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PubMed1991

INFORM: integrated support for decisions and activities in intensive care.

Many medical decision support systems that have been developed in the past have failed to enter routine clinical practice. Often this is because the developers have failed to analyse in sufficient detail the precise user requirements, because they have produced a system which takes too narrow a view of the patient, or because the decision support facilities have not been sufficiently well integrated into the routine clinical data handling activities. In this paper we discuss how the AIM-INFORM project is setting out to deal with these issues, in the context of the provision of decision support in the intensive care unit.

Artificial Intelligence↗
PubMed2001

Representation of clinical practice guidelines for computer-based implementations.

Representation of clinical practice guidelines is a critical issue for computer-based guideline development, implementation and evaluation. We studied eight types of computer-based guideline representation models. Typical primitives for these models include decisions, actions, patient states and execution states. Temporal constraints and nesting are important aspects of guideline structure representation. Integration of guidelines with electronic medical records can be facilitated by the introduction of formal models of patient data. Patient states and execution states are closely related to one another. Data collection, decision, patient state and intervention are four basic steps in a guideline's logic flow.

Artificial Intelligence↗
PubMed1983

Use of conditional rule structure to automate clinical decision support: a comparison of artificial intelligence and deterministic programming techniques.

A rule-based computer system was developed to perform clinical decision-making support within a medical information system, oncology practice, and clinical research. This rule-based system, which has been programmed using deterministic rules, possesses features of generalizability, modularity of structure, convenience in rule acquisition, explanability, and utility for patient care and teaching, features which have been identified as advantages of artificial intelligence (AI) rule-based systems. Formal rules are primarily represented as conditional statements; common conditions and actions are stored in system dictionaries so that they can be recalled at any time to form new decision rules. Important similarities and differences exist in the structure of this system and clinical computer systems utilizing artificial intelligence (AI) production rule techniques. The non-AI rule-based system possesses advantages in cost and ease of implementation. The degree to which significant medical decision problems can be solved by this technique remains uncertain as does whether the more complex AI methodologies will be required.

Computers↗
PubMedDate not supplied

Evaluation of decision support systems in medicine.

Evaluation deals with the measurement or judgement of system characteristics and with comparison of these with the frame of reference. Evaluation of medical decision support systems is important because these systems are planned to support human decision making in tasks where information from different sources is combined to support clinicians' decisions concerning diagnosis, therapy planning and monitoring of the disease and treatment processes. As the field of decision support systems is still relatively unexplored, standards or generally accepted methodologies are not yet available for evaluation. Evaluation of medical decision support systems should be approached from the perspectives of knowledge acquisition, system development life-cycle and user-system integrated environment.

Artificial Intelligence↗
PubMed1999

Computer-assisted decision making in portal verification--optimization of the neural network approach.

PURPOSE: Conventional portal verification requires that a qualified radiation oncologist make decisions as to the set-up acceptability. This scheme is no longer sustainable with the large numbers of images available on-line and stringent time constraints. Therefore the objective of this study was to develop, optimize, and evaluate on clinical data an artificial intelligence decision-making tool for portal verification. The tool, based on the artificial neural network (ANN) approach, should approximate, as closely as possible, portal verification assessments made by a radiation oncologist expert. METHODS AND MATERIALS: A total of 328 electronic portal images of tangential breast irradiations were included in the study. A radiation oncologist expert evaluated these images and rated the treatment set-up acceptability on a scale from 0 to 10. Translational and rotational errors in the placement of the radiation field boundaries formed seven-dimensional feature vectors that represented each of the 328 portal images/treatments. The feature vectors were used as inputs to a three-layer, feedforward ANN. The neural network was trained on the oncologist's ratings. RESULTS: The rms discrepancy between the ANN and the expert's ratings was 1.05 rating points. Using the decision threshold equal to 5 for both sets of ratings, the ANN classifier was capable of detecting 100% of the portals classified as "unacceptable" by the expert. Only 6.5% of portals acceptable to the oncologist were misclassified as "unacceptable" by the ANN. CONCLUSION: The results of this study indicate the feasibility of using the ANN portal image classifier as an automated assistant to the radiation oncologist. Its role would be to recommend an appropriate decision as to the acceptability or otherwise of a given treatment set-up depicted in a portal image.

Breast Neoplasms↗
PubMed2001

Modeling data and knowledge in the EON guideline architecture.

Compared to guideline representation formalisms, data and knowledge modeling for clinical guidelines is a relatively neglected area. Yet it has enormous impact on the format and expressiveness of decision criteria that can be written, on the inferences that can be made from patient data, on the ease with which guidelines can be formalized, and on the method of integrating guideline-based decision-support services into implementation sites' information systems. We clarify the respective roles that data and knowledge modeling play in providing patient-specific decision support based on clinical guidelines. We show, in the context of the EON guideline architecture, how we use the Protégé-2000 knowledge-engineering environment to build (1) a patient-data information model, (2) a medical-specialty model, and (3) a guideline model that formalizes the knowledge needed to generate recommendations regarding clinical decisions and actions. We show how the use of such models allows development of alternative decision-criteria languages and allows systematic mapping of the data required for guideline execution from patient data contained in electronic medical record systems.

Artificial Intelligence↗
PubMed2003

The development of variable MLM editor and TSQL translator based on Arden Syntax in Taiwan.

The Arden Syntax standard has been utilized in the medical informatics community in several countries during the past decade. It is never used in nursing in Taiwan. We try to develop a system that acquire medical expert knowledge in Chinese and translates data and logic slot into TSQL Language. The system implements TSQL translator interpreting database queries referred to in the knowledge modules. The decision-support systems in medicine are data driven system where TSQL triggers as inference engine can be used to facilitate linking to a database.

Artificial Intelligence↗
PubMedDate not supplied

A methodology for generating computer-based explanations of decision-theoretic advice.

Decision analysis is an appealing methodology with which to provide decision support to the practicing physician. However, its use in the clinical setting is impeded because computer-based explanations of decision-theoretic advice are difficult to generate without resorting to mathematical arguments. Nevertheless, human decision analysts generate useful and intuitive explanations based on decision trees. To facilitate the use of decision theory in a computer-based decision support system, the authors developed a computer program that uses symbolic reasoning techniques to generate nonquantitative explanations of the results of decision analyses. A combined approach has been implemented to explain the differences in expected utility among branches of a decision tree. First, the mathematical relationships inherent in the structure of the tree are used to find any asymmetries in tree structure or inequalities among analogous decision variables that are responsible for a difference in expected utility. Next, an explanation technique is selected and applied to the most significant variables, creating a symbolic expression that justifies the decision. Finally, the symbolic expression is converted to English-language text, thereby generating an explanation that justifies the desirability of the choice with the greater expected utility. The explanation does not refer to mathematical formulas, nor does it include probability or utility values. The results suggest that explanations produced by a combination of decision analysis and symbolic processing techniques may be more persuasive and acceptable to clinicians than those produced by either technique alone.

Artificial Intelligence↗
PubMed2002

An integrative model for in-silico clinical-genomics discovery science.

Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

Artificial Intelligence↗
PubMed1997

Decision support system to assist mechanical ventilation in the adult respiratory distress syndrome.

This paper presents a knowledge-based decision support system to assist mechanical ventilation in patients with the Adult Respiratory Distress Syndrome (DSSARDS). The knowledge base uses clinical algorithms developed from interviews and seminars with experts. The system contains 140 rules, applies backward chaining and was built on an IBM-PC compatible microcomputer. Clinical and physiological data and ventilator settings were used for suggestions of ventilatory support mode (VSMODE) and settings (MVSET) and for hemodynamic evaluation and therapy (HEMO). Success rates (s) and kappa coefficient (k) were used to measure agreement between DSSARDS and physicians at 4 decision steps related to: beginning of mechanical ventilation (FIRSTSET), VSMODE, MVSET and HEMO, DSSARDS prototype was evaluated in a development phase with 6 patients aged 48.6 +/- 15.9 years. Agreement results for 142 decision steps were: FIRSTSET k = 0.90, s = 0.93; VSMODE k = 0.76, s = 0.92; HEMO k = 0.58, s = 0.70, MVSET k = 0.86, s = 0.92 (p < 0.05 for all k). Improvements in the knowledge base were performed mainly in HEMO and VSMODE modules. The subsequent test phase studied 5 patients aged 54.8 +/- 11.0 years in a total of 900 decision steps. Results were: FIRSTSET k = 0.93, s = 0.95; VSMODE k = 0.93, s = 0.96; HEMO k = 0.97, s = 0.99, MVSET k = 0.96, s = 0.97 (p < 0.05 for all k). The results indicate significant agreement between DSSARDS and physicians for all decision steps. This suggests that DSSARDS may be used as a support for decision making and a training tool for mechanical ventilation in patients with the adult respiratory distress syndrome.

Adult↗
PubMed1997

Understanding time series networks: a case study in rule extraction.

A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We have developed an algorithm, called TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Given a trained network, TREPAN produces a decision tree that approximates the concept represented by the network. In this article, we discuss the application of TREPAN to a neural network trained on a noisy time series task: predicting the Dollar-Mark exchange rate. We present experiments that show that TREPAN is able to extract a decision tree from this network that equals the network in terms of predictive accuracy, yet provides a comprehensible concept representation. Moreover, our experiments indicate that decision trees induced directly from the training data using conventional algorithms do not match the accuracy nor the comprehensibility of the tree extracted by TREPAN.

Artificial Intelligence↗
PubMed2000

Data mining and structuring of executable data analysis reports: guideline development and implementation in a narrow sense.

In this paper we present a data mining scenario that supports development of automated web-based documentation of data analysis for diagnosis and treatment. The documents can be seen as guidelines in a narrow sense, and are designed to include executable modules for the corresponding decision support systems. Our aim is to discuss the possibilities of identifying certain types of diagnoses and treatments for which guidelines can be generated and computerised more systematically.

Artificial Intelligence↗
PubMed2004

Using statistical decision theory to predict speech intelligibility. III. Effect of audibility on speech recognition sensitivity.

The speech recognition sensitivity (SRS) model [H. Müsch and S. Buus, J. Acoust. Soc. Am. 109, 2896-2909 (2001)] is a macroscopic model for predicting speech intelligibility. The present study proposes a modification to the relation between the SRS model's audibility-noise variance and the signal-excitation to noise-excitation ratio (SNRE) in the auditory periphery. The modified relation is derived from data obtained in nine studies that measured normal-hearing listeners' consonant-recognition performance at several levels of speech-spectrum shaped noise. When the audibility-noise variance is directly proportional to the relative power of the noise excitation in the auditory periphery, the SRS model yields good predictions of the data. Four of the nine studies also reported consonant-recognition performance in various filtering conditions. Good predictions of these data were achieved with SRS model parameters that were consistent with the model parameters fitting the speech-in-noise data and with the model parameters used in the original SRS papers.

Auditory Cortex↗
PubMed1999

Decision tree-based formation of consensus protein secondary structure prediction.

MOTIVATION: Prediction of protein secondary structure provides information that is useful for other prediction methods like fold recognition and ab initio 3D prediction. A consensus prediction constructed from the output of several methods should yield more reliable results than each of the individual methods. METHOD: We present an approach that reveals subtle but systematic differences in the output of different secondary structure prediction methods allowing the derivation of coherent consensus predictions. The method uses a machine learning technique that builds decision trees from existing data. RESULTS: The first results of our analysis show that consensus prediction of protein secondary structure may be improved both quantitatively and qualitatively.

Algorithms↗
PubMed2004

On the structure and initial parameter identification of Gaussian RBF networks.

We consider the efficient initialization of structure and parameters of generalized Gaussian radial basis function (RBF) networks using fuzzy decision trees generated by fuzzy ID3 like induction algorithms. The initialization scheme is based on the proposed functional equivalence property of fuzzy decision trees and generalized Gaussian RBF networks. The resulting RBF network is compact, easy to induce, comprehensible, and has acceptable classification accuracy with stochastic gradient descent learning algorithm.

Algorithms↗
PubMed2006

Decision forest analysis of large-scale sib-pair identical-by-decent profiles for locating the underlying disease genes for alcoholism in human.

OBJECTIVE: To extract the relevant SNPs for alcoholism using sib-pair IBD profiles of pedigrees. METHODS: We used the ensemble decision approach, a supervised learning approach based on decision forests, to locate alcoholism relevant SNPs using genome-wide SNP data. RESULTS: Application to a publicly available large dataset of 100 simulated replicates for three American populations (http://www.gaworkshop.org/) demonstrates that the proposed approach has successfully located all of the simulated true loci. CONCLUSION: The numerical results establish the proposed decision forest analysis to be a powerful and practical alternative for large-scale family-based association study.

Alcoholism↗
PubMed1991

Factors influencing the transferability of medical decision support systems.

Transferability is a key issue in the development and implementation of medical decision support systems. Such systems have up to now tended to be confined to the development site. Transferability represents the integrated effect of several more basic attributes of Decision Support Systems. These attributes can be considered to belong to two main groups: those concerned with the Medical Domain (of the system) and those concerned with the Information Technology by which the System functions. Among the Domain issues the most important are: Epidemiology, Terminology and Methodology. Concerning the Information Technology issues the most important are: Knowledge Acquisition and Representation methods, Database design and integration with inference mechanism. The effect of each of the individual factors is considered by illustrations from the literature and by studying the results of recent experiments where databases and decision support systems from different countries are interchanged. This exercise allows some planning in the design of future systems with the aim of improving overall transferability and therefore applicability.

Artificial Intelligence↗
PubMed2006

Machine learning in soil classification.

In a number of engineering problems, e.g. in geotechnics, petroleum engineering, etc. intervals of measured series data (signals) are to be attributed a class maintaining the constraint of contiguity and standard classification methods could be inadequate. Classification in this case needs involvement of an expert who observes the magnitude and trends of the signals in addition to any a priori information that might be available. In this paper, an approach for automating this classification procedure is presented. Firstly, a segmentation algorithm is developed and applied to segment the measured signals. Secondly, the salient features of these segments are extracted using boundary energy method. Based on the measured data and extracted features to assign classes to the segments classifiers are built; they employ Decision Trees, ANN and Support Vector Machines. The methodology was tested in classifying sub-surface soil using measured data from Cone Penetration Testing and satisfactory results were obtained.

Algorithms↗