Stefania Montani

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.

Web-based telemedicine systems for home-care: technical issues and experiences

The use of the Web for telemedicine applications seems nowadays a compulsory solution: the Web has become a standardized infrastructure for giving access to sophisticated telemedicine applications from virtually any machine and operating system. Such standardized communication platform guarantees accessibility and usability advantages to both customers and providers (patients and physicians). However, there are several issues that should be discussed in depth, with particular reference to all the applications related to the provision of care at distance, nowadays called telecare applications. In telecare applications the role of the patient becomes central, since he/she is actively involved in the process of managing care and treatments, and since he/she (or his/her families) is responsible for collecting some measurements and related information. In this paper we will discuss the general architectural and technical issues related to the development of Web-based systems for telecare applications, relying on the experience we gained within the telecare project T-IDDM (Telematic Management of Insulin Dependent Diabetes Mellitus), devoted to assist the management and home-monitoring of Type 1 Diabetes Mellitus patients.

RADYBAN : A tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks

In this paper, we present Radyban (Reliability Analysis with DYnamic BAyesian Networks), a software tool which allows to analyze a dynamic fault tree relying on its conversion into a dynamic Bayesian network. The tool implements a modular algorithm for automatically translating a dynamic fault tree into the corresponding dynamic Bayesian network and exploits classical algorithms for the inference on dynamic Bayesian networks, in order to compute reliability measures. After having described the basic features of the tool, we show how it operates on a real world example and we compare the unreliability results it generates with those returned by other methodologies, in order to verify the correctness and the consistency of the results obtained.

Exploring new roles for case-based reasoning in heterogeneous AI systems for medical decision support

AbstractBackground 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.

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 experts 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.

Meta-Analysis of the Effect of the Use of Computer-Based Systems on the Metabolic Control of Patients with Diabetes Mellitus

The purpose of this study was to evaluate, through a meta-analysis study, whether the use of computer-based systems reported in the literature improves the metabolic control of diabetic patients. On the retrieved papers, a set of meta-analysis studies were performed: first the difference of HbA1c between cases and controls at follow-up was evaluated (sign test); then the difference between cases and controls in the total variation of HbA1c from the beginning to the end of the trial was considered (method of effect sizes). The latter methodology was reapplied also on three more homogeneous article subgroups. The sign test was performed on 16 papers: in two of them, the HbA1c level was higher in the intervention group than in the control group at follow-up: it is unlikely that this is a random occurrence (p < 0.01). The method of effect sizes was first applied to 13 papers, as in the others some needed data were missing: the results obtained showed a statistically significant amelioration of metabolic control in the intervention group in comparison to the control group (p < 0.01). A progressive reinforcement of this outcome was obtained on the trial subgroups. Our study supports the hypothesis that the use of computer-based systems can be an effective means of improving metabolic control. The differential benefit obtained in the amelioration of HbA1c does not justify, by itself, the applicability of such systems into clinical practice; additional investigations should be carried out to evaluate the enhancement of other clinical and organizational indicators.

Supporting reliability engineers in exploiting the power of Dynamic Bayesian Networks

In this paper, we present an approach to reliability modeling and analysis based on the automatic conversion of a particular reliability engineering model, the Dynamic Fault Tree (DFT), into Dynamic Bayesian Networks (DBN). The approach is implemented in a software tool called RADYBAN (Reliability Analysis with DYnamic BAyesian Networks). The aim is to provide a familiar interface to reliability engineers, by allowing them to model the system to be analyzed with a standard formalism; however, a modular algorithm is implemented to automatically compile a DFT into the corresponding DBN. In fact, when the computation of specific reliability measures is requested, classical algorithms for the inference on Dynamic Bayesian Networks are exploited, in order to compute the requested parameters. This is performed in a totally transparent way to the user, who could in principle be completely unaware of the underlying Bayesian Network. The use of DBNs allows the user to be able to compute measures that are not directly computable from DFTs, but that are naturally obtainable from DBN inference. Moreover, the modeling capabilities of a DBN, allow us to extend the basic DFT formalism, by introducing probabilistic dependencies among system components, as well as the definition of specific repair policies that can be taken into account during the reliability analysis phase. We finally show how the approach operates on some specific examples, by describing the advantages of having available a full inference engine based on DBNs for the requested analysis tasks.

Adopting model checking techniques for clinical guidelines verification

OBJECTIVES Clinical guidelines (GLs) are assuming a major role in the medical area, in order to grant the quality of the medical assistance and to optimize medical treatments within healthcare organizations. The verification of properties of the GL (e.g., the verification of GL correctness with respect to several criteria) is a demanding task, which may be enhanced through the adoption of advanced Artificial Intelligence techniques. In this paper, we propose a general and flexible approach to address such a task. METHODS AND MATERIALS Our approach to GL verification is based on the integration of a computerized GL management system with a model-checker. We propose a general methodology, and we instantiate it by loosely coupling GLARE, our system for acquiring, representing and executing GLs, with the model-checker SPIN. RESULTS We have carried out an in-depth analysis of the types of properties that can be effectively verified using our approach, and we have completed an overview of the usefulness of the verification task at the different stages of the GL life-cycle. In particular, experimentation on a GL for ischemic stroke has shown that the automatic verification of properties in the model checking approach is able to discover inconsistencies in the GL that cannot be detected in advance by hand. CONCLUSION Our approach thus represents a further step in the direction of general and flexible automated GL verification, which also meets usability requirements.

Protocol-based reasoning in diabetic patient management

We propose a system for teleconsultation in Insulin Dependent Diabetes Mellitus (IDDM) management, accessible through the use of the net. The system is able to collect monitoring data, to analyze them through a set of tools, and to suggest a therapy adjustment in order to tackle the identified metabolic problems and to fit the patients needs. The therapy revision has been implemented through the Episodic Skeletal Planning Methodi, it generates an advice and employs it to modify the current therapeutic protocol, presenting to the physician a set of feasible solutions, among which she can choose the new one.

Retrieval in a Prototype-Based Case Library: A Case Study in Diabetes Therapy Revision

Case retrieval is a complex and important stage of the overall CBR process, involving situation assessment and a flexible combination of case memory search and matching. The goal of the paper is to discuss a retrieval approach in a case library organized through classes of prototypes. Situation assessment is realized with a Bayesian classification step, aimed at defining a uniform framework for feature evaluation and at restricting search only to relevant parts of the case library. Classical K-NN methods or pruning based technique like Pivoting-Based Retrieval can then be applied to retrieve and match cases, by considering intra-class or inter-class retrieval. The proposed method has been evaluated within a decision support system for the therapy revision of patients affected by Diabetes Mellitus.