the post-processing phase results in a new set of features that embeds high level information on the patient dynamics. The derived features set is used to obtain new knowledge through the application of machine learning algorithms. The paper describes in detail the application of this methodology and presents some results obtained on simulated data and on a data-set of four diabetic patients monitored for > 1 year.

Artificial Intelligence↗
PubMed2001

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data.

Combining the predictions of a set of classifiers has shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. There are many methods for combining the predictions given by component classifiers. We introduce a new method that combine a number of component classifiers using a Bayesian network as a classifier system given the component classifiers predictions. Component classifiers are standard machine learning classification algorithms, and the Bayesian network structure is learned using a genetic algorithm that searches for the structure that maximises the classification accuracy given the predictions of the component classifiers. Experimental results have been obtained on a datafile of cases containing information about ICU patients at Canary Islands University Hospital. The accuracy obtained using the presented new approach statistically improve those obtained using standard machine learning methods.

Algorithms↗
PubMed2001

Knowledge discovery approach to automated cardiac SPECT diagnosis.

The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses.

Artificial Intelligence↗
PubMed2003

A multi-agent system approach for monitoring the prescription of restricted use antibiotics.

Hospitals have a specified set of antibiotics for restricted use (ARU), very expensive, which are only recommended for special pathologies. The pharmacy department daily checks the prescription of this kind of antibiotics since it is often the case that, after a careful analysis, one can get the same therapeutic effects by using normal antibiotics which are much cheaper and usually less aggressive. In this paper, we describe a multi-agent system to help in the revision of medical prescriptions containing antibiotics of restricted use. The proposed approach attaches an agent to each patient which is responsible of checking different medical aspects related to his/her prescribed therapy. A pharmacy agent is responsible for analyzing it and suggesting alternative antibiotic treatments. All these agents are integrated in a hospital distributed scenario composed by many different kinds of software and human agents. This patient-centered multi-agent scenario is specified using the design methodology of Electronic Institutions.

Anti-Bacterial Agents↗
PubMed1999

An evaluation of intelligent prognostic systems for colorectal cancer.

In this paper we describe attempts at building a robust model for predicting the length of survival of patients with colorectal cancer. The aim of the research, reported in this paper, is to study the effective utilisation of artificial intelligence techniques in the medical domain. We suggest that an important research objective of proponents of intelligent prognostic systems must be to evaluate the additionality that AI techniques can bring to an already well-established field of medical prognosis. Towards this end, we compare a number of different AI techniques that lend themselves to the task of predicting survival in colorectal cancer patients. We describe the pros and cons of each of these methods using the usual metrics of accuracy and perspicuity. We then present the notion of intelligent hybrid systems and evaluate the role that they may potentially play in developing robust prognostic models. In particular we evaluate a hybrid system that utilises the k Nearest Neighbour technique in conjunction with Genetic Algorithms. We describe a number of innovations used within this hybrid paradigm used to build the prognostic model. We discuss the issue of censored patients and how this issue can be tackled within the various models used. In keeping with our objective of studying the additionality that AI techniques bring to building prognostic models, we use Cox's regression as a standard and compare each AI technique with it, attempting to discover their capabilities in enhancing prognostic methods in medicine. In doing so we address two main questions--which model fits the data best?, and are the results obtained by the various AI techniques significantly different from those of Cox's regression? We conclude this paper by discussing future enhancements to the work presented and lessons learned from the study to date.

Age Factors↗
PubMed1999

Refining instructional text generation after evaluation.

In this paper, we describe how user-adapted explanations about drug prescriptions can be generated from already existing data sources. We start by illustrating the two-step approach employed in the first version of the natural language generator and the limitations of generated texts, that we discovered through analytical and empirical evaluations. We claim that, although style refinement would be needed in these texts, particular care should be devoted to implementing some of the persuasion techniques that doctors employ in their explanations. This would require either thoroughly revising the text planning techniques employed or converting to a multistep generation architecture. We justify why we selected this second alternative and propose some heuristics to repair problems found in the first version of the generator. Some final considerations about the advantages of this approach and the possibility of generalizing it to other domains conclude the paper.

Adrenergic beta-Antagonists↗
PubMed2001

Generation of an intelligent medical system, using a real database, to diagnose bacterial infection in hospitalized patients.

The initial diagnosis of bacterial infections in the absence of laboratory microbiological data requires physicians to use clinical algorithms based on symptoms, patient history and infection site. Optimization of such algorithms would be achieved by including as many variables associated with bacterial infection as possible. Demographic data are easily available and frequently used to sub-group human populations. A prospective investigation was, therefore, undertaken to examine the influence of demographic variables on bacterial infection rates, using data obtained from 173 patients presenting to Albert Einstein Medical Center. Data was randomly selected from 149 of these patients and used to generate fuzzy rules to model an intelligent medical system. To test the accuracy of this system at determining bacterial infection, based solely on demographic data, the program was given the remaining 24 patients' information. All 18 patients with either streptococcal, staphylococcal or Escherichia coli infections were correctly diagnosed. Non-E.coli GNR were misdiagnosed as E. coli infections in two patients resulting in an overall prediction rate for the 24 patients of 91.66%. This study suggests that the direct correlation of demographic variables with a predisposition to bacterial infection allow the design of an intelligent medical system, which shows great future potential as a diagnostic tool for all physicians.

Adult↗