UTOPIA: a consultation system for visit-by-visit diabetes management

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.

Renal telehealthcare system based on a patient physiological image: a novel hybrid approach in telemedicine

This paper presents a novel renal telemedicine system, Virtual Center for Renal Support (VCRS), focused on the end-stage renal disease (ESRD) population. The VCRS design modifies the telemedicine paradigm, currently centered on communication technologies and monitoring devices, by emphasizing the way that biosignals are used to extract on-line knowledge to be used by physicians to solve current needs of this population. We begin with an ESRD review, from which a summary of major limitations of current renal health assistance programs is obtained. This is used to form the basis for the VCRS. This work is focused on a theoretical description of the technological architecture of VCRS, followed by a simulation experiment showing some preliminary results from a prototype of a patient physiologic image (PPI) computer component, the major knowledge creator of VCRS. Preliminary results show that PPI technology provides the ability to supervise internal variables representing the patient's dynamic behavior. The demonstrated relation between adequate control of extracellular volume and blood pressure suggests that VCRS is able to generate hypovolemia warnings before their occurrence during a hemodyalysis session delivered remotely. However, PPI is not restricted to kinetic models, which were initially chosen because of their successful results in the provision of dialysis. Preliminary results suggest the ability of this telemedicine system to enhance remote patient supervision and care.

Virtual center for renal support: technological approach to patient physiological image

The patient physiological image (PPI) is a novel concept which manages the knowledge of the virtual center for renal support (VCRS), currently being developed by the Biomedical Engineering Group of the University of Seville. PPI is a virtual "replica" of the patient, built by means of a mathematical model, which represents several physiological subsystems of a renal patient. From a technical point of view, PPI is a component-oriented software module based on cutting-edge modeling and simulation technology. This paper provides a methodological and technological approach to the PPI. Computational architecture of PPI-based VCRS is also described. This is a multi-tier and multi-protocol system. Data are managed by several ORDBMS instances. Communications design is based on the virtual private network (VPN) concept. Renal patients have a minimum reliable access to the VCRS through a public switch telephone network--X.25 gateway. Design complies with the universal access requirement, allowing an efficient and inexpensive connection even in rural environments and reducing computational requirements in the patient's remote access unit. VCRS provides support for renal patients' healthcare, increasing the quality and quantity of monitored biomedical signals, predicting events as hypotension or low dialysis dose, assisting further to avoid them by an online therapy modification and easing diagnostic tasks. An online therapy adjustment experiment simulation is presented. Finally, the presented system serves as a computational aid for research in renal physiology. This is achieved by an open and reusable modeling and simulation architecture which allows the interaction among models and data from different scales and computer platforms, and a faster transference of investigation models toward clinical applications.

Cased-Based Reasoning for medical knowledge-based systems

In this paper we present the results of the MIE/GMDS-2000 Workshop 'Case-Based Reasoning for Medical Knowledge-based Systems'. While in many domains Cased-Based Reasoning (CBR) has become a successful technique for knowledge-based systems, in the medical field attempts to apply the complete CBR cycle are rather exceptional. Some systems have recently been developed, which on the one hand use only parts of the CBR method, mainly the retrieval, and on the other hand enrich the method by a generalisation step to fill the knowledge gap between the specificity of single cases and general rules. And some systems rely on integrating CBR and other problem solving methodologies. In this paper we discuss the appropriateness of CBR for medical knowledge-based systems, point out problems, limitations and possible ways to cope with them.

Intelligent analysis of clinical time series: an application in the diabetes mellitus domain

This paper describes the application of a method for the intelligent analysis of clinical time series in the diabetes mellitus domain. Such a method is based on temporal abstractions and relies on the following steps: (i) 'pre-processing' of raw data through the application of suitable filtering techniques: (ii) 'extraction' from the pre-processed data of a set of abstract episodes (temporal abstractions); and (iii) 'post-processing' of temporal abstractions; the post-processing phase results in a new set of features that embeds high level information on the patient dynamics. The derived features set is used to obtain new knowledge through the application of machine learning algorithms. The paper describes in detail the application of this methodology and presents some results obtained on simulated data and on a data-set of four diabetic patients monitored for > 1 year.

Clinical decision support, systems methodology, and telemedicine: their role in the management of chronic disease

In this paper, the design and evaluation of decision support systems, including those incorporating a telematic component, are considered. It is argued that effective design and evaluation are dependent upon the adoption of appropriate methodology set firmly within a systemic framework. Systems modeling is proposed as an approach to system design, with evaluation adopting an approach incorporating evaluability analysis and formative and summative evaluation, including the use of stakeholder matrix analysis. The relevance of such systemic methodology is demonstrated in the context of diabetes and end-stage renal disease as examples of the generic clinical problem of the management of chronic disease.

Compartmental models for glycaemic prediction and decision-support in clinical diabetes care: promise and reality

This paper reviews and critically appraises the application of compartmental models for generating glycaemic predictions and offering clinical decision support in diabetes care. Comparisons are made with alternative algorithmic-based approaches. Unresolved issues raised for model-based techniques include the relative lack of input data necessary for generating reasonable blood glucose predictions, and the high level of uncertainty associated with such predictions which limits their use as guides for therapeutic insulin-dosage adjustments. It is concluded that compartmental model-based approaches, while not offering much benefit for clinical/therapeutic application, will have a role to play as research tools and for educational use. By contrast it is proposed that algorithmic-based approaches, especially in conjunction with telemedicine and Internet applications, are likely to see growing use for day-to-day therapeutic decision support. Randomised controlled clinical trials however will be required, together with other evaluation efforts, before algorithmic-based approaches-like any other clinical technique-can be widely adopted into routine medical practice.

A distributed system for diabetic patient management

This paper describes a telemedicine system for diabetic patients management, presenting its architecture, the technical solutions adopted and the methodologies on which it is based. The system, designed to provide decision support in a distributed environment, is composed of two modules, a Patient Unit and a Medical Unit, connected by telecommunication services. We outline how the two modules can interact to perform an effective monitoring and a cooperative control of glucose metabolism. In particular, we detail the data analysis tasks performed by the two units and how the results are exploited to assist patients and physicians in revising and adjusting the therapeutic protocol. We will finally describe the current prototypical implementation of the system that uses HTTP as the communication protocol and HTML pages as the graphical user interface.

Decision support systems in diabetes: a systems perspective

This paper examines, from a systems perspective, some of the major issues associated with the provision of computer-based decision support in the management of the diabetic patient. The importance of understanding the underlying dynamics is emphasised, as is the value of a systems approach to the specification, design and evaluation of decision support systems if they are to find clinical acceptance.