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At least 613 records · Page 34Linked to original sources
PubMed2004

Modeling prioritized multicriteria decision making.

We consider the problem of multicriteria decision making (MCDM) in the situation in which there exists a prioritization of criteria. A good example of prioritization among criteria occurs in the case of air travel, where concerns about passenger safety have a higher priority then economic concerns. Tradeoffs between saving on gasoline usage and jeopardizing passenger safety are unacceptable. We show how this prioritization of criteria can be modeled by using importance weights in which the weights associated with the lower priority criteria are related to the satisfaction of the higher priority criteria. We provide some models that allow for the formalization of these prioritized MCDM problems using both the Bellman-Zadeh paradigm for MCDM and the ordered weighted averaging (OWA) operator method.

Algorithms↗
PubMed1995

Allocation of surgical procedures to operating rooms.

Reduction of health care costs is of paramount importance in our time. This paper is a part of the research which proposes an expert hospital decision support system for resource scheduling. The proposed system combines mathematical programming, knowledge base, and database technologies, and what is more, its friendly interface is suitable for any novice user. Operating rooms in hospitals represent big investments and must be utilized efficiently. In this paper, first a mathematical model similar to job shop scheduling models is developed. The model loads surgical cases to operating rooms by maximizing room utilization and minimizing overtime in a multiple operating room setting. Then a prototype expert system which replaces the expertise of the operations research analyst for the model, drives the modelbase, database, and manages the user dialog is developed. Finally, an overview of the sequencing procedures for operations within an operating room is also presented.

Artificial Intelligence↗
PubMed2002

Self-administered decision support tool for triage: results of a retrospective study.

BACKGROUND: This study was designed to evaluate the safety of a self-administered triage tool. MATERIALS: Ninety-five patients older than 14 years who presented to Memorial Hermann Hospital emergency room (ER) with chief complaint of abdominal pain were included in the study. Their ER disposition and final diagnoses were logged into a database. The assigned disposition and top three diagnoses by the triage tool for each patient were also logged into the database. An emergency physician blinded to the actual disposition reviewed all cases and provided a disposition for each patient. RESULTS: The system disposed 51.1% of cases appropriately and under-disposed 4.4% of cases. Comparison between the system and the emergency physician shows that all cases under-disposed by the system are also under-disposed by the physician.

Abdominal Pain↗
PubMed1998

Case based diagnosis in histopathology of breast tumours.

Relevant knowledge and decision making process in histopathology is mostly included in typical pathological cases encountered by the expert. In this article we address the issue of exploiting this knowledge in the diagnosis process. We present the first steps of a Case-Based-Reasoning (CBR) system that uses the previously resolved pathological cases in order to facilitate decision making and diagnosis formulation of a new case. The work has been performed in two phases. Firstly, an object-oriented model of the domain was developed and 35 pathological cases of breast tumours were represented within this model. Secondly, the functional architecture of the CBR system was designed and the main procedure, the selection of similar cases, was achieved. The selection procedure is based on an original similarity measure that takes into account both semantic and structural resemblances and differences between the cases. A first evaluation of the system was performed on several cases of the data base. The interest of the CBR approach in situations where heuristic rules cannot be clearly defined is discussed.

Algorithms↗
PubMed1999

Classification algorithms applied to narrative reports.

Narrative text reports represent a significant source of clinical data. However, the information stored in these reports is inaccessible to many automated decision support systems. Data mining techniques can assist in extracting information from narrative data. Multiple classification methods, such as rule generation, decision trees, Bayesian classifiers, and information retrieval were used to classify a set of 200 chest X-ray reports according to 6 clinical conditions indicated. A general-purpose natural language processor was used to convert the narrative text into a coded form that could be used by the classification algorithms. Significant differences in performance were found between algorithms. The best performing algorithm applied to the processor output was significantly better than information retrieval applied to raw text. Predictor variables from the coded processor output were limited to avoid overfitting. Methods that limited by domain knowledge performed significantly better than those that limited by conditional probabilities of the variables in the training set. Algorithms were also shown to be dependent on training set size.

Algorithms↗
PubMed2005

Methods for reasoning from geometry about anatomic structures injured by penetrating trauma.

This paper presents the methods used for three-dimensional (3D) reasoning about anatomic structures affected by penetrating trauma in TraumaSCAN-Web, a platform-independent decision support system for evaluating the effects of penetrating trauma to the chest and abdomen. In assessing outcomes for an injured patient, TraumaSCAN-Web utilizes 3D models of anatomic structures and 3D models of the regions of damage associated with stab and gunshot wounds to determine the probability of injury to anatomic structures. Probabilities estimated from 3D reasoning about affected anatomic structures serve as input to a Bayesian network which calculates posterior probabilities of injury based on these initial probabilities together with available information about patient signs, symptoms and test results. In addition to displaying textual descriptions of conditions arising from penetrating trauma to a patient, TraumaSCAN-Web allows users to visualize the anatomy suspected of being injured in 3D, in this way providing a guide to its reasoning process.

Artificial Intelligence↗
PubMed2004

Automated soil resources mapping based on decision tree and Bayesian predictive modeling.

This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.

Algorithms↗
PubMed2000

Planning treatment of ischemic heart disease with partially observable Markov decision processes.

Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead, they are very often dependent and interleaved over time. This is mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framework of partially observable Markov decision processes (POMDPs) developed and used in the operations research, control theory and artificial intelligence communities is particularly suitable for modeling such a complex decision process. In this paper, we show how the POMDP framework can be used to model and solve the problem of the management of patients with ischemic heart disease (IHD), and demonstrate the modeling advantages of the framework over standard decision formalisms.

Cost-Benefit Analysis↗
PubMed2004

Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods.

OBJECTIVE: In this work, methods utilizing supervised and unsupervised machine learning are applied to analyze radiologically derived morphological and calculated kinetic tumour features. The features are extracted from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) time-course data. MATERIAL: The DCE-MRI data of the female breast are obtained within the UK Multicenter Breast Screening Study. The group of patients imaged in this study is selected on the basis of an increased genetic risk for developing breast cancer. METHODS: The k-means clustering and self-organizing maps (SOM) are applied to analyze the signal structure in terms of visualization. We employ k-nearest neighbor classifiers (k-nn), support vector machines (SVM) and decision trees (DT) to classify features using a computer aided diagnosis (CAD) approach. RESULTS: Regarding the unsupervised techniques, clustering according to features indicating benign and malignant characteristics is observed to a limited extend. The supervised approaches classified the data with 74% accuracy (DT) and providing an area under the receiver-operator-characteristics (ROC) curve (AUC) of 0.88 (SVM). CONCLUSION: It was found that contour and wash-out type (WOT) features determined by the radiologists lead to the best SVM classification results. Although a fast signal uptake in early time-point measurements is an important feature for malignant/benign classification of tumours, our results indicate that the wash-out characteristics might be considered as important.

Artificial Intelligence↗
PubMed2006

Topology of resultant networks shaped by evolutionary pressure.

Understanding the topology of complex systems abstracted to networks is important for unraveling their functional capabilities. Many such networks follow the small-world and scale-free regimes. Several models of artificially growing networks lead to this observed network topology. Most previously proposed models for growing networks, such as rich-get-richer and duplication-divergence, produce realistic network topologies but do not consider the effects of exogenous forces such as optimization for adaptation in shaping network topology. It is likely that such forces have shaped complex systems throughout their evolution. To develop further insights into possible mechanisms that shape networks, a model that uses several previously proposed network growth algorithms was developed to grow networks that adapt under exogenous stress. A decision tree problem was used to generate a complex Boolean function. Growing networks were required to adapt to correctly decode this function using an evolutionary selection process. Under this growth regimen all growing network models are similarly adaptable. The newly added nodes tend to cluster into pathways emanating from few inputs, regardless of the growth algorithm. Distribution of redundant pathways from inputs to the output follow a power-law function with a scaling exponent (approximately 1.3). Similar distribution of redundant pathways was observed from inputs in a cell signaling network and an air traffic control network. A flat distribution of redundant pathways from inputs was observed in growing networks that do not attempt to adapt. This analysis provides initial insights into distribution of pathways in naturally evolving complex systems that have defined input-output relationships.

Algorithms↗
PubMed2001

An implementation framework for GEM encoded guidelines.

Access to timely decision support information is critical for delivery of high-quality medical care. Transformation of clinical knowledge that is originally expressed in the form of a guideline to a computable format is one of the main obstacles to the integration of knowledge sharing functionality into computerized clinical systems. The Guideline Element Model (GEM) provides a methodology for such a transformation. Although the model has been used to store heterogeneous guideline knowledge, it is important to demonstrate that GEM markup facilitates guideline implementation. This report demonstrates the feasibility of implementation of GEM-encoded guideline recommendations using Apache Group s Cocoon Web Publishing Framework. We further demonstrate how XML-based programming allows for maintaining the separation of guideline content from processing logic and from presentation format. Finally, we analyze whether the guideline authors original intent has been sufficiently captured and conveyed to the end user.

Artificial Intelligence↗
PubMed2006

A decision support system based on support vector machines for diagnosis of the heart valve diseases.

In this paper, a decision support system that classifies the Doppler signals of the heart valve to two classes (normal and abnormal) is presented to support the cardiologist. The paper uses our previous paper where ANN is used as a classifier, as feature extractor from measured Doppler signal. To make this, it uses wavelet transforms and short time Fourier transform methods. Before it classifies these features, it applies Wavelet entropy to them. In this paper, our aim is to develop our previous work by using least-squares support vector machine (LS-SVM) classifier instead of ANN. We use LS-SVM and backpropagation artificial neural network (BP-ANN) to classify the extracted features. In addition, we use receiver operator characteristic (ROC) curves to compare sensitivities and specificities of these classifiers and compute the area under the curves. Finally, we evaluate two classifiers in all aspects.

Adolescent↗
PubMed2004

Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis.

Cardiac beat classification is a key process in the detection of myocardial ischemic episodes in the electrocardiographic signal. In the present study, we propose a multicriteria sorting method for classifying the cardiac beats as ischemic or not. Through a supervised learning procedure, each beat is compared to preclassified category prototypes under five criteria. These criteria refer to ST segment changes, T wave alterations, and the patient's age. The difficulty in applying the above criteria is the determination of the required method parameters, namely the thresholds and weight values. To overcome this problem, we employed a genetic algorithm, which, after proper training, automatically calculates the optimum values for the above parameters. A task-specific cardiac beat database was developed for training and testing the proposed method using data from the European Society of Cardiology ST-T database. Various experimental tests were carried out in order to adjust each module of the classification system. The obtained performance was 91% in terms of both sensitivity and specificity and compares favorably to other beat classification approaches proposed in the literature.

Age Factors↗
PubMed2001

Decision support for infectious diseases--a working prototype.

This paper presents a decision support system for nosocomial infections and its integration in the large HIS of the University Hospital of Giessen. The system comprises five different engines and a data dictionary. It is designed to detect hospital acquired infections even in a situation where only a restricted amount of clinical data is available (the data is split up in different information systems). Furthermore the model prevents time consuming manual data entry. The five engines split the main task into: (1) a preselection, which sorts out patients who definitely do not have a nosocomial infection; (2) a rule based reasoning process which detects patients likely to have such an infection; (3) an alarm process which is responsible for the presentation of the alert; (4) an explanation process to follow up the reasoning; and (5) statistic tools to answer specific hygienic questions. A data dictionary supplies the controlled vocabulary, which is required to understand data structures used in the different clinical subsystems and may those with each other.

Artificial Intelligence↗
PubMed2000

Decision support for infectious diseases--a working prototype.

This paper presents a decision support system for nosocomial infections and its integration in the large HIS of the University Hospital of Giessen. The model comprises five different engines and a data dictionary. It is designed to detect hospital acquired infections even in a situation where only a restricted amount of clinical data is available (the data is split up in different information systems). Furthermore the model prevents time consuming manual data entry. The five engines split the main task into 1) a preselection, which sort out patients who definitely do not have a nosocomial infection; 2) a rule based reasoning process which detects patients likely to have such an infection; 3) an alarm process which is responsible for the presentation of the alert; 4) an explanation process to follow up the reasoning and 5) statistic tools to answer specific hygienic questions. A data dictionary supplies the controlled vocabulary, but it is also required to understand datastructures used in the different clinical subsystems.

Artificial Intelligence↗
PubMed1997

The development of a hybrid expert system for the interpretation of fetal acid-base status.

This article presents the development of an expert system for the interpretation of fetal scalp acid-base status. The system consists of logistic transformations, back-propagation neural networks and decision algorithms connected in series. It checks for out-of-range errors and the physiological coherence between measurements. It then determines whether acidosis should be diagnosed, and if so, whether it is more likely to be metabolic, respiratory or mixed. It will also flag those cases where it is difficult to interpret the data in physiological terms. The system was tested on a database of 2174 scalp blood samples collected at the Queens Medical Centre, Nottingham. Of these 88 samples were rejected as erroneous; 13 because of an out-of-range pH alone (> or = 7.48); 73 because more than one measurement was marginally out of range, and two because the relationship between measurements did not make sense. A total of 527 cases (24.2%) were diagnosed as being acidotic; of these, 139 were respiratory, 114 mixed and 274 metabolic. We were unable to fault the system's interpretation when the cases at the margins between diagnostic categories were reviewed clinically.

Acid-Base Equilibrium↗
PubMed2001

Flexible guideline-based patient careflow systems.

Workflow Management Systems integrate domain and organisational knowledge to support business processes. When applied to the medical environment, they can be termed "Careflow Management Systems", and may be used to manage care delivery by enhancing co-operation among healthcare professionals. This paper focuses on care delivery based on clinical practice guidelines. Healthcare organisations are very different from industrial or commercial companies: their main goal is not profit, but maintaining and improving the health of the public. Therefore, outcomes are difficult to measure. Firstly, physicians, while playing a variety of roles, are quite independent decision-makers; secondly, the object of the process, i.e. the patient, may be involved in choosing treatment options, and may be treated by different institutions. For these reasons, the standard functionality of typical Workflow Management Systems must be strongly enhanced in order to cope with healthcare delivery needs. A major issue is accounting for exceptions. In most non-clinical settings this is not a problem because processes are very well defined and can often be easily controlled by some higher authority. As explained above, this does not happen in healthcare organisations. Responsibilities are widely shared, and health care professionals may be non-compliant with guidelines for a variety of reasons. The paper presents a classification of possible exceptions, and shows how the sequence of tasks described by a guideline may be altered, at the implementation level, in order to meet actual user needs, while maintaining guideline intentions as much as possible. A terminology server is also exploited towards this end. This work illustrates a prototype of a Careflow Management System based on an international guideline for ischemic stroke treatment, developed by the American Heart Association.

Artificial Intelligence↗
PubMed1982

Relationships among speech threshold, loudness discomfort, comfortable loudness, and PB max in the elderly hearing impaired.

The purposes of this study were (1) to determine the effect of the severity of hearing loss on the most comfortable loudness level (MCL) and the loudness discomfort level (LDL), and (2) to assess the efficacy of obtaining maximum intelligibility (PB max) at MCL and/or LDL. One hundred twenty-nine ears were tested from seventy-four elderly subjects having mild to moderate sensorineural hearing losses. The results showed that mean MCLs and LDLs remained fairly constant as spondaic thresholds increased from 5 to 55 dB HTL. The data also exhibited considerable intersubject variability and indicated that MCLs and LDLs could not be closely predicted from threshold. Thus, for most clinical purposes MCL and LDL must be measured directly. This study also indicated that measuring speech intelligibility at MCL approximated PB max (+/- 12 percent) only about two-third of the time. Thus, decisions concerning auditory functioning may frequently be inaccurate if intelligibility is measured only at MCL. Neither can LDL be the single intensity at which speech intelligibility is measured, at least with elderly patients, since one in ten scores did not closely approximate PB max. Consequently, we concur with previous investigators who recommend generating an entire intelligibility function. The present findings also suggest that hearing aid users may not receive optimal benefit if their hearing aid is adjusted to MCL, and that improved intelligibility may be achieved a higher volume control settings. The hearing aid evaluation process should investigate this possibility.

Aged↗