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At least 181 records · Page 10Linked to original sources
PubMed2004

OWA aggregation over a continuous interval argument with applications to decision making.

We briefly describe the ordered weighted average (OWA) operator. We discuss its role in decision making under uncertainty. We provide an extension of the OWA operator to the case in which our argument is a continuous valued interval rather than a finite set of values. We look at some examples of this type of aggregation. We show how it can be used in some tasks that arise in decision making. We consider the extension of the continuous interval argument OWA operator to the more general case in which the argument values have importance weights. We use this to introduce the idea of an attitudinal-based expected value associated with a continuous random variable.

Algorithms↗
PubMed2000

Knowledge representation forms for data mining methodologies as applied in thoracic surgery.

Typical ways of disseminating and using results of clinical research are scientific journals and reports. Presentation forms are condensed and comprehensible mainly to the experts following the specific topics. A vast amount of information remains unutilized due to the complex form of presenting the knowledge. Subject of this research is to explore possibilities of representation and also visualization of the results obtained using data mining methodologies. The intention is to formulate more than scientific ways to communicate facts that are of interest for the clinicians, medical students and even patients. Internet technologies as already widely established media support knowledge representation forms such as hypertext documents and structured knowledge components. The "Assist Me" decision support system for surgical treatment of cardiac patients integrates several forms of data mining and representation methodologies. We are showing a feasibility study in which scientific outcomes were forwarded to a broad group of potential users.

Artificial Intelligence↗
PubMed1986

A new strategy for clinical decision making: censors and neuroendocrinological diseases.

A patient rarely has a single, isolated disease. The situation is usually much more complex since the different parts of the human organism and its metabolism interact with each other on multiple levels and follow several feedback patterns. These interactions and feedback patterns become even more complex when the effects of the external environment are considered. When several diseases are present, the first steps in medical diagnosis are to determine whether one of the diseases interacts with ("Censors") or changes the significant symptoms, signs, or results of the laboratory tests of the other diseases. We will try, within this paper, to go beyond the scope of the first generation of Artificial Intelligence systems in medicine to determine the effects of two diseases on each other. One important part of the effect of two diseases on each other is the effect of Censors. In addition, causal reasoning, reasoning by analogy, and learning from precedents are important and necessary for a human-like expert in medicine. Their application to thyroid diseases, with an implemented system, are considered in this paper.

Artificial Intelligence↗
PubMed2004

Diagnostic classification of cancer using DNA microarrays and artificial intelligence.

The application of artificial intelligence (AI) to microarray data has been receiving much attention in recent years because of the possibility of automated diagnosis in the near future. Studies have been published predicting tumor type, estrogen receptor status, and prognosis using a variety of AI algorithms. The performance of intelligent computing decisions based on gene expression signatures is in some cases comparable to or better than the current clinical decision schemas. The goal of these tools is not to make clinicians obsolete, but rather to give clinicians one more tool in their armamentarium to accurately diagnose and hence better treat cancer patients. Several such applications are summarized in this chapter, and some of the common pitfalls are noted.

Artificial Intelligence↗
PubMed2001

AIM: a personal view of where I have been and where we might be going.

My own career in medical informatics and AI in medicine has oscillated between concerns with medical records and concerns with knowledge representation with decision support as a pivotal integrating issue. It has focused on using AI to organise information and reduce 'muddle' and improve the user interfaces to produce 'useful and usable systems' to help doctors with a 'humanly impossible task'. Increasingly knowledge representation and ontologies have become the fulcrum for orchestrating re-use of information and integration of systems. Encouragingly, the dilemma between computational tractability and expressiveness is lessening, and ontologies and description logics are joining the mainstream both in AI in Medicine and in Intelligent Information Management generally. It has been shown possible to scale up ontologies to meet medical needs, and increasingly ontologies are playing a key role in meeting the requirements to scale up the complexity of clinical systems to meet the ever increasing demands brought about by new emphasis on reduction of errors, clinical accountability, and the explosion of knowledge on the Web.

Artificial Intelligence↗
PubMed1994

Mid-term report on the Arden Syntax in a clinical event monitor.

In implementing a clinical event monitor (CEM), a decision-support system, we worked with an existing repository of clinical data (Keystone), fed from ancillary systems using HL7. The rules are written in the Arden Syntax, an ASTM standard for expressing medical knowledge as medical logic modules (MLMs). The Arden Syntax leaves unspecified the clinical data model and deductive database access language; we briefly describe our query language and the related medical concepts dictionary (MCD). This paper gives an overview of our implementation of the Arden Syntax, the MCD and the deductive database access language, with reasons for the major design decisions. Overall, less than a quarter of the development effort has gone into the Arden compiler and interpreter; the rest has focused on accessing the data and integrating with other systems. We feel that the Arden Syntax has proved its worth in writing rules; effort should now be focused on medical vocabularies and data models.

Artificial Intelligence↗
PubMed2000

A customisable framework for the assessment of therapies in the solution of therapy decision tasks.

In current medical research, a growing interest can be observed in the definition of a global therapy-evaluation framework which integrates considerations such as patients preferences and quality-of-life results. In this article, we propose the use of the research results in this domain as a source of knowledge in the design of support systems for therapy decision analysis, in particular with a view to application in oncology. We discuss the incorporation of these considerations in the definition of the therapy-assessment methods involved in the solution of a generic therapy decision task, described in the context of AI software development methodologies such as CommonKADS. The goal of the therapy decision task is to identify the ideal therapy, for a given patient, in accordance with a set of objectives of a diverse nature. The assessment methods applied are based either on data obtained from statistics or on the specific idiosyncrasies of each patient, as identified from their responses to a suite of psychological tests. In the analysis of the therapy decision task we emphasise the importance, from a methodological perspective, of using a rigorous approach to the modelling of domain ontologies and domain-specific data. To this aim we make extensive use of the semi-formal object oriented analysis notation UML to describe the domain level.

Artificial Intelligence↗
PubMed2003

Developing quality indicators and auditing protocols from formal guideline models: knowledge representation and transformations.

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically context-specific and case-mix-adjusted quality indicators that can model global or local levels of detail about the guideline parameterized by defining the reliability of each indicator or element of the guideline.

Algorithms↗
PubMed2000

A quality and safety framework for point-of-care clinical guidelines.

The electronic dissemination of medical knowledge in the form of executable clinical guidelines and decision support systems must be accompanied by comprehensive methods for ensuring the quality of their knowledge content and their safety in use. This paper outlines a set of quality and safety requirements, and reviews three current guideline technologies, the Arden Syntax, GLIF and PROforma, against these requirements. The approaches used in these technologies have different strengths, and we propose a general framework for ensuring quality and safety that combines them. This framework brings together the normal documentation standards of medical publishing, rigorous design methods from software engineering, and active safety management techniques from artificial intelligence.

Artificial Intelligence↗
PubMed1997

NéoGanesh: a working system for the automated control of assisted ventilation in ICUs.

Automating the control of therapy administered to a patient requires systems which integrate the knowledge of experienced physicians. This paper describes NéoGanesh, a knowledge-based system which controls, in closed-loop, the mechanical assistance provided to patients hospitalized in intensive care units. We report on how new advances in knowledge representation techniques have been used to model medical expertise. The clinical evaluation shows that such a system relieves the medical staff of routine tasks, improves patient care, and efficiently supports medical decisions regarding weaning. To be able to work in closed-loop and to be tested in real medical situations, NéoGanesh deals with a voluntarily limited problem. However, embedded in a powerful distributed environment, it is intended to support future extensions and refinements and to support reuse of knowledge bases.

Artificial Intelligence↗
PubMed1999

Integrating case based and rule based reasoning in a decision support system: evaluation with simulated patients.

We present a Web-based knowledge management and decision support system for Type I Diabetes patients' care. The tool exploits the integration of two methodologies, Case Based Reasoning and Rule Based Reasoning, and supports physicians in the definition of therapeutic strategies. Such a work is being integrated in the EU funded T-IDDM project architecture. In this paper we report a first evaluation obtained on simulated patients.

Artificial Intelligence↗
PubMed2004

Learning Boolean queries for article quality filtering.

Prior research has shown that Support Vector Machine models have the ability to identify high quality content-specific articles in the domain of internal medicine. These models, though powerful, cannot be used in Boolean search engines nor can the content of the models be verified via human inspection. In this paper, we use decision trees combined with several feature selection methods to generate Boolean query filters for the same domain and task. The resulting trees are generated automatically and exhibit high performance. The trees are understandable, manageable, and able to be validated by humans. The subsequent Boolean queries are sensible and can be readily used as filters by Boolean search engines.

Algorithms↗
PubMed2003

Meta-learning in reinforcement learning.

Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner. We tested our algorithm in both a simulation of a Markov decision task and in a non-linear control task. Our results show that the algorithm robustly finds appropriate meta-parameter values, and controls the meta-parameter time course, in both static and dynamic environments. We suggest that the phasic and tonic components of dopamine neuron firing can encode the signal required for meta-learning of reinforcement learning.

Algorithms↗
PubMed2006

A planning system based on Markov decision processes to guide people with dementia through activities of daily living.

Older adults with dementia often cannot remember how to complete activities of daily living and require a caregiver to aid them through the steps involved. The use of a computerized guidance system could potentially reduce the reliance on a caregiver. This paper examines the design and preliminary evaluation of a planning system that uses Markov decision processes (MDPs) to determine when and how to provide prompts to a user with dementia for guidance through the activity of handwashing. Results from the study suggest that MDPs can be applied effectively to this type of guidance problem. Considerations for the development of future guidance systems are presented.

Activities of Daily Living↗
PubMed1989

PEPTY: a knowledge-based program for assisting medical reasoning in peptic diseases.

PEPTY is a program developed with the aim of providing a diagnostic and therapeutic assistance in managing peptic diseases. Its theoretical basis is an accurate analysis of current concepts in peptic disease diagnosis and treatment. This was done by reviewing recent literature and consulting skilled gastroenterologists. The decision tree includes three sections dealing with diagnostic, therapeutic and monitoring problems. The diagnostic section starts by evaluating clinical data from patient history and physical examination; the diagnostic hypotheses given at this level are refined and eventually confirmed by further information in the following section. Here the decision tree becomes modular in that a proper therapeutic and monitoring pathway is defined for four disease classes: gastroduodenal peptic ulcer and duodenitis, gastro-oesophageal reflux, erosive gastritis, and chronic antral gastritis. In the therapeutic section a cost-benefit analysis of possible therapeutic choices is always performed, but the final decision is made by the user. Complications, side effects and treatment efficacy are also considered and the program finally suggests the appropriate maintenance treatment. Patient data display, storage and retrieval, and explanation facilities are supplied. The system can provide a 'second opinion' in the medical practice and may be a useful learning tool for medical students.

Artificial Intelligence↗
PubMedDate not supplied

The DELPHI method as a consensus and knowledge acquisition tool for the evaluation of the DIABETES system for insulin administration.

DIABETES is a decision-support system in the field of insulin administration. System performance evaluation is particularly difficult because of the absence of a uniform decision-making model followed by the specialists. The DELPHI method has been selected since it is appropriate for those domains where there is divergence among experts' opinions. The DELPHI approach helps a number of diabetologists arrive at a consensus and thus it facilitates performance evaluation and further knowledge acquisition. Insulin administration regimes, for 100 diabetic subjects, were proposed by DIABETES and five diabetologists (round 1). These suggestions were compiled and forwarded back to the specialists who proposed a second management approach (round 2). In each case, the experts were asked to justify their decision and comment on the suggestions of their colleagues and DIABETES. A novel scoring system for quantification of agreement was adopted. The DELPHI procedure significantly increased the agreement among the diabetologists from 67% to 84% (X2, p = 0.0001). The agreement between experts' and DIABETES recommendations was to a level of 54%. A total of 3500 comments were acquired by the experts.

Adult↗
PubMed1994

Information integration in a decision support system.

Electronic medical records pose a challenge because of the complex types of data which are included. Decision support systems must be able to deal effectively with these data types. In the expert system demonstrated here, a diversity of data types are included. These data are processed by three different methods. However, the different methods of processing are transparent to the user. An overall rule-based interface integrates the different methods into one comprehensive system.

Artificial Intelligence↗
PubMedDate not supplied

Disseminating medical knowledge: the PROforma approach.

Medical knowledge is traditionally disseminated via the publication of documents and through participation in clinical practice. Information technology offers to extend both modes of dissemination, via electronic publishing and virtual reality training, for example. AI promises even more radical changes through the possibility of publishing clinical expertise in the form of expert systems, which assist patient care through active decision support and workflow management. PROforma is a knowledge representation language that is designed to support this new mode of dissemination. It is based on an intuitive model of the processes of care and well-understood logical semantics. This paper provides a description of the language and associated software tools, and discusses its potential roles in, and implications for, medical knowledge publishing.

Artificial Intelligence↗