Towards case-based reasoning for diabetes management

Computational Intelligence, 2009

This paper presents a case-based decision support system prototype to assist patients with Type 1 diabetes on insulin pump therapy. These patients must vigilantly maintain blood glucose levels within prescribed target ranges to prevent serious disease complications, including blindness, neuropathy, and heart failure. Case-based reasoning (CBR) was selected for this domain because (a) existing guidelines for managing diabetes are general and must be tailored to individual patient needs; (b) physical and lifestyle factors combine to influence blood glucose levels; and (c) CBR has been successfully applied to the management of other long-term medical conditions. An institutional review board (IRB) approved preliminary clinical study, involving 20 patients, was conducted to assess the feasibility of providing case-based decision support for these patients. Fifty cases were compiled in a case library, situation assessment routines were encoded to detect common problems in blood glucose control, and retrieval metrics were developed to find the most relevant past cases for solving current problems. Preliminary results encourage continued research and work toward development of a practical tool for patients.

2016

This research focused on the use of case based reasoning (CBR) for treatment and management of diabetes. CBR is a field of artificial intelligence where one uses past cases as resolution for similar problems. The concept is based on dynamic memory theory where human beings solve problems by recalling encountered cases [1]. This research has applied CBR in the field of medicine for treatment and management of diabetes. Diabetes is a family of metabolic disease condition where the patient has elevated blood glucose. There is a rise on the prevalence of diabetes in Kenya with over 2 Million Kenyans suffering from the condition [2]. Damage to nerves, heart failure, kidney failure blindness and amputations are among the diabetes associated complications. Some of key challenges encountered during the management of diabetes include lack of insulin, high cost of drugs, an overworked workforce and low awareness among others. A formative questionnaire was conducted to find out the viability o...

Journal of Diabetes Science and Technology, 2008

Computer methods and …, 2000

In this paper we propose a case-based decision support tool, designed to help physicians in 1st type diabetes therapy revision through the intelligent retrieval of data related to past situations (or 'cases') similar to the current one. A case is defined as a set of variable values (or features) collected during a visit. We defined taxonomy of prototypical patients' conditions, or classes, to which each case should belong. For each input case, the system allows the physician to find similar past cases, both from the same patient and from different ones. We have implemented a two-steps procedure; (1) it finds the classes to which the input case could belong; (2) it lists the most similar cases from these classes, through a nearest neighbor technique, and provides some statistics useful for decision taking. The performance of the system has been tested on a data-base of 147 real cases, collected at the Policlinico S. Matteo Hospital of Pavia. The tool is fully integrated in the web-based architecture of the EU funded Telematic management of Insulin Dependent Diabetes Mellitus (T-IDDM) project.

Personalized medical care is an individualized approach to managing and treating diseases in the healthcare system. It follows the personalized medicine concept and has recently received much attention from the governing, scientific and healthcare diseases communities worldwide. Personalized medicine is speedily impacting how patients are managed and treated and also how healthcare delivery is channelling its resources to maximize patient benefits. The management of Diabetes Mellitus Type 2 consists of major lifestyle (dietary pattern), drug administration and physical exercise. The main objective of this study was to develop a Case-based reasoning system for personalized medical care for patients with Diabetes Mellitus Type 2. Design science within knowledge engineering method and data gathering tools such as semi-structured interviews and document analysis were employed to develop a prototype system. The domain experts were selected by using the purposive sampling technique. The k...

Lecture Notes in Computer Science, 2008

This paper presents a case-based approach to decision support for diabetes management in patients with Type 1 diabetes on insulin pump therapy. To avoid serious disease complications, including heart attack, blindness and stroke, these patients must continuously monitor their blood glucose levels and keep them as close to normal as possible. Achieving and maintaining good blood glucose control is a difficult task for these patients and their health care providers. A prototypical case-based decision support system was built to assist with this task. A clinical research study, involving 20 patients, yielded 50 cases of actual problems in blood glucose control, with their associated therapeutic adjustments and clinical outcomes, for the prototype's case base. The prototype operates by: (1) detecting problems in blood glucose control in large quantities of patient blood glucose and life event data; (2) finding similar past problems in the case base; and (3) offering the associated therapeutic adjustments stored in the case base to the physician as decision support. Results from structured evaluation sessions and a patient feedback survey encourage continued research and work towards a practical tool for diabetes management.

Procedia Computer Science, 2015

The field of reasoning methodologies is very important in the area of knowledge computing and engineering. Reasoning methodologies has been one of the standard and improving techniques with strong methods for health expert systems industry. Reasoning techniques has provided greatest support for predicting diagnosing and treatment of disease with correct case of results. Diabetes needs greatest support of reasoning techniques for diagnosis and treatment. This paper focus on the main technical characteristics of four reasoning methodologies which are commonly used in developing diabetic expert systems, namely; reasoning with production rules, fuzzy reasoning, case-based reasoning, and ontological-case based reasoning. In addition, this paper proposes the best reasoning technique for diabetic expert systems. The main result of this study covers a variety of four reasoning methodologies, which reveals that case based reasoning paradigm is the best reasoning technique methodology regarding to the issues of maintenance ,powerful and knowledge representations .

Artificial Intelligence …, 2003

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. #

2013

Managing diabetes using intelligent techniques is a recent priority for healthcare information systems and the medical domain. Diabetes is one of the most widespread diseases around the world including Australia. Numerous intelligent systems supporting diabetes management (DM) have been widely deployed, yet how to effectively develop a DM system integrating intelligent techniques remains a big issue. Case-based reasoning (CBR), as an intelligent technique, has been applied in various fields including customer services, medical diagnosis, and clinical treatment. This paper proposes a case-based lifecycle for DM consisting of case-based symptoms, case-based diagnosis, case-based prognosis, case-based treatment, and case-based care. The lifecycle is integrated with a web-based system in which CBR as an intelligent intermediary. The approach proposed in this research might facilitate research and development of diabetes management, healthcare information systems and intelligent systems.

2015

The 4 Diabetes Support System (4DSS) is a prototypical hy- brid case-based reasoning (CBR) system that aims to help patients with type 1 diabetes on insulin pump therapy achieve and maintain good blood glucose control. The CBR cycle revolves around treating blood glucose control problems by retrieving and reusing therapeutic adjust- ments that have been eectively used to treat similar problems in the past. Other articial intelligence (AI) approaches have been integrated primarily to aid in situation assessment: knowing when a patient has a blood glucose control problem and characterizing the type of problem that the patient has. Over the course of ten years, emphasis has shifted toward situation assessment and machine learning approaches for pre- dicting blood glucose levels, as that is the area of greatest patient need. The goal has been to make large volumes of raw insulin, blood glucose and life-event data actionable. During the past year, newly available t- ness bands have provi...