A multi-modal reasoning methodology for managing IDDM patients

Using Case-Based Reasoning in a Learning System: A Prototype of a Pedagogical Nurse Tool for Evidence-Based Diabetic Foot Ulcer Care

BACKGROUND

Currently, evidence-based learning systems to increase knowledge and evidence level of wound care are unavailable to wound care nurses in Denmark, which means that they need to learn about diabetic foot ulcers from experience and peer-to-peer training, or by asking experienced colleagues. Interactive evidence-based learning systems built on case-based reasoning (CBR) have the potential to increase wound care nurses' diabetic foot ulcer knowledge and evidence levels.

METHOD

A prototype of a CBR-interactive, evidence-based algorithm-operated learning system calculates a dissimilarity score (DS) that gives a quantitative measure of similarity between a new case and cases stored in a case base in relation to six variables: necrosis, wound size, granulation, fibrin, dry skin, and age. Based on the DS, cases are selected by matching the six variables with the best predictive power and by weighing the impact of each variable according to its contribution to the prediction. The cases are ranked, and the six cases with the lowest DS are visualized in the system.

RESULTS

Conventional education, that is, evidence-based learning material such as books and lectures, may be less motivating and pedagogical than peer-to-peer training, which is, however, often less evidence-based. The CBR interactive learning systems presented in this study may bridge the two approaches. Showing wound care nurses how individual variables affect outcomes may help them achieve greater insights into pathophysiological processes.

CONCLUSION

A prototype of a CBR-interactive, evidence-based learning system that is centered on diabetic foot ulcers and related treatments bridges the gap between traditional evidence-based learning and more motivating and interactive learning approaches.

Towards Dynamic Uncertain Causality Graphs for the Intelligent Diagnosis and Treatment of Hepatitis B

Hepatitis B is a widespread epidemic in the world, but so far no single drug has been shown to kill or eliminate the Hepatitis B virus and heal people with chronic Hepatitis B virus infection. Based on comprehensive investigations to relevant characteristics of Hepatitis B, a diagnostic modelling and reasoning methodology using Dynamic Uncertain Causality Graph is proposed. The symptoms, physical signs, examinations results, medical histories, etiology, pathogenesis and other factors were included in the diagnosis model. In order to reduce the difficulty of building the model, a modular modeling scheme is proposed, which provides multi-perspectives and arbitrary granularity for the expression of disease causality. The chain reasoning algorithm and weighted logic operation mechanism are introduced to ensure the correctness and effectiveness of diagnostic reasoning under incomplete and uncertain information. In addition, the causal view of the potential interactions between diseases and symptoms visually shows the reasoning process in a graphical way. In the relevant model, the model of the diagnostic process and the model of the therapeutic process are symmetrical. The results show that, even with incomplete observations, the proposed methodology achieves encouraging diagnostic accuracy and effectiveness, providing a promising assistance tool for physicians in the diagnosis of Hepatitis B.

Cost-sensitive case-based reasoning using a genetic algorithm: application to medical diagnosis

OBJECTIVE

The paper studies the new learning technique called cost-sensitive case-based reasoning (CSCBR) incorporating unequal misclassification cost into CBR model. Conventional CBR is now considered as a suitable technique for diagnosis, prognosis and prescription in medicine. However it lacks the ability to reflect asymmetric misclassification and often assumes that the cost of a positive diagnosis (an illness) as a negative one (no illness) is the same with that of the opposite situation. Thus, the objective of this research is to overcome the limitation of conventional CBR and encourage applying CBR to many real world medical cases associated with costs of asymmetric misclassification errors.

METHODS

The main idea involves adjusting the optimal cut-off classification point for classifying the absence or presence of diseases and the cut-off distance point for selecting optimal neighbors within search spaces based on similarity distribution. These steps are dynamically adapted to new target cases using a genetic algorithm. We apply this proposed method to five real medical datasets and compare the results with two other cost-sensitive learning methods-C5.0 and CART.

RESULTS

Our finding shows that the total misclassification cost of CSCBR is lower than other cost-sensitive methods in many cases. Even though the genetic algorithm has limitations in terms of unstable results and over-fitting training data, CSCBR results with GA are better overall than those of other methods. Also the paired t-test results indicate that the total misclassification cost of CSCBR is significantly less than C5.0 and CART for several datasets.

CONCLUSION

We have proposed a new CBR method called cost-sensitive case-based reasoning (CSCBR) that can incorporate unequal misclassification costs into CBR and optimize the number of neighbors dynamically using a genetic algorithm. It is meaningful not only for introducing the concept of cost-sensitive learning to CBR, but also for encouraging the use of CBR in the medical area. The result shows that the total misclassification costs of CSCBR do not increase in arithmetic progression as the cost of false absence increases arithmetically, thus it is cost-sensitive. We also show that total misclassification costs of CSCBR are the lowest among all methods in four datasets out of five and the result is statistically significant in many cases. The limitation of our proposed CSCBR is confined to classify binary cases for minimizing misclassification cost because our proposed CSCBR is originally designed to classify binary case. Our future work extends this method for multi-classification which can classify more than two groups.

A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning

Since clinical management of patients and clinical research are essentially time-oriented endeavours, reasoning about time has become a hot topic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with case-based reasoning. It is useful for application domains where neither well-known standards, nor known periodicity, nor a complete domain theory exist. We have used our method in two prognostic applications. The first one deals with prognosis of the kidney function for intensive care patients. The idea is to elicit impairments on time, especially to warn against threatening kidney failures. Our second application deals with a completely different domain, namely geographical medicine. Its intention is to compute early warnings against approaching infectious diseases, which are characterised by irregular cyclic occurrences. So far, we have applied our program on influenza and bronchitis. In this paper, we focus on influenza forecast and show first experimental results.

Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients

We present a multi-modal reasoning (MMR) methodology that integrates case-based reasoning (CBR), rule-based reasoning (RBR) and model-based reasoning (MBR), meant to provide physicians with a reliable decision support tool in the context of type 1 diabetes mellitus management. In particular, we have implemented a decision support system that is able to jointly exploit a probabilistic model of the glucose-insulin system at the steady state, a RBR system for suggestion generation and a CBR system for patient's profiling. The integration of the CBR, RBR and MBR paradigms allows for an optimized exploitation of all the available information, and for the definition of a therapy properly tailored to the patient's needs, overcoming the single approaches limitations. The system has been tested both on simulated and on real patients' data.

Supporting decisions in medical applications: the knowledge management perspective

In the medical domain, different knowledge types are typically available. Operative knowledge, collected during every day practice, and reporting expert's skills, is stored in the hospital information system (HIS). On the other hand, well-assessed, formalised medical knowledge is reported in textbooks and clinical guidelines. We claim that all this heterogeneous information should be secured and distributed, and made available to physicians in the right form, at the right time, in order to support decision making: in our view, therefore, a decision support system cannot be conceived as an independent tool, able to substitute the human expert on demand, but should be integrated with the knowledge management (KM) task. From the methodological viewpoint, case based reasoning (CBR) has proved to be a very well suited reasoning paradigm for managing knowledge of the operative type. On the other hand, rule based reasoning (RBR) is historically one of the most successful approaches to deal with formalised knowledge. To take advantage of all the available knowledge types, we propose a multi modal reasoning (MMR) methodology, that integrates CBR and RBR, for supporting context detection, information retrieval and decision support. Our methodology has been successfully tested on an application in the field of diabetic patients management.

A telemedicine support for diabetes management: the T-IDDM project

In the context of the EU funded Telematic Management of Insulin-Dependent Diabetes Mellitus (T-IDDM) project, we have designed, developed and evaluated a telemedicine system for insulin dependent diabetic patients management. The system relies on the integration of two modules, a Patient Unit (PU) and a Medical Unit (MU), able to communicate over the Internet and the Public Switched Telephone Network. Using the PU, patients are allowed to automatically download their monitoring data from the blood glucose monitoring device, and to send them to the hospital data-base; moreover, they are supported in their every day self monitoring activity. The MU provides physicians with a set of tools for data visualization, data analysis and decision support, and allows them to send messages and/or therapeutic advice to the patients. The T-IDDM service has been evaluated through the application of a formal methodology, and has been used by European patients and physicians for about 18 months. The results obtained during the project demonstration, even if obtained on a pilot study of 12 subjects, show the feasibility of the T-IDDM telemedicine service, and seem to substantiate the hypothesis that the use of the system could present an advantage in the management of insulin dependent diabetic patients, by improving communications and, potentially, clinical outcomes.