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Results for “decision intelligence”

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At least 1,279 records · Page 71Linked to original sources
PubMed2006

Breast tumor malignancy modelling using evolutionary neural logic networks.

The present work proposes a computer assisted methodology for the effective modelling of the diagnostic decision for breast tumor malignancy. The suggested approach is based on innovative hybrid computational intelligence algorithms properly applied in related cytological data contained in past medical records. The experimental data used in this study were gathered in the early 1990s in the University of Wisconsin, based in post diagnostic cytological observations performed by expert medical staff. Data were properly encoded in a computer database and accordingly, various alternative modelling techniques were applied on them, in an attempt to form diagnostic models. Previous methods included standard optimisation techniques, as well as artificial intelligence approaches, in a way that a variety of related publications exists in modern literature on the subject. In this report, a hybrid computational intelligence approach is suggested, which effectively combines modern mathematical logic principles, neural computation and genetic programming in an effective manner. The approach proves promising either in terms of diagnostic accuracy and generalization capabilities, or in terms of comprehensibility and practical importance for the related medical staff.

Artificial Intelligence↗
PubMed2000

Knowledge representation and tool support for critiquing clinical trial protocols.

The increasing complexities of clinical trials have led to increasing costs for investigators and organizations that author and administer those trials. The process of authoring a clinical trial protocol, the document that specifies the details of the study, is usually a manual task, and thus authors may introduce subtle errors in medical and procedural content. We have created a protocol inspection and critiquing tool (PICASSO) that evaluates the procedural aspects of a clinical trial protocol. To implement this tool, we developed a knowledge base for clinical trials that contains knowledge of the medical domain (diseases, drugs, lab tests, etc.) and of specific requirements for clinical trial protocols (eligibility criteria, patient treatments, and monitoring activities). We also developed a set of constraints, expressed in a formal language, that describe appropriate practices for authoring clinical trials. If a clinical trial designed with PICASSO violates any of these constraints, PICASSO generates a message to the user and a list of inconsistencies for each violated constraint. To test our methodology, we encoded portions of a hypothetical protocol and implemented designs consistent and inconsistent with known clinical trial practice. Our hope is that this methodology will be useful for standardizing new protocols and improving their quality.

Artificial Intelligence↗
PubMed2002

Temporal knowledge representation for scheduling tasks in clinical trial protocols.

Clinical trial protocols include detailed temporal constraints on treatment and associated tasks. Unlike health-care guidelines, protocols are highly prescriptive. Therefore, informatics applications that enforce such temporal constraints are more directly useful with protocols than with guidelines. Although there are some temporal knowledge representation efforts for health-care guidelines, we find these to be insufficiently expressive for clinical trial protocols. In this paper, we focus on temporal knowledge representation for clinical trial protocols and the task of patient-specific scheduling in protocols. We define a temporal ontology, use it to encode clinical trial protocols, and describe a prototype tool to carry out patient-specific scheduling for the tasks in protocols. We predict that an expressive temporal knowledge representation can support a number of scheduling and management tasks for protocol-based care.

Appointments and Schedules↗
PubMed1994

An intelligent Computer-Assisted Instruction system for clinical case teaching.

The use of computers in the field of medical education is common. Our purpose is to present a Computer-Assisted Instruction system which has been developed over ten years at the University of Compiègne and the University of Rennes Medical School. This system can be used to help the student to solve clinical cases by analyzing and critiquing their answers and by using a knowledge base which has been previously structured in a rule network. It is an intelligent Computer-Assisted Instruction system comprising an author module, a pedagogical module and a student module. The CAI system can be used as a simulation model for any type of diagnostic or therapeutic problem. In this paper we present the author and pedagogical module which have been built using our previous work on intelligent computer-assisted instruction systems.

Artificial Intelligence↗
PubMed1994

Generating explanations and tutorial problems from Bayesian networks.

We present a system that generates explanations and tutorial problems from the probabilistic information contained in Bayesian belief networks. BANTER is a tool for high-level interaction with any Bayesian network whose nodes can be classified as hypotheses, observations, and diagnostic procedures. Users need no knowledge of Bayesian networks, only familiarity with the particular domain and an elementary understanding of probability. Users can query the knowledge base, identify optimal diagnostic procedures, and request explanations. We describe BANTER's algorithms and illustrate its application to an existing medical model.

Algorithms↗
PubMed1993

An intelligent interactive simulator of clinical reasoning in general surgery.

We introduce an interactive computer environment for teaching in general surgery and for diagnostic assistance. The environment consists of a knowledge-based system coupled with an intelligent interface that allows users to acquire conceptual knowledge and clinical reasoning techniques. Knowledge is represented internally within a probabilistic framework and externally through a interface inspired by Concept Graphics. Given a set of symptoms, the internal knowledge framework computes the most probable set of diseases as well as best alternatives. The interface displays CGs illustrating the results and prompting essential facts of a medical situation or a process. The system is then ready to receive additional information or to suggest further investigation. Based on the new information, the system will narrow the solutions with increased belief coefficients.

Artificial Intelligence↗
PubMed1997

Cyberspaced.

It's weird science-a chore to challenge even the likes of IBM and Microsoft. Hospital computer systems can't be easily extended to outpatient satellites. But despite the odds and expense, a few are making headway.

Ambulatory Care Information Systems↗
PubMed1987

Re B. (a Minor) (Sterilization), 30 April 1987.

A local authority, who had care of seventeen year old mentally handicapped and epileptic girl, sought an order making her a ward and authorizing her sterilization. It claimed that she was becoming sexually aware, did not understand that intercourse could cause childbirth, and would not be able to cope with pregnancy or caring for a child. The Official Solicitor, acting as the girl's guardian ad litem, appealed a decision authorizing the sterilization. The House of Lords ruled that the welfare and best interest of the girl were of paramount concern and that in this case they warranted her sterilization.

Behavior↗
PubMed1976

Problems in the interpretation of cognitive abnormalities in schizophrenia.

This study compared matched groups of patients with acute schizophrenia and with depression on three tests used in the assessment of schizophrenic thinking disorder. Most measures derived from these tests significantly differentiated the groups; however, within the schizophrenic group there were no significant correlations between scores on the three tests. Further data were available from a choice reaction-time card-sorting task, from which estimates of distractability, stimulus decision time, response decision time, and movement time, were obtained. Only one significant relation was found between these measures and scores on the clinical tests. The possible confounding effects of intelligence and responsiveness are discussed. It is argued that more direct measures of the latter are preferable to interpreting tests of thinking disorder in terms of information processing deficits.

Acute Disease↗
PubMed1997

Decision theoretic steering and genetic algorithm optimization: application to stereotactic radiosurgery treatment planning.

Treatment planning for stereotactic radiosurgery and fractionated radiotherapy is currently a labor intensive, operator-dependent process. Many degrees of freedom exist to make rigorous optimization intractable except by computationally intelligent techniques. The quality of a given plan is determined by an aggregate of clinical objectives, most of which are subject to competing tradeoffs. In this work, we present an autonomous scheme that couples decision theoretic guidance with a genetic algorithm for optimization. Ordinal ranking among a population of viable treatment plans is based on a generalized distance metric, which promotes a decreasing hyperfrontier of the efficient solution set. The solution set is driven toward efficiency by the genetic algorithm, which uses the tournament selection mechanism based on the ordinal ranking. Goals and satisficing conditions can be defined to signal the ultimate and the minimum achievement levels in a given objective. A conventionally challenging case in radiosurgery was used to demonstrate the practical utility and the problem-solving power of the decision theoretic genetic algorithm. Treatment plans with one isocenter and four isocenters were derived under the autonomous scheme and compared to the actual treatment plan manually optimized by the expert planner. Quality assessment based on dose-volume histograms and normal tissue complication probabilities suggested that computational optimization could be driven to offer varying degrees of dosimetric improvement over a human-guided optimization effort. Furthermore, it was possible to achieve a high degree of isodose conformity to the target volume in computational optimization by increasing the degree of freedom in the treatment parameters. The time taken to derive an efficient planning solution was comparable and usually shorter than in the manual planning process, and can be scaled down almost linearly with the number of processors. Overall, the autonomous genetic algorithm scheme was found to be powerful and versatile as a computationally intelligent counterpart to human-guided strategies in treatment optimization for stereotactic radiosurgery and radiotherapy.

Algorithms↗
PubMedDate not supplied

Virtual instrumentation and real-time executive dashboards. Solutions for health care systems.

Successful organizations have the ability to measure and act on key indicators and events in real time. By leveraging the power of virtual instrumentation and open architecture standards, multidimensional executive dashboards can empower health care organizations to make better and faster data-driven decisions. This article will highlight how user-defined virtual instruments and dashboards can connect to hospital information systems (e.g., admissions/discharge/transfer systems, patient monitoring networks) and use statistical process control to "visualize" information and make timely, data-driven decisions. The case studies described will illustrate enterprisewide solutions for: bed management and census control, operational management, data mining and business intelligence applications, and clinical applications (physiological data acquisition and wound measurement and analysis).

Bed Occupancy↗
PubMed2003

What factors determine the success of clinical decision support systems?

Computerized decision support systems (CDSS) which improve the quality of patient care are strong and necessary incentives for clinicians to use electronic medical records. We have noted previously that the logical path of CDSS design, which would be to determine the factors that predict success before the system is designed, appears rarely to have been followed. In this overview update of the literature on predictors of successful CDSS, we conclude that the predictors have not been adequately identified and the success of CDSS may improve when they are.

Artificial Intelligence↗
PubMed1991

Using computer software to improve group decision-making.

This article provides a review of some of the work done in the area of knowledge-based systems for strategic planning. Since 1985, with the founding of the Center for Knowledge-based Systems for Business Management, the project has focused on developing knowledge-based systems (KBS) based on these models. In addition, the project also involves developing a variety of computer and non-computer methods and techniques for assisting both technical and non-technical managers and individuals to do decision modelling and KBS development. This paper presents a summary of one segment of the project: a description of integrative groupware useful in strategic planning. The work described here is part of an ongoing research project. As part of this project, for example, over 200 non-technical and technical business managers, most of them working full-time during the project, developed over 160 KBS prototype systems in conjunction with MBA course in strategic planning and management decision making. Based on replies to a survey of this test group, 28 per cent of the survey respondents reported their KBS were used at work, 21 per cent reportedly received promotions, pay rises or new jobs based on their KBS development work, and 12 per cent reported their work led to participation in other KBS development projects at work. All but two of the survey respondents reported that their work on the KBS development project led to a substantial increase in their job knowledge or performance.

Artificial Intelligence↗
PubMedDate not supplied

Application of computers in diabetes care--a review. II. Computers for decision support and education.

This paper concludes a review of the contribution of information technology to diabetes care. The role of decision support tools for planning insulin therapy using various clinical algorithms, knowledge-based approaches and interactive simulations is discussed, and certain telemedicine approaches to therapy are considered. The validation and clinical evaluation of these tools is reviewed, as is the likely impact of the routine clinical application of implantable/non-invasive blood glucose monitoring devices. Finally, the application of computers as teaching tools is considered, and ways in which such educational approaches might be applied for disseminating the benefits of the Diabetes Control and Complications Trial more widely are discussed.

Artificial Intelligence↗