Case-based reasoning for medical decision support tasks: The Inreca approach

Althoff, Klaus-Dieter; Bergmann, Ralph; Wess, Stefan; Manago, Michel; Auriol, Eric; Larichev, Oleg I.; Bolotov, Alexander; Zhuravlev, Yurii I.; Gurov, Sergey V.

doi:10.1016/s0933-3657(97)00038-9

Cited by 47 publications

(19 citation statements)

References 18 publications

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“…In the area of medical informatics, a knowledge base is typically useful in supporting the decision making involved in medical prescription [19]. Over the past two decades, numerous knowledge-based techniques have been successfully applied to prescription [7,27]. However, Schmidt et al [29] argued that CBR is the one technique which is particularly suitable for medical knowledge based systems.…”

Section: Case-based Reasoning In Medical Prescriptionmentioning

confidence: 99%

A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center

Ting

Kwok

Tsang

et al. 2011

Knowledge-Based Systems

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ObjectiveWith the increased complexity and uncertainty in drug information, issuing medical prescriptions has become a vexing issue. As many as 240,000 medicines are available on the market, so this paper proposes a novel approach to the issuing of medical prescriptions. The proposed process will provide general practitioners (GPs) with medication advice and suggest a range of medicines for specific medical conditions by taking into consideration the collective pattern as well as the individual preferences of physicians' prescription decisions. Methods and MaterialA hybrid approach is described that uses a combination of case-based reasoning (CBR) and Bayesian reasoning. In the CBR process, all the previous knowledge retrieved via similarity measures is made available for the reference of physicians as to what medicines have been prescribed (to a particular patient) in the past. After obtaining the results from CBR, Bayesian reasoning is then applied to model the prescription experience of all physicians within the organization. By comparing the two sets of results, more refined recommendations on a range of medicines are suggested along with the ranking for each recommendation. ResultsTo validate the proposed approach, a Hong Kong medical center was selected as a testing site. Through application of the hybrid approach in the medical center for a period of one month, the results demonstrated that the approach produced satisfactory performance in terms of user satisfaction, ease of use, flexibility and effectiveness. In addition, the proposed approach yields better results and a faster learning rate than This is the Pre-Published Version. 2 when either CBR or Bayesian reasoning are applied alone. ConclusionEven with the help of a decision support system, the current approach to anticipating what drugs are to be prescribed is not flexible enough to cater for individual preferences of GPs, and provides little support for managing complex and dynamic changes in drug information. Therefore, with the increase in the amount of information about drugs, it is extremely difficult for physicians to write a good prescription. By integrating CBR and Bayesian reasoning, the general practitioners' prescription practices can be retrieved and compared with the collective prescription experience as modeled by probabilistic reasoning. As a result, physicians can select the drugs which are supported by informed evidential decisions. That is, they can take into consideration the pattern of decisions made by other physicians in similar cases.

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Section: Case-based Reasoning In Medical Prescriptionmentioning

confidence: 99%

A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center

Ting

Kwok

Tsang

et al. 2011

Knowledge-Based Systems

View full text Add to dashboard Cite

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“…Case Based Reasoning (1; 2) techniques have been shown to be useful in a multitude of domains, with widespread applications ranging from the optimization of autoclave loading (3), the diagnosis and treatment of many medical problems (4), to the synthesis of high quality expressive music (5).…”

Section: Introductionmentioning

confidence: 99%

Improving Reinforcement Learning by Using Case Based Heuristics

Bianchi

Ros

Mántaras

2009

Case-Based Reasoning Research and Development

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Abstract. This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HARL is a subset of RL that makes use of a heuristic function derived from a case base, in a Case Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Q-Learning is also proposed. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and results obtained shows that using CB-HARL, the agents learn faster than using either RL or HARL methods.

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“…Based on the above, we have devised a compositional adaptation approach that is applicable to a specialized class of cases, whereby the case solution is a composite of individual subsolutions each associated with a problem-defining attribute of a case [2]. Specific to our problem description, the case solution-i.e.…”

Section: Figure 1 Process Flow Diagram Showing the Cbr-mediated Compmentioning

confidence: 99%

“…The rationale for our approach is grounded in the principle that since inter-case similarity is determined at an attribute-level, therefore a finegrained solution adaptation should be achieved by adapting the attribute-specific sub-solutions based on the attribute's similarity measure derived when comparing the past and present case. As per our approach, for a relatively imperfect match (at the attribute-level) the associated subsolution will need to be adapted based on the attribute-specific similarity measure between the past and current case [2].…”

Section: Figure 1 Process Flow Diagram Showing the Cbr-mediated Compmentioning

confidence: 99%

“…Personalization of generic healthcare information, as per an individual's HP, provides an interesting application of CBR [2], in particular the CBR-mediated Case Adaptation process [3,4]. The general idea is to dynamically author a Personalized Health Information Package (PHIP) by applying CBR techniques to a cohort of past cases, where each medical-practitioner generated case represents a relationship between the HP component (the situation) and the corresponding PHIP component (the solution/action).…”

Section: Introductionmentioning

confidence: 99%

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