Case-based reasoning in the health sciences

Artificial Intelligence in Medicine (2006) 36, 121—125

http://www.intl.elsevierhealth.com/journals/aiim

GUEST EDITORIAL

Case-based reasoning in the health sciences 1. Introduction Case-based reasoning (CBR) in the health sciences is a particularly active area of research, as attest in particular several recent workshops conducted at ICCBR-03 and ECCBR-04. The 2nd Workshop on casebased reasoning in the health sciences, which took place at ECCBR-04 in Madrid, Spain, was the opportunity to organize this special issue based on the four best papers presented at the workshop. As the health sector is continuing to expand due to population lifespan increase, advanced decision-support systems become more and more sought after in the evolution of medicine towards a more standardized and computerized science. CBR systems are notable examples of decision-support systems as they base their recommendations on the subset of the most similar experiences previously encountered. It is thus a method of choice for such experimental sciences as the natural and life sciences, and in particular for biology and medicine. This editorial aims at providing the reader with the main concepts of case-based reasoning that will be helpful for understanding the included papers, as well as at explaining how case-based reasoning has been applied until now to the health sciences. Finally, it introduces the articles selected.

2. Case-based reasoning methodology Case-based reasoning (CBR) is a problem-solving methodology that proposes to reuse previously solved and memorized problem situations, called cases. A case is a concrete problem-solving experience. CBR is quite different from any other artificial intelligence (AI) problem-solving methodology in that it searches for the most specific case or set of cases to reuse. Most other AI problem solvers, such as Bayesian networks, neural networks, or decision trees, have a known tendency to over-

generalize [1], and thus cannot shape their behaviour according to the most specific instance in their training set. Thus, it can be expected that CBR can achieve excellent accuracy provided that it capitalizes on a consequent and broad-spanning memory of cases. One of the main assets of CBR is its eagerness to learn. Learning in CBR can be as simple as memorizing a new case CBR has developed from these premises and been found suitable to solve any type of problem, but preferably experimental sciences problems, where cases are readily available in the form of patients, living things, or natural phenomena. Traditionally, CBR’ reasoning cycle [2] starts with the presentation to the system of a new problem to solve (see Fig. 1). Let us take the example of a patient undergoing a first visit at a physician’s office. The problem to solve, called the new case, is here to determine a diagnosis for the patients’ complaints–— we could say problem list from the physician’s standpoint. For this task, the reasoning of the system proceeds through the following steps [2]: 1. Interpretation (Ra): given the description of a new case, the system constructs, by interpretation, the initial situation expressed in the knowledge representation language of the system. Abstraction is the main reasoning type used here and in particular temporal abstraction is often pertinent in a medical domain [3]. 2. Retrieve (Ri): the case base is searched for the most similar cases. The pertinent method is casebased retrieval, which is based on applying a similarity measure between the new case and either all the cases in memory, or a subset of these. The resulting set of cases, called the retrieved cases, is ranked by decreasing order of similarity measure. This means that the most similar cases are at the top of this list. 3. Reuse (Ru): the case or cases at the top of the list previously retrieved is reused. Its solution is then

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Figure 1

The classical CBR reasoning cycle.

adapted. Some systems perform instead of an adaptation an interpretation. It depends upon the type of task to solve. For a diagnostic task, an adaptation is more appropriate. Many CBR systems do not perform a complete reuse step: these are case-based retrieval systems, for which the reasoning process has for main goal to retrieve the most pertinent cases in memory. The end result of the reuse step is the production of a proposed solution to the new case. This constitutes the solved case. It is ready to be tested in the application domain, which occurs outside of the CBR reasoning cycle. 4. Revise (Rv): this step consists, after testing the solved case in the real world environment, in repairing the causes of any error that arose during testing. Testing the case may also take the form of performing some evaluation of it against a teacher, an expert, a simulation or model, or known solutions from a test-set. At the end of this step, and eventually repair actions, the solved case becomes the tested — or repaired — case since the system needs to memorize only valid solutions. A CBR system is known for learning from both good results, and failures, but the latter need to be identified precisely in order to not repeat them in the future. 5. Retain (Rt): the current case, corresponding to the tested case, is ready to be added to the memory. This step is also called memorization. The complete solution is memorized with the target case solved.

In our medical example, the solution will be a list of diagnoses explaining the problem list of the patient. This solution will have been inferred by the system from the solutions memorized with the most similar patient cases in memory. Among the important research topics in CBR, we can list: design of a similarity measure, organization of the memory for fast retrieval, determination of the indices that will guide the retrieval, adaptation techniques, and case base maintenance. Another topic is how the application domain constrains CBR, and directs specialized research developments. A domain of application of choice for CBR has since the beginning been biomedicine.

3. CBR in biomedicine CBR in biomedicine has been a very fruitful area of research. We count today more than 250 papers published in specialized CBR conferences and workshops, AI journals, books, but also medical informatics and bioinformatics conferences and workshops. We also note a regular increase in the number of papers published in CBR in biomedicine. These different publication venues have spread the community, and slowed its strengthening and integration processes. Efforts have been required to build a community of CBR in biomedicine cooperating on defining and advancing the state-of-the-art. A notable example has been CBR-MED Web site, created by Berger. Bichindaritz has recently revived this into an on-line CBR-BIOMED Web site and

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research group. Different specialized events have been organized in the past, such as Macura’s tutorial on CBR at the 1995 American Medical Informatics Association (AMIA) conference and in 1997 Macura edited a special issue of the Artificial Intelligence in Medicine Journal on CBR. More recently, Bichindaritz and Marling organized two workshops on CBR in the Health Sciences, at the 5th International Conference on Case-Based Reasoning (ICCBR-03), and the 7th European Conference on Case-Based Reasoning (ECCBR-04). These workshops had most of the contemporary medical researchers in CBR represented. Bichindaritz and Marling are organizing a third workshop on CBR in the Health Sciences at the 6th International Conference on Case-Based Reasoning (ICCBR-05). Several reviews on CBR in medicine have been published. We can list Schmidt et al. [4] and Nilsson and Sollenborn [5]. We summarize here the state-ofthe-art of CBR in the health sciences. Early CBR systems in biomedicine have been Kolodner and Kolodner [6], Bareiss and Porter [7], Koton [8], and Turner [9]. They focused on diagnosis and were not yet systems developed in clinical settings. Since then, CBR has been applied to a variety of tasks, among which we can cite diagnosis (and more generally classification tasks), treatment planning (and similar tasks such as assessment tests planning), image analysis, long-term follow-up, quality control, tutoring, and research assistance (in conjunction with data mining). The main pioneering systems of CBR in the health sciences, with their application domain and type of task, are, ranked by date:  SHRINK, psychiatry, diagnosis (1987) [6];  PROTOS, audiology disorders, diagnosis (1987) [7];  CASEY, heart failure, diagnosis (1988) [8];  MEDIC, dyspnoea, diagnosis (1988) [9];  ALEXIA, hypertension, assessment tests planning (1992) [10];  ICONS, intensive care, antibiotics therapy (1993) [11];  BOLERO, pneumonia, diagnosis (1993) [12];  FLORENCE, health care planning (1993) [13];  MNAOMIA, psychiatry, diagnosis, treatment planning, clinical research assistance (1994) [3];  ROENTGEN, oncology, radiation therapy (1994) [14];  MACRAD, image analysis (1994) [15];  IMAGECREEK, image analysis (1996) [16];  CADI, tutoring for medical students (1996) [17];  SCINA, coronary heart disease, diagnosis (1997) [18];  CARE-PARTNER, stem cell transplantation, diagnosis, treatment planning (1998) [19];

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 CAMP, daily menu planning (1999) [20];  AUGUSTE, Alzheimer’s disease, diagnosis, treatment planning (2001) [21];  T-IDDM, diabetes, treatment planning (2001) [22]. CBR has found in biomedicine one if its most successful application areas, but also one of its most complex ones. The main reason for these achievements and interest from the biomedical community is that case-based reasoning capitalizes on the reuse of existing cases, or experiences. These abound in biology and medicine, since they belong to the family of descriptive experimental sciences, where knowledge stems from the study of natural phenomena, patient problem situations, or other living beings and their set of problems. In particular, the important variability in the natural and life sciences plays an active role in fostering the development of case-based approaches in these sciences where complete, causal models fully explaining occurring phenomena are not available. One consequence of this fact is that biomedicine is a domain where expertise beyond the novice level comes from learning by solving real and/or practice cases, which is precisely what case-based reasoning is accomplishing. Prototypical models are often more adapted to the description of biomedical knowledge [3] than other types of models, which also argues in favor of case-based reasoning. Among the complexities of biomedicine, we can list the high-dimensionality of cases, as is noted in particular in bioinformatics [23], but also in longterm follow-up [3,19]. Other factors are the cooccurrence of several diseases, not clearly bounded diagnostic categories, the need to mine for features that can be abstracted from time series, sensor signals, or other continuous input data, and the use of data-mining techniques in addition to casebased reasoning. This special issue showcases some of these complex and hard to model application domains in biology and medicine; it is striking that these systems have managed to successfully adhere to the peculiarities of their application domain each in their very unique way.

4. Selected papers presentation The first paper, entitled ‘‘Case-Based Reasoning in the Health Sciences: What’s Next?’’, by Isabelle Bichindaritz and Cindy Marling sketches a review of current research in case-based reasoning in the health sciences, describes current trends and issues, and projects future directions for work in this field.

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It summarizes the research themes examined by the contributing authors of the first two workshops on Case-Based Reasoning in the Health Sciences (ICCBR-03 and ECCBR-04). It highlights such trends as CBR in bioinformatics, support to the elderly and people with disabilities, formalization of CBR in biomedicine, and feature and case mining. A notable trend is that CBR systems are being better designed to account for the complexity of biomedicine, to integrate into clinical settings, and to communicate and interact with diverse systems and methods. It also provides an interesting introduction to the other papers, which were selected from the submissions to the 2nd Workshop on Case-based Reasoning in the Health Sciences at ECCBR-04. The second paper, entitled ‘‘Case-Based Object Recognition for Airborne Fungi Recognition’’, by Petra Perner, Silke Ja ¨nichen, and Horst Perner, presents an application of CBR to computer vision. It explains in a particularly clear manner the advantages of resorting to a case-based approach for detecting biomedical objects such as airborne fungi because of their important variation in appearance. The paper details further the complex similarity measure designed to solve this type of task and the problems found developing this CBR system. In the paper entitled ‘‘Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system’’, Markus Nilsson, Peter Funk, Erik M. G. Olsson, Bo von Sche ´ele, Ning Xiong, and Mikael Sollenborn present a casebased decision-support system for diagnosing stress related disorders. This system deals with signal measurements such as breathing and heart rate expressed as physiological time series. The main components of the system are a signal-classifier and a pattern identifier. HR3Modul, the signal-classifier, uses a feature mining technique called wavelet extraction to learn features from the continuous signals. Being a case-based reasoning system, HR3Modul classifies the signals based on retrieving similar patterns to determine whether a patient may be suffering from a stress related disorder as well as the nature of the disorder. The last paper, entitled ‘‘Me ´moire: A Framework for Semantic Interoperability of Case-based Reasoning Systems in Biology and Medicine’’, by Isabelle Bichindaritz, proposes the Me ´moire framework for sharing and distributing case bases and case-based reasoning systems in biology and medicine. The paper first summarizes the semantic Web approach and its development in the biomedical domain. It

Guest Editorial

then analyzes a series of case-based reasoning systems developed in biomedicine, and shows the important role played by ontologies and the use of semantics in these systems. From this experience, it proposes a framework, called Me ´moire, based on the OWL representation language, and a set of generic tools to build semantics infused casebased reasoning systems in biology and medicine that capitalize on the experience gained in this area of research. As such, this system implements the ‘‘reuse’’ philosophy of CBR a step further.

5. Conclusion The special issue illustrates some of the main issues and challenges identified in the ‘‘CaseBased Reasoning in the Health Sciences: What’s Next?’’ paper. Papers address particularly well how to scale up the development of case-based reasoning systems in biology and medicine, by using a specialized formalization framework that capitalizes on biomedical case bases and ontologies, by mining for features and cases in either digital images or sensor signals, by designing more complex similarity measures than in the past, and by taking advantage of the complementarity between case-based reasoning and knowledgebased approaches.

Acknowledgements Cynthia Marling, University of Ohio, USA, co-organized the 1st and 2nd workshop on case-based reasoning in the health sciences at ICCBR-03 and ECCBR-04. The author would like to thank her and the program committee of the 2nd workshop on case-based reasoning in the health sciences for reviewing — and some of them writing — the papers assembled here. The special issue could not have happened without them. Their alphabetical list is: Agnar Aamodt, Norwegian University of Science and Technology, Norway. Riccardo Bellazzi, University of Pavia, Italy. Susan Craw, The Robert Gordon University, Scotland, UK. Peter Funk, Ma ¨lardalen University, Sweden. Daniel Hennessy, University of Pittsburgh, USA. Marie-Christine Jaulent, Faculte ´ de Me ´decine Broussais-Hotel-Dieu, France. Stefania Montani, University of Piemonte Orientale, Italy. Petra Perner, Institute of Computer Vision and Applied Computer Sciences, Germany. Brigitte Se ´roussi, Service d’Informatique Medicale, France. Rainer Schmidt, Institut fur Medizinische Informatik und Biometrie, Germany. Derek Sleeman, University of Aberdeen, Scotland, UK.

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Isabelle Bichindaritz* Computing & Software Systems, University of Washington, 1900 Commerce Street, Tacoma, WA 98402, USA *Tel.: +1 253 692 4605; fax: +1 253 692 5862 E-mail address: [email protected]