Integrating CBR into the health care organization

Alexandrini, Fábio; Krechel, Dirk; Maximini, K.; Wangenheim, Aldo von

doi:10.1109/cbms.2003.1212778

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…One such pathway is the development of personalized diagnostic model based on patient similarity. Case-based Reasoning (CBR), an artificial intelligent approach is very close to human reasoning, and has become a well-adapted methodology in medicine for developing personalized diagnostic model based on patient similarity measure [2]. CBR methodology adapts instance based learning, which aims to learn and derive insights from patients similar to the query patient and then analyze the derived insights in the diagnostic model to provide personalized diagnostic/treatment recommendations to the query patient.…”

Section: Case-based Decision Support System With Contextual Bandits Lmentioning

confidence: 99%

Case-Based Decision Support System with Contextual Bandits Learning for Similarity Retrieval Model Selection

Sekar

Wang

2018

Knowledge Science, Engineering and Management

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Case-based reasoning has become one of the well-sought approaches that supports the development of personalized medicine. It trains on previous experience in form of resolved cases to provide solution to a new problem. In developing a case-based decision support system using case-based reasoning methodology, it is critical to have a good similarity retrieval model to retrieve the most similar cases to the query case. Various factors, including feature selection and weighting, similarity functions, case representation and knowledge model need to be considered in developing a similarity retrieval model. It is difficult to build a single most reliable similarity retrieval model, as this may differ according to the context of the user, demographic and query case. To address such challenge, the present work presents a case-based decision support system with multi-similarity retrieval models and propose contextual bandits learning algorithm to dynamically choose the most appropriate similarity retrieval model based on the context of the user, query patient and demographic data. The proposed framework is designed for DESIREE project, whose goal is to develop a web-based software ecosystem for the multidisciplinary management of primary breast cancer.

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Section: Case-based Decision Support System With Contextual Bandits Lmentioning

confidence: 99%

Case-Based Decision Support System with Contextual Bandits Learning for Similarity Retrieval Model Selection

Sekar

Wang

2018

Knowledge Science, Engineering and Management

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“…As one of the common techniques for DSS, case-based reasoning (CBR) is a problem solving paradigm that is fundamentally different from other major artificial intelligent approaches (Aamodt and Plaza, 1994). Instead of relying solely on the general knowledge of a problem domain, CBR provides a conceptual framework, by which to store operator experiences and subsequently provide access to these experiences to other operators to facilitate situation assessment and solution formulation processes (Alexandrini et al, 2003). A CBR framework is a cycle (Kolodner, 1992) of the following steps:…”

Section: Outbound Operations In Warehousesmentioning

confidence: 99%

“…As one of the common techniques for DSS, case‐based reasoning (CBR) is a problem solving paradigm that is fundamentally different from other major artificial intelligent approaches (Aamodt and Plaza, 1994). Instead of relying solely on the general knowledge of a problem domain, CBR provides a conceptual framework, by which to store operator experiences and subsequently provide access to these experiences to other operators to facilitate situation assessment and solution formulation processes (Alexandrini et al , 2003). A CBR framework is a cycle (Kolodner, 1992) of the following steps:retrieval of the most similar case or cases;reusing of information and knowledge in that case to solve the problem;revising the proposed solution; andretaining the parts of the experience that are likely to be useful for future problem solving tasks.However, Liao (2000) and Liu and Ke (2007) argued that there are some considerations in integrating CBR into the architecture of DSS to obtain appropriate learning abilities for future problem solving.…”

Section: Literature Reviewmentioning

confidence: 99%

A decision support system to facilitate warehouse order fulfillment in cross‐border supply chain

Lam

Choy

Chung

2011

Journal of Manufacturing Technology Management

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2002),"Linking warehouse complexity to warehouse planning and control structure: An exploratory study of the use of warehouse management information systems"If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to provide a decision support system (DSS) to enhance the performance of cross-border supply chain, the goal of which is to improve order planning and fulfill customer orders within the warehouse. Design/methodology/approach -An intelligent DSS, namely order picking planning system (OPPS) with the adoption of case-based reasoning, is proposed to support managers in making appropriate order fulfilling decisions when an order involves cross-border activities. Similar cases in the past are retrieved and adapted in reference to the new order. A case study is then conducted to illustrate the feasibility and effectiveness of the system. Findings -Recommendations are given to replace the objective decision-making process in cross-border supply chain with the help of the DSS. The warehouse order planning time has been reduced and useful information from past order records can be applied to solve new problems. Originality/value -With the increasing demand for material sourcing across different places, cross-border supply chain has raised the concern for manufacturers to seek lower material and rental costs. The focus on warehouse operations can increase efficiency in order delivery by considering cross-border requirements.

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“…Face-to-face sharing among social workers is the dominant method of cross-referencing of similar cases, whereas learning from organizational databases and artifacts is often under-developed. Computerized decision-support or expert systems have been suggested or experimented with in social care (Carlson, 1999; Goodman et al, 1989; Nolan, 1997; Schoech et al, 2000, 2001), although more are found in the health care areas (Alexandrini et al, 2003; Chang, 2005; Chang et al, 2004; Frize and Walker, 2000; Hsu and Ho, 2004; López and Plaze, 1997; Trivedi et al, 2004; Zhang et al, 1999).…”

Section: Rule-based and Case-based Reasoningmentioning

confidence: 99%

A pilot study on a knowledge-based case library to support suicide risk assessment

Cheung

Wang

Leung

2011

International Social Work

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This article describes the system development processes for an electronic case library, built to facilitate and support the suicide prevention service provided by a social service organization in Hong Kong. It is a knowledge management system in which both 'rule-based reasoning' (RBR) and 'casebased reasoning' (CBR) are adopted. Initial test results, potentials and limitations of this knowledge management approach are discussed.

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Integrating CBR into the health care organization

Cited by 11 publications

References 18 publications

Case-Based Decision Support System with Contextual Bandits Learning for Similarity Retrieval Model Selection

Case-Based Decision Support System with Contextual Bandits Learning for Similarity Retrieval Model Selection

A decision support system to facilitate warehouse order fulfillment in cross‐border supply chain

A pilot study on a knowledge-based case library to support suicide risk assessment

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