Case-based reasoning
HYDI: a hybrid system with feedback for diagnosing multiple disorders
HYDI: a hybrid system with feedback for diagnosing multiple disorders
A framework for knowledge-based temporal abstraction
Artificial Intelligence
The Application of Case-Based Reasoning to the Tasks of Health Care Planning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Integration Rules and Cases for the Classification Task
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Case-Based Reasoning Technology, From Foundations to Applications
The Use of Exogenous Knowledge to Learn Bayesian Networks from Incomplete Databases
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
MacRad: Radiology Image Resource with a Case-Based Retrieval System
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Case-Based Reasoning in CARE-PARTNER: Gathering Evidence for Evidence-Based Medical Practice
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Knowledge acquisition planning: gaining expertise through experience
Knowledge acquisition planning: gaining expertise through experience
Case-based retrieval to support the treatment of end stage renal failure patients
Artificial Intelligence in Medicine
Case-based reasoning in the health sciences: What's next?
Artificial Intelligence in Medicine
Case-based object recognition for airborne fungi recognition
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
The socio-organizational age of artificial intelligence in medicine
Artificial Intelligence in Medicine
Case-Based Reasoning in the Health Sciences: Why It Matters for the Health Sciences and for CBR
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Multi-modal and multi-purpose case-based reasoning in the health sciences
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Case-based systems in health sciences: a case study in the field of stress
WSEAS TRANSACTIONS on SYSTEMS
Artificial Intelligence in Medicine
eXiT*CBR: A framework for case-based medical diagnosis development and experimentation
Artificial Intelligence in Medicine
A modular database architecture enabled to comparative sequence analysis
Transactions on large-scale data- and knowledge-centered systems IV
Transductive cost-sensitive lung cancer image classification
Applied Intelligence
Flexible case-based retrieval for comparative genomics
Applied Intelligence
Engineering Applications of Artificial Intelligence
Synergistic case-based reasoning in medical domains
Expert Systems with Applications: An International Journal
A knowledge-based architecture for the management of patient-focused care pathways
Applied Intelligence
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Background Supporting medical decision making is a complex task, that offers challenging research issues to Artificial Intelligence (AI) scientists. The Case-based Reasoning (CBR) methodology has been proposed as a possible means for supporting decision making in this domain since the 1980s. Nevertheless, despite the variety of efforts produced by the CBR research community, and the number of issues properly handled by means of this methodology, the success of CBR systems in medicine is somehow limited, and almost no research product has been fully tested and commercialized; one of the main reasons for this may be found in the nature of the problem domain, which is extremely complex and multi-faceted. Materials and methods In this environment, we propose to design a modular architecture, in which several AI methodologies cooperate, to provide decision support. In the resulting context CBR, originally conceived as a well suited reasoning paradigm for medical applications, can extend its original roles, and cover a set of additional tasks. Results and conclusions As an example, in the paper we will show how CBR can be exploited for configuring the parameters relied upon by other (reasoning) modules. Other possible ways of deploying CBR in this domain will be the object of our future investigations, and, in our opinion, a possible research direction for people working on CBR in the health sciences.