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PubMed2004

Dynamic ion channel activation scheduling in patch clamp on a chip.

In 2002, Fertig et al. made a remarkable invention: the first successful demonstration of a patch clamp on a chip--a planar quartz-based biological chip that contains up to several hundred ion channels. This patch-clamp chip can be used in massively parallel screens for ion channel activity, thereby providing a high-throughput screening tool for drug discovery efforts. In this paper, we propose computationally efficient dynamic stochastic scheduling algorithms for activating individual ion channels in the patch-clamp chip. By formulating the ion channel activation scheduling problem as a partially observed Markov decision process with a multiarmed bandit structure, near-optimal dynamic scheduling for activation of the individual channels is achieved to optimize the information gained from the patch-clamp chip. Numerical examples using state-of-the-art algorithms developed recently in artificial intelligence and operations research are presented to illustrate these dynamic ion channel (macromolecule) activation scheduling algorithms.

Algorithms↗
PubMed1993

A decision class analysis of critical care life-support decision-making.

Decision analysis is a powerful methodology that can help clinicians make good decisions. Because it is not practical to place a decision analyst at the bedside in critical care units, the application of this methodology will require leveraging the analyst through computer-based systems. A decision class analysis is a collective analysis of a group of decisions that provides the high-level specification for such a computer system. This paper presents a decision class analysis of critical care life-support decisions. Key elements of this analysis are: the simplification of an otherwise extremely complex multistage sequential decision problem by using a sequence of two-stage models, and the use of six generic knowledge maps that capture the extremely complex relevant medical knowledge.

Artificial Intelligence↗
PubMed2004

Adaptive probabilistic neural networks for pattern classification in time-varying environment.

In this paper, we propose a new class of probabilistic neural networks (PNNs) working in nonstationary environment. The novelty is summarized as follows: 1) We formulate the problem of pattern classification in nonstationary environment as the prediction problem and design a probabilistic neural network to classify patterns having time-varying probability distributions. We note that the problem of pattern classification in the nonstationary case is closely connected with the problem of prediction because on the basis of a learning sequence of the length n, a pattern in the moment n + k, k > or = 1 should be classified. 2) We present, for the first time in literature, definitions of optimality of PNNs in time-varying environment. Moreover, we prove that our PNNs asymptotically approach the Bayes-optimal (time-varying) decision surface. 3) We investigate the speed of convergence of constructed PNNs. 4) We design in detail PNNs based on Parzen kernels and multivariate Hermite series.

Algorithms↗
PubMed2002

Intelligent automation of high-performance liquid chromatography method development by means of a real-time knowledge-based approach.

We describe the development, attributes and capabilities of a novel type of artificial intelligence system, called LabExpert, for automation of HPLC method development. Unlike other computerised method development systems, LabExpert operates in real-time, using an artificial intelligence system and design engine to provide experimental decision outcomes relevant to the optimisation of complex separations as well as the control of the instrumentation, column selection, mobile phase choice and other experimental parameters. LabExpert manages every input parameter to a HPLC data station and evaluates each output parameter of the HPLC data station in real-time as part of its decision process. Based on a combination of inherent and user-defined evaluation criteria, the artificial intelligence system programs use a reasoning process, applying chromatographic principles and acquired experimental observations to iteratively provide a regime for a priori development of an acceptable HPLC separation method. Because remote monitoring and control are also functions of LabExpert, the system allows full-time utilisation of analytical instrumentation and associated laboratory resources. Based on our experience with LabExpert with a wide range of analyte mixtures, this artificial intelligence system consistently identified in a similar or faster time-frame preferred sets of analytical conditions that are equal in resolution, efficiency and throughput to those empirically determined by highly experienced chromatographic scientists. An illustrative example, demonstrating the potential of LabExpert in the process of method development of drug substances, is provided.

Artificial Intelligence↗
PubMed1995

Knowledge discovery in clinical databases based on variable precision rough set model.

Since a large amount of clinical data are being stored electronically, discovery of knowledge from such clinical databases is one of the important growing research area in medical informatics. For this purpose, we develop KDD-R (a system for Knowledge Discovery in Databases using Rough sets), an experimental system for knowledge discovery and machine learning research using variable precision rough sets (VPRS) model, which is an extension of original rough set model. This system works in the following steps. First, it preprocesses databases and translates continuous data into discretized ones. Second, KDD-R checks dependencies between attributes and reduces spurious data. Third, the system computes rules from reduced databases. Finally, fourth, it evaluates decision making. For evaluation, this system is applied to a clinical database of meningoencephalitis, whose computational results show that several new findings are obtained.

Artificial Intelligence↗
PubMed2002

The use of visual search for knowledge gathering in image decision support.

This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements. The framework involves the construction of a generic image feature extraction library, from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis. The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features. The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure. The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers. In all cases, the accuracy of the students improved from near random decision making (33%) to accuracies ranging from 50% to 68%.

Algorithms↗
PubMed2006

Emotion-affected decision making in human simulation.

Human modelling is an interdisciplinary research field. The topic, emotion-affected decision making, was originally a cognitive psychology issue, but is now recognized as an important research direction for both computer science and biomedical modelling. The main aim of this paper is to attempt to bridge the gap between psychology and bioengineering in emotion-affected decision making. The work is based on Ortony's theory of emotions and bounded rationality theory, and attempts to connect the emotion process with decision making. A computational emotion model is proposed, and the initial framework of this model in virtual human simulation within the platform of Virtools is presented.

Artificial Intelligence↗
PubMed2005

A satisfactory-oriented approach to multiexpert decision-making with linguistic assessments.

This paper proposes a multiexpert decision-making (MEDM) method with linguistic assessments, making use of the notion of random preferences and a so-called satisfactory principle. It is well known that decision-making problems that manage preferences from different experts follow a common resolution scheme composed of two phases: an aggregation phase that combines the individual preferences to obtain a collective preference value for each alternative; and an exploitation phase that orders the collective preferences according to a given criterion, to select the best alternative/s. For our method, instead of using an aggregation operator to obtain a collective preference value, a random preference is defined for each alternative in the aggregation phase. Then, based on a satisfactory principle defined in this paper, that says that it is perfectly satisfactory to select an alternative as the best if its performance is as at least "good" as all the others under the same evaluation scheme, we propose a linguistic choice function to establish a rank ordering among the alternatives. Moreover, we also discuss how this linguistic decision rule can be applied to the MEDM problem in multigranular linguistic contexts. Two application examples taken from the literature are used to illuminate the proposed techniques.

Algorithms↗
PubMed2002

SYNCHRONUS: a reusable software module for temporal integration.

Querying time-stamped data in clinical databases is an essential step in the actuation of many decision-support rules. Since previous methods of temporal data management are not readily transferable among legacy databases, developers must create de novo querying methods that allow temporal integration of a decision-support program and existing database. In this paper, we outline four software-engineering principles that support a general, reusable approach to temporal integration. We then describe the design and implementation of SYNCHRONUS, a software module that advances our prior work on temporal querying. We show how this module satisfies the four principles for the task of temporal integration. SYNCHRONUS can help developers to overcome the software-engineering burden of temporal model heterogeneity within decision-support architectures.

Algorithms↗
PubMed2001

The socio-organizational age of artificial intelligence in medicine.

The increasing pressure on Health Care Organizations (HCOs) to ensure efficiency and cost-effectiveness, balancing quality of care and cost containment, will drive them towards a more effective management of medical knowledge derived from research findings. The relation between science and health services has until recently been too casual. The primary job of medical research has been to understand the mechanisms of disease and produce new treatments, not to worry about the effectiveness of the new treatments or their implementation. As a result many new treatments have taken years to become part of routine practice, ineffective treatments have been widely used, and medicine has been opinion rather than evidence based. This results in suboptimal care for patients. Knowledge management technology may provide effective approaches in speeding up the diffusion of innovative medical procedures whose clinical effectiveness have been proved: the most interesting one is represented by computer-based utilization of evidence-based clinical guidelines. As researchers in Artificial Intelligence in Medicine (AIM), we are committed to foster the strategic transition from opinion to evidence-based decision making. Reviews of the effectiveness of various methods of guideline dissemination show that the most predictable impact is achieved when the guideline is made accessible through computer-based and patient specific reminders that are integrated into the clinician's workflow. However, the traditional single doctor-patient relationship is being replaced by one in which the patient is managed by a team of health care professionals, each specializing in one aspect of care. Such shared care depends critically on the ability to share patient-specific information and medical knowledge easily among them. Strategically there is a need to take a more clinical process view of health care delivery and to identify the appropriate organizational and information infrastructures to support this process. Thus, the great challenge for AIM researchers is to exploit the astonishing capabilities of new technologies to disseminate their tools to benefit HCOs by assuring the conditions of knowledge management and organizational learning at the fullest extent possible. To achieve such a strategic goal, a guideline can be viewed as a model of the care process. It must be combined with an organization model of the specific HCO to build patient careflow management systems. Artificial intelligence can be extensively used to design innovative tools to support all the development stages of those systems. However, exploiting the knowledge represented in a guideline to build them requires to extend today's workflow technology by solving some challenging problems.

Artificial Intelligence↗
PubMed1993

The design and implementation of a ventilator-management advisor.

VentPlan is an implementation of the architecture developed by the qualitative-quantitative (QQ) research group for combining qualitative and quantitative computation in a ventilator-management advisor (VMA). VentPlan calculates recommended settings for four controls of a ventilator by evaluating the predicted effects of alternative ventilator settings. A belief network converts clinical diagnoses to distributions on physiologic parameters. A mathematical-modeling module applies a patient-specific mathematical model of cardiopulmonary physiology to predict the effects of alternative ventilator settings. A decision-theoretic plan evaluator ranks the predicted effects of alternative ventilator settings according to a multiattribute-value model that specifies physician preferences for ventilator treatments. Our architecture allows VentPlan to interpret quantitative observations in light of the clinical context (such as the clinical diagnosis). We report a retrospective study of the ventilator-setting changes encountered in postoperative patients in a surgical intensive-care unit (ICU). We conclude that the QQ architecture allows VentPlan to apply a patient-specific physiologic model to calculate ventilator settings that are optimal with respect to a decision-theoretic value model describing physician preferences for setting the ventilator.

Algorithms↗
PubMed1998

Knowledge discovery and case based reasoning in health promotion: development of a help-desk for prevention of occupational injuries.

This paper presents the concepts, ideas and techniques behind Case Based Reasoning (CBR) in relation to knowledge extraction techniques for health promotion. The ultimate goal is to develop a help-desk service for advice about preventive measures to be taken concerning concrete occupational injury hazards. CBR has been suggested to be a complimentary method to knowledge extraction in order to take direct advantages of large databases for building decision support systems. In this work a database on work injuries is being used to develop a CBR application using a CBR shell-called Recall.

Accidents, Occupational↗
PubMed2006

Numeracy and decision making.

A series of four studies explored how the ability to comprehend and transform probability numbers relates to performance on judgment and decision tasks. On the surface, the tasks in the four studies appear to be widely different; at a conceptual level, however, they all involve processing numbers and the potential to show an influence of affect. Findings were consistent with highly numerate individuals being more likely to retrieve and use appropriate numerical principles, thus making themselves less susceptible to framing effects, compared with less numerate individuals. In addition, the highly numerate tended to draw different (generally stronger or more precise) affective meaning from numbers and numerical comparisons, and their affective responses were more precise. Although generally helpful, this tendency may sometimes lead to worse decisions. The less numerate were influenced more by competing, irrelevant affective considerations. Analyses showed that the effect of numeracy was not due to general intelligence. Numerical ability appears to matter to judgments and decisions in important ways.

Adult↗
PubMed1993

An evaluation of explanations of probabilistic inference.

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.

Bayes Theorem↗
PubMed1993

The Arden syntax for medical logic modules.

The Arden Syntax for sharing medical knowledge bases is described. Its current focus is on knowledge that is represented as a set of independent modules that can provide therapeutic suggestions, alerts, diagnosis scores, etc. The syntax is based largely upon HELP and the Regenstrief Medical Record System. Each module, called a Medical Logic Module or MLM, is made of slots grouped into maintenance, library, and knowledge categories. The syntax has provisions for querying a clinical database and representing time. Several clinical information systems were analyzed and appear to be compatible with the syntax. The syntax has been tested for syntactic ambiguities using the tools lex and yacc. Seventeen institutions are currently in the process of adopting the Arden Syntax for their decision-support systems. A subcommittee of ASTM has been formed to develop standards for sharing medical knowledge bases. The Arden Syntax has been published by ASTM as a initial standard for sharing medical knowledge.

Artificial Intelligence↗
PubMed2001

A before-after study using OncoDoc, a guideline-based decision support-system on breast cancer management: impact upon physician prescribing behaviour.

Guideline-based decision support systems have been developed to influence the prescribing behaviour of clinicians, but they have not yet shown to increase physician compliance with best practices in routine. OncoDoc is a non-automated system that allows flexibility in guideline interpretation to obtain best patient-specific recommendations at the point of care. OncoDoc is applied to breast cancer management. We have experimented the system at the Institut Gustave Roussy with a before-after study in which treatment decisions for breast cancer patients were measured before and after using the system in order to evaluate its impact upon physicians' prescribing behaviour. After 4 months, 127 decisions were recorded. Physicians compliance with OncoDoc was significantly improved (p < 10(-4) ) to reach 85.03% after using the system. Comparison of initial and final decisions showed that physicians modified their prescription in 31% of the cases. Clinical trial accrual rate increased of 50%, though not statistically significant because estimated on small figures.

Artificial Intelligence↗
PubMed1992

An evaluation of explanations of probabilistic inference.

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results, to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.

Anesthesia↗
PubMed1999

Development of autonomous control in a closed microbial bioreactor.

Space-based life support systems which include ecological components will rely on sophisticated hardware and software to monitor and control key system parameters. Autonomous closed artificial ecosystems are useful for research in numerous fields. We are developing a bioreactor designed to study both microbe-environment interactions and autonomous control systems. Currently we are investigating N-cycling and N-mass balance in closed microbial systems. The design features of the system involve real-time monitoring of physical parameters (e.g. temperature, light), growth solution composition (e.g. pH, NOx, CO2), cell density and the status of important hardware components. Control of key system parameters is achieved by incorporation of artificial intelligence software tools that permit autonomous decision-making by the instrument. These developments provide a valuable research tool for terrestrial microbial ecology, as well as a testbed for implementation of artificial intelligence concepts. Autonomous instrumentation will be necessary for robust operation of space-based life support systems, and for use on robotic spacecraft. Sample data acquired from the system, important features of software components, and potential applications for terrestrial and space research will be presented.

Artificial Intelligence↗