DREAM: a shell-like software system for medical data analysis and decision support

Kuncheva, Ludmila I.; Andreeva, Kostadinka

doi:10.1016/0169-2607(93)90001-2

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Unattended patient registration and randomization systems using telephone and synthesized voice [65,66], fax and optical character recognition [67] or Web-technology [68,69] are in common usage and the service is offered by several commercial companies (eg, Clinphone.ivr TM from ClinPhone Ltd). The commercial world also offers a diversity of applications and services for data management or for work process support.…”

Section: Software Architecturementioning

confidence: 99%

“…Finally, research on tools for statistical analysis of data from observational research and clinical trials have focused on the design of knowledge-based user interfaces for analytical tools [66], the development of data-mining tools to be embedded in analytical software [67], and knowledge-based methods for manipulation of aggregate data and creation of statistical tables [68,69].…”

Section: Software Architecturementioning

confidence: 99%

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Design aspects of a distributed clinical trials information system

Oliveira¹,

Salgado

2006

Clinical Trials

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Backgound Clinical trials informatics has evolved through the development of multiple applications addressing distinct parts of the clinical trials cycle. This setting creates difficulties in the transport and sharing of data among applications that serve a common functionality. Purpose We present an alternative approach for the design of clinical trials information systems consisting of loosely coupled components standing on a comprehensive model of the global clinical trial process. Methods We describe how such a structure is able to support the development and implementation of complex knowledge-driven modules, such as automated database query systems, reporting systems and intelligent data-analysis tools, and how currently available internet technologies may be used to support the independent development of applications and collaboration between researchers. Results These principles were applied to the development of a fully functional system that has been in production for eight years in a diversity of pharmaceutical and academic drug trials. Marked time savings in database set-up and statistical reporting have been documented, as well as good reliability in the selection of appropriate statistical methods to clinical trial data analysis. Conclusions In order to meet the expected functionality and to avoid the proliferation of databases and software applications, clinical trials information systems need to be based on a generic model of clinical trials and on a distributed architecture.

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Section: Software Architecturementioning

confidence: 99%

Section: Software Architecturementioning

confidence: 99%

Design aspects of a distributed clinical trials information system

Oliveira¹,

Salgado

2006

Clinical Trials

View full text Add to dashboard Cite

show abstract

“…(b) Prognosis [2,14,22,25,33,72,73,85,86,98,99,110,165,166]. Prognosis is the process of selectively gathering information concerning a patient, and interpreting it according to previous knowledge, in order to predict the future development of the patient's condition (Section A.2.1).…”

Section: A31 According To the Medical Taskmentioning

confidence: 99%

“…(a) Unidimensional, binary genome [2,5,[7][8][9]20,22,24,25,29,31,33,42,53,58,60,70,72,73,83,85,86,88,98,99,108,110,115,125,138,153,154,[165][166][167]174,176,179,187,[189][190][191]197]. This is the most popular representation in genetic algorithms because it is simple to use and applicable to almost any problem [171].…”

Section: A32 According To the Evolutionary Techniquementioning

confidence: 99%

Coevolutionary Fuzzy Modeling

Reyes¹

2004

Lecture Notes in Computer Science

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This thesis presents Fuzzy CoCo, a novel approach for system design, conducive to explaining human decisions. Based on fuzzy logic and coevolutionary computation, Fuzzy CoCo is a methodology for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (precision) and linguistic representation (interpretability). From a numeric point of view, fuzzy systems exhibit nonlinear behavior and can handle imprecise and incomplete information. Linguistically, they represent knowledge in the form of rules, a natural way for explaining decision processes. Fuzzy modeling-meaning the construction of fuzzy systems-is an arduous task, demanding the identification of many parameters. This thesis analyses the fuzzy-modeling problem and different approaches to coping with it, focusing on evolutionary fuzzy modelingthe design of fuzzy inference systems using evolutionary algorithms-which constitutes the methodological base of my approach. In order to promote this analysis the parameters of a fuzzy system are classified into four categories: logic, structural, connective, and operational. The central contribution of this work is the use of an advanced evolutionary techniquecooperative coevolution-for dealing with the simultaneous design of connective and operational parameters. Cooperative coevolutionary fuzzy modeling succeeds in overcoming several limitations exhibited by other standard evolutionary approaches: stagnation, convergence to local optima, and computational costliness. Designing interpretable systems is a prime goal of my approach, which I study thoroughly herein. Based on a set of semantic and syntactic criteria, regarding the definition of linguistic concepts and their causal connections, I propose a number of strategies for producing more interpretable fuzzy systems. These strategies are implemented in Fuzzy CoCo, resulting in a modeling methodology providing high numeric precision, while incurring as little a loss of interpretability as possible. After testing Fuzzy CoCo on a benchmark problem-Fisher's Iris data-I successfully apply the algorithm to model the decision processes involved in two breast-cancer diagnostic problems: the WBCD problem and the Catalonia mammography interpretation problem. For the WBCD problem, Fuzzy CoCo produces systems both of high performance and high interpretability, comparable (if not better) than the best systems demonstrated to date. For the Catalonia problem, an evolved high-performance system was embedded within a web-based tool-called COBRA-for aiding radiologists in mammography interpretation. Several aspects of Fuzzy CoCo are thoroughly analyzed to provide a deeper understanding of the method. These analyses show the consistency of the results. They also help derive 1 "Y tuve muchos maestros de que aprender. Sólo conocían su ciencia y el deber." Charly García. A...

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Feature Extraction and Evolution Based Pattern Recognition

Nam

Rhee

2006

Lecture Notes in Computer Science

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DREAM: a shell-like software system for medical data analysis and decision support

Cited by 8 publications

References 18 publications

Design aspects of a distributed clinical trials information system

Design aspects of a distributed clinical trials information system

Coevolutionary Fuzzy Modeling

Feature Extraction and Evolution Based Pattern Recognition

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