156,321 research outputs found

    Design of a goal ontology for medical decision-support

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    Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references (leaves 34-36).Objectives: There are several ongoing efforts aimed at developing formal models of medical knowledge and reasoning to design decision-support systems. Until now, these efforts have focused primarily on representing content of clinical guidelines and their logical structure. The present study aims to develop a computable representation of health-care providers' intentions to be used as part of a framework for implementing clinical decision-support systems. Our goal is to create an ontology that supports retrieval of plans based on the intentions or goals of the clinician. Methods: We developed an ontological representation of medical goals, plans, clinical scenarios and other relevant entities in medical decision-making. We used the resulting ontology along with an external ontology inference engine to simulate selection of clinical recommendations based on goals. The ontology instances used in the simulation were modeled from two clinical guidelines. Testing the design: Thirty-two clinical recommendations were encoded in the experimental model. Nine test cases were created to verify the ability of the model to retrieve the plans. For all nine cases, plans were successfully retrieved. Conclusion: The ontological design we developed supported effective reasoning over a medical knowledge base.(cont.) The immediate extension of this approach to be fully developed in medical applications may be partially limited by the lack of available editing tools. Many efforts in this area are currently aiming to the development of needed technologies.by Davide Zacacagnini [i.e. Zaccagnini].S.M

    Decision Support Systems

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    Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

    Decision making based on quality-of-information a clinical guideline for chronic obstructive pulmonary disease scenario

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    Springer - Series Advances in Intelligent and Soft Computing, vol. 79In this work we intend to advance towards a computational model to hold up a Group Decision Support System for VirtualECare, a system aimed at sustaining online healthcare services, where Extended Logic Programs (ELP) will be used for knowledge re-presentation and reasoning. Under this scenario it is possible to evaluate the ELPs making in terms of the Quality-of-Information (QoI) that is assigned to them, along the several stages of the decision making process, which is given as a truth value in the interval 0ā€¦1, i.e., it is possible to provide a measure of the value of the QoI that supports the decision making process, an end in itself. It will be also considered the problem ofQoI evaluation in a multi-criteria decision setting, being the criteria to be fulfilled that of a Clinical Guideline (CG) for Chronic Obstructive Pulmonary Disease

    Decision support system for the diagnosis of schizophrenia disorders

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    Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ). The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34%) and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.Universidade Federal de SĆ£o Paulo (UNIFESP) Escola Paulista de Medicina Departamento de PsiquiatriaUniversidade Federal de SĆ£o Paulo (UNIFESP) Escola Paulista de Medicina Departamento de InformĆ”tica MĆ©dicaUNIFESP, EPM, Depto. de PsiquiatriaUNIFESP, EPM, Depto. de InformĆ”tica MĆ©dicaSciEL

    Pedagogical reasoning of pre-service teachers: Juggling priorities and managing resources

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    The purpose of this study was to examine the cognitive and motivational factors related to the development of self-regulated teaching. These factors were defined by Shulman (1986; 2006) as theoretical, practical and moral knowledge for pedagogical reasoning. This study occurred in a clinical, instructional setting specifically designed to provide pre-service teachers with the environmental factors necessary to support the development of self-regulated instructional decision making. These factors included specific modeling of teacher thinking, regular formative feedback, instructional autonomy and strategic reflection. An instructional sequence based on Schunk and Zimmermanā€™s (1998; 2007) Model of the Development of Self-Regulatory Skill was applied which allowed pre-service teachers opportunities to observe, emulate, control and self-regulate teaching decisions. This study used interpretive content analysis to examine how preservice teachers recognize teaching decisions and apply three types of professional knowledge in order to justify decisions related to reading instruction for elementary students

    A review of clinical decision-making: Models and current research

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    Aims and objectives: The aim of this paper was to review the current literature with respect to clinical decision-making models and the educational application of models to clinical practice. This was achieved by exploring the function and related research of the three available models of clinical decision making: information processing model, the intuitive-humanist model and the clinical decision making model. Background: Clinical decision-making is a unique process that involves the interplay between knowledge of pre-existing pathological conditions, explicit patient information, nursing care and experiential learning. Historically, two models of clinical decision making are recognised from the literature; the information processing model and the intuitive-humanist model. The usefulness and application of both models has been examined in relation the provision of nursing care and care related outcomes. More recently a third model of clinical decision making has been proposed. This new multidimensional model contains elements of the information processing model but also examines patient specific elements that are necessary for cue and pattern recognition. Design: Literature review Methods: Evaluation of the literature generated from MEDLINE, CINAHL, OVID, PUBMED and EBESCO systems and the Internet from 1980 ā€“ November 2005

    User-centered visual analysis using a hybrid reasoning architecture for intensive care units

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    One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

    Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning

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    Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider disease manifestations and provides physicians with treatment solutions from similar previous cases for reference. The proposed DSS supports natural language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease classification with the help of the ontology
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