Olga Vorobieva

Case-Based Reasoning Investigation of Therapy Inefficacy

In this paper, we present ISOR, a Case-Based Reasoning system for long-term therapy support in the endocrine domain and in psychiatry. ISOR performs typical therapeutic tasks, such as computing initial therapies, initial dose recommendations, and dose updates. Apart from these tasks ISOR deals especially with situations where therapies become ineffective. Causes for inefficacy have to be found and better therapy recommendations should be computed. In addition to the typical Case-Based Reasoning knowledge, namely former already solved cases, ISOR uses further knowledge forms, especially medical histories of query patients themselves and prototypical cases (prototypes). Furthermore, the knowledge base consists of therapies, conflicts, instructions etc. So, retrieval does not only provide former similar cases but different forms and steps of retrieval are performed, while adaptation occurs as an interactive dialog with the user. Since therapy inefficacy can be caused by various circumstances, we propose searching for former similar cases to get ideas about probable reasons that subsequently should be carefully investigated. We show that ISOR is able to successfully support such investigations.

Case-based reasoning investigation of therapy inefficacy

ISOR is a case-based reasoning system for long-term therapy support in the endocrine domain and in psychiatry. ISOR performs typical therapeutic tasks, such as computing initial therapies, initial dose recommendations and dose updates. ISOR deals especially with situations where therapies become ineffective. Causes for inefficacy have to be found and better therapy recommendations should be computed. In addition to former already solved cases, ISOR uses further knowledge forms, especially medical histories of query patients themselves and prototypes. Furthermore, the knowledge base consists of therapies, conflicts, instructions, etc. So, different forms and steps of retrieval are performed, while adaptation occurs as an interactive dialog with the user.

Adaptation and medical case-based reasoning focusing on endocrine therapy support

So far, Case-Based Reasoning has not become as successful in medicine as in some other application domains. One, probably the main reason is the adaptation problem. In Case-Based Reasoning the adaptation task still is domain dependent und usually requires specific adaptation rules. Furthermore, in medicine adaptation is often more difficult than in other domains, because usually more and complex features have to be considered. We have developed some programs for endocrine therapy support, especially for hypothyroidism. In this paper, we do not present them in detail, but focus on adaptation. We do not only summarise experiences with adaptation in medicine, but we want to elaborate typical medical adaptation problems and hope to indicate possibilities how to solve them.

Adaptation Problems in Therapeutic Case-Based Reasoning Systems

Case-based Reasoning has become a successful technique in many application domains. Unfortunately, so far it is not so successful in medicine. One, probably the main reason is that on the one side in Case-based Reasoning the adaptation problem can not be solved in a domain independent way. On the other side in medicine the adaptation is often more difficult than in other domains, because more and complex features have to be considered. In this paper, we want to indicate possibilities how to solve adaption problems in medical Case-based Reasoning systems.

Case-Based Reasoning to explain medical model exceptions

In medicine many exceptions occur. In medical practise and in knowledge-based systems too, it is necessary to consider them and to deal with them appropriately. In medical studies and in research exceptions shall be explained. We present a system that helps to explain cases that do not fit into a theoretical hypothesis. Our starting points are situations where neither a well-developed theory nor reliable knowledge nor a proper case base is available. So, instead of reliable theoretical knowledge and intelligent experience, we have just some theoretical hypothesis and a set of measurements. In this paper, we propose to combine CBR with a statistical model. We use CBR to explain those cases that do not fit the model. The case base has to be set up incrementally, it contains the exceptional cases, and their explanations are the solutions, which can be used to help to explain further exceptional cases.

Prototypes for Medical Case-Based Applications

Already in the early stages of Case-Based Reasoning prototypes were considered as an interesting technique to structure the case base and to fill the knowledge gap between single cases and general knowledge. Unfortunately, later on prototypes never became a hot topic within the CBR community. However, for medical applications they have been used rather regularly, because they correspond to the reasoning of doctors in a natural way. In this paper, we illustrate the role of prototypes by application programs, which cover all typical medical tasks: diagnosis, therapy, and course analysis.

ISOR-2: a case-based reasoning system to explain exceptional dialysis patients

In medicine many exceptions occur. In medical practice and in knowledge-based systems too, it is necessary to consider them and to deal with them appropriately. In medical studies and in research, exceptions shall be explained. We present a system that helps to explain cases that do not fit into a theoretical hypothesis. Our starting points are situations where neither a well-developed theory nor reliable knowledge nor a priori a proper case base is available. So, instead of reliable theoretical knowledge and intelligent experience, we have just some theoretical hypothesis and a set of measurements. In this paper, we propose to combine CBR with a statistical model. We use CBR to explain those cases that do not fit the model. The case base has to be set up incrementally, it contains the exceptional cases, and their explanations are the solutions, which can be used to help to explain further exceptional cases.

Combining Statistics and Case-Based Reasoning for Medical Research

In medicine many exceptions occur. In medical practice and in knowledge-based systems too, it is necessary to consider them and to deal with them appropriately. In medical studies and in research, exceptions should be explained.We present a system, called ISOR, that helps to explain cases that do not fit to a theoretical hypothesis. Starting points are situations where neither a well-developed theory nor reliable knowledge nor, at the beginning, a case base is available. So, instead of theoretical knowledge and intelligent experience, just some theoretical hypothesis and a set of measurements are given. In this chapter, we focus on the application of the ISOR system to the hypothesis that a specific exercise program improves the physical condition of dialysis patients. Additionally, for this application a method to restore missing data is presented.

ISOR: an expert-system for investigations of therapy inefficacy

ISOR is a Case-Based system for long-term therapy support in the endocrine domain and in psychiatry. It performs typical therapeutic tasks, namely computing initial therapies, initial dose recommendations, and dose updates. Furthermore, ISOR deals especially with situations where therapies become ineffective. Causes for inefficacy have to be found and better therapy recommendations should be computed. In addition to the typical Case-Based Reasoning knowledge, namely former already solved cases, ISOR uses further knowledge forms, especially medical histories of query patients themselves, prototypical cases (prototypes) and therapies, conflicts, instructions etc. So, different forms and steps of retrieval are performed, while adaptation occurs as an interactive dialog with the user. Since therapy inefficacy can be caused by various circumstances, ISOR searches for former similar cases to get ideas about probable reasons that subsequently should be carefully investigated.

Explaining Medical Model Exceptions

In this chapter, a system named ISOR is presented, that supports research doctors to investigate and to explain cases that do not fit a theoretical hypothesis. The system is designed for situations where neither a well-developed theory nor reliable knowledge nor, at the beginning, a case base is available. Instead of theoretical knowledge and intelligent experience, just a theoretical hypothesis and a set of measurements are given. ISOR is a Case-Based Reasoning system. That means, when attempting to find an explanation for an exceptional case, solutions of already explained similar exceptional cases are considered. However, ISOR uses further knowledge sources, especially a dialog where the user (a research doctor) can make suggestions for an explanation. ISOR is domain independent and can be applied to various research problems. However, in this chapter, it is focused on the hypothesis that a specific exercise program improves the physical condition of dialysis patients. Since many data are missing for this research problem, a method to impute missing data was developed and is also presented here. This method combines general domain independent techniques with domain knowledge provided by a medical expert. For the latter technique Case-based Reasoning is applied again.