Emergent Frameworks for Decision Support Systems

Knowledge is generated and accessed from heterogeneous spaces. The recent advances in in-formation technologies provide enhanced tools for improving the efficiency of knowledge-based decision support systems. The purpose of this paper is to present the frameworks for developing the optimal blend of technologies required in order to better the knowledge acquisition and reuse in large scale decision making environments. The authors present a case study in the field of clinical decision support systems based on emerging technologies. They consider the changes generated by the upraising social technologies and the challenges brought by the interactive knowledge building within vast online communities.Knowledge Acquisition, CDDSS, 2D Barcodes, Mobile Interface

Composite Ontology-Based Medical Diagnosis Decision Support System Framework

Current medical decision support systems have evolved from the automation of medical decision routines to improving the quality of health care services. Knowledge-based systems, compared to conventional data-driven techniques, are promising to support medical decision making. However, knowledge acquisition is usually a bottleneck in the process of developing such systemsOne possibility for acquiring medical knowledge, particularly tacit knowledge, is to use data or cases in both syntactic and semantic ways. Case-based Reasoning (CBR) methodology provides a practical way of problem solving with recalled knowledge memory of solved cases. To reduce the difficulty of knowledge acquisition, this paper proposes a design of the system framework that utilizes the simplified medical knowledge:disease-symptom ontology for prediagnosis, given patients symptoms and signs as input. In the first stage, simple pattern matching is used to gather candidate diseases in diagnosis. Following that, case-based reasoning is used to refine diagnostic decision. The case base is structured with ontological knowledge model. The case retrieval process is based on semantic similarity. The diagnostic system uses a composite knowledge base, and will allow automated diagnosis recommendation. The system framework also aims at facilitating semantic explanations to the solution derived

Emergent Frameworks for Decision Support Systems

Knowledge is generated and accessed from heterogeneous spaces. The recent advances in in-formation technologies provide enhanced tools for improving the efficiency of knowledge-based decision support systems. The purpose of this paper is to present the frameworks for developing the optimal blend of technologies required in order to better the knowledge acquisition and reuse in large scale decision making environments. The authors present a case study in the field of clinical decision support systems based on emerging technologies. They consider the changes generated by the upraising social technologies and the challenges brought by the interactive knowledge building within vast online communities

Research Proposal: Preference Acquisition through Reconciliation of Inconsistencies

The quality of performance of a decision-support system (or an expert system) is determined to a large extent by its underlying preference model (or knowledge base). The difficulties in preference and knowledge acquisition make them a major focus of current research in decision-support and expert systems. Researchers have used various concepts to develop promising acquisition techniques. One of the concepts used is knowledge maintenence where the knowledge base is changed in response to incorrect or inadequate performance by the expert system. This dissertation investigates a preference acquisition technique based on the reconciliation of inconsistencies between the preference model and the decision maker by allowing the decision maker to modify the preference model interactively. The technique can be used in the class of decision-support systems which objectively evaluate competing plans and select the best plan. The technique will be implemented in the domain of evaluating three-dimensional (3-D) radiation treatment plans. Another major aim of the dissertation is to develop a clinically-relevant objective plan-evaluation model for 3-D radiation treatment plans, and to build a clinical decision-support system to assist in that task using the new preference acquisition method

How to help intelligent systems with different uncertainty representations cooperate with each other

In order to solve a complicated problem one must use the knowledge from different domains. Therefore, if one wants to automatize the solution of these problems, one has to help the knowledge-based systems that correspond to these domains cooperate, that is, communicate facts and conclusions to each other in the process of decision making. One of the main obstacles to such cooperation is the fact that different intelligent systems use different methods of knowledge acquisition and different methods and formalisms for uncertainty representation. So an interface f is needed, 'translating' the values x, y, which represent uncertainty of the experts' knowledge in one system, into the values f(x), f(y) appropriate for another one. The problem of designing such an interface as a mathematical problem is formulated and solved. It is shown that the interface must be fractionally linear: f(x) = (ax + b)/(cx + d)

Making diagnosis explicit

What is good diagnostic practice? The answer is elusive for many medical students and equally puzzling for those trying to build effective medical decision support systems. Much of the problem lies in the difficult of 'getting at' diagnosis. Expert diagnosticians find it difficult to introspect on their own strategies, thus making it difficult to pass on their expertise.Traditional knowledge acquisition methods are designed for gathering static domain knowledge and are inappropriate for the acquisition of knowledge about the diagnos¬ tic 'task'. More advanced knowledge acquisition methodologies, particularly those which focus on the modelling of problem-solving knowledge seem to hold more promise, but are not sufficiently practicable to allow anyone other than a knowledge engineer to operate directly. Given the difficulty experts have in accessing their own diagnostic strategies what is needed is a tool which would enable diagnosticians themselves to directly formu¬ late and experiment with their own methods of diagnosis.This research describes the development of a knowledge acquisition methodology geared specifically towards the exposition of medical diagnosis. The methodology is implemented as a toolkit enabling exploration and construction of medical diagnostic models and production of model-based medical diagnostic support systems. The toolkit allows someone skilled in diagnosis to articulate their diagnostic strategy so that it can be used by those with less experience

A machine learning approach to automated construction of knowledge bases for expert systems for remote sensing image analysis with GIS data

Knowledge-based remote sensing image analysis with GIS data is acknowledged as a promising technique. However, the difficulty in knowledge acquisition, a well-known bottleneck in building knowledge-based systems, impedes the adoption of this technique. Automating knowledge acquisition is therefore in demand. This paper presents a machine learning approach to automated construction of knowledge bases for image analysis expert systems integrating remotely sensed and GIS data. The methodology applied in the study is based on inductive learning techniques in machine learning, a subarea of artificial intelligence. It involves training with examples from remote sensing and GIS data, learning using the inductive principles, decision tree generating, rule generating from the decision tree, and knowledge base building for an image analysis expert system. This method was used to construct a knowledge base for wetland classification of Par Pond on the Savannah River Site, SC, using SPOT image data and GIS data. The preliminary results show that this method can provide an effective approach to integration of remotely sensed and GIS data in geographic information processing

Knowledge Acquisition and Structuring by Multiple Experts in a Group Support Systems Environment

This study addresses the impact of Group Decision Support Systems (GDSS) on expert system development by multiple Domain Experts. Current approaches to building expert systems rely heavily on knowledge acquisition and prototyping by a Knowledge Engineer working directly with the Domain Expert. Although the complexity of knowledge domains and new organizational approaches demand the involvement of multiple experts, standard procedures limit the ability of the Knowledge Engineer to work with more than one expert at a time. Group Decision Support Systems offer a networked computerized environment for group work activities, in which multiple experts may express their ideas concurrently and anonymously through the electronic channel. GDSS systems have been widely used in other applications to support idea generation, conflict management, and the organizing, prioritizing, and synthesizing of ideas. The effects of many group process and technical factors on GDSS have been widely studied and documented. A review of the literature on expert systems, GDSS, and GDSS in relation to expert systems was conducted. Knowledge gained from this review was applied in the construction of an exploratory research model intended to provide the necessary breadth to identify factors worthy of future, more statistically-based, investigation. Domain Experts represented by college students were charged with developing and prioritizing ideas for creating a pre-prototypical expert system. The treatment group worked in a GDSS environment with a facilitator; a control group worked with a facilitator but without the assistance of GDSS. Each group then exchanged facilitators and technology to address another real-life problem. Additional groups worked with GDSS over time, addressing both problems. Data were gathered, analyzed and discussed relating to group efficiency factors, group process factors, attitudinal factors, and product quality factors. Independent Knowledge Engineers and Domain Experts evaluated the validity and verifiability of the group products. Analysis focused on the effect of GDSS in facilitating the acquisition and structuring of ideas for expert systems by multiple Domain Experts

Clinical decision support using Open Data

First Online: 18 May 2020.The growth of Electronical Health Records (EHR) in healthcare has been gradual. However, a simple EHR system has become inefficient in supporting health professionals on decision making. In this sense, the need to acquire knowledge from storing data using open models and techniques has emerged, for the sake of improving the quality of service provided and to support the decision-making process. The usage of open models promotes interoperability between systems, communicating more efficiently. In this sense, the OpenEHR open data approach is applied, modelling data in two levels to distinguish knowledge from information. The application of clinical terminologies was fundamental in this study, in order to control data semantics based on coded clinical terms. This article culminated from the conceptualization of the knowledge acquisition process to represent Clinical Decision Support, using open data models.The work has been supported by FCT–Fundação para a Ciência e Tec-nologia within the Project Scope UID/CEC/00319/2019 and DSAIPA/DS/0084/2018