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Biomedical subjects

R Bellazzi

Publications and source records attributed to R Bellazzi.

At least 37 records · Page 2Linked to original sources
PubMed2000

Building telemedicine systems for supporting decisions in diabetes care: a report from a running experience.

This paper describes some issues that should be investigated to implement telemedicine systems designed for effectively supporting decisions in diabetic patients management, namely situation assessment, information sharing, and knowledge management. The solutions and experiences carried on in this field within a European Union (EU)-funded project, called T-IDDM (Telematic Management of Insulin Dependent Diabetes Mellitus), are reported.

Blood Glucose↗
PubMed2000

Bayesian analysis of blood glucose time series from diabetes home monitoring.

This paper describes the application of a novel Bayesian estimation technique to extract the structural components, i.e., trend and daily patterns, from blood glucose level time series coming from home monitoring of insulin dependent diabetes mellitus patients. The problem is formulated through a set of stochastic equations, and is solved in a Bayesian framework by using a Markov chain Monte Carlo technique. The potential of the method is illustrated by analyzing data coming from the home monitoring of a 14-year old male patient.

Adolescent↗
PubMed2000

Bayesian identification of a population compartmental model of C-peptide kinetics.

When models are used to measure or predict physiological variables and parameters in a given individual, the experiments needed are often complex and costly. A valuable solution for improving their cost effectiveness is represented by population models. A widely used population model in insulin secretion studies is the one proposed by Van Cauter et al. (Diabetes 41:368-377, 1992), which determines the parameters of the two compartment model of C-peptide kinetics in a given individual from the knowledge of his/her age, sex, body surface area, and health condition (i.e., normal, obese, diabetic). This population model was identified from the data of a large training set (more than 200 subjects) via a deterministic approach. This approach, while sound in terms of providing a point estimate of C-peptide kinetic parameters in a given individual, does not provide a measure of their precision. In this paper, by employing the same training set of Van Cauter et al., we show that the identification of the population model into a Bayesian framework (by using Markov chain Monte Carlo) allows, at the individual level, the estimation of point values of the C-peptide kinetic parameters together with their precision. A successful application of the methodology is illustrated in the estimation of C-peptide kinetic parameters of seven subjects (not belonging to the training set used for the identification of the population model) for which reference values were available thanks to an independent identification experiment.

Adult↗
PubMed2000

Exploiting multi-modal reasoning for knowledge management and decision support: an evaluation study.

We present the first evaluation results of a knowledge management and decision support system for Type I diabetes patients' care. Such system, meant to help physicians in therapy revision, relies on the integration of Rule Based Reasoning and Case Based Reasoning, and exploits both explicit and implicit knowledge. Reliability was positively judged by a group of expert diabetologists; an increase in its performances is foreseen as new knowledge will be acquired, through the system usage in clinical practice.

Artificial Intelligence↗
PubMed1999

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 patient's 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.

Adult↗
PubMed1999

Integrating case based and rule based reasoning in a decision support system: evaluation with simulated patients.

We present a Web-based knowledge management and decision support system for Type I Diabetes patients' care. The tool exploits the integration of two methodologies, Case Based Reasoning and Rule Based Reasoning, and supports physicians in the definition of therapeutic strategies. Such a work is being integrated in the EU funded T-IDDM project architecture. In this paper we report a first evaluation obtained on simulated patients.

Artificial Intelligence↗
PubMed1999

Supporting decisions in diabetic patients management through case-based retrieval.

In this paper we present a tool for the intelligent retrieval of past cases to support Insulin Dependent Diabetes Mellitus patients therapy revision. This tool was designed for assisting physicians during periodical monitoring visits. Each case is represented through i) a collection of features that describe the patient's clinical state at the decision time, i.e. the control visit, ii) the decision taken in terms of therapy revision and iii) the outcome obtained on the metabolic control during the following monitoring period. A new case is first classified into a protypical monitoring situation, and then similar cases are retrieved and shown to the user. This tool is fully integrated in a Web-based distributed system for Diabetes Management, developed within in the EU project T-IDDM.

Decision Support Techniques↗
PubMed1998

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.

Diabetes Mellitus, Type 1↗
PubMed1998

A development environment for knowledge-based medical applications on the World-Wide Web.

The World-Wide Web (WWW) is increasingly being used as a platform to develop distributed applications, particularly in contexts, such as medical ones, where high usability and availability are required. In this paper we propose a methodology for the development of knowledge-based medical applications on the web, based on the use of an explicit domain ontology to automatically generate parts of the system. We describe a development environment, centred on the LISPWEB Common Lisp HTTP server, that supports this methodology, and we show how it facilitates the creation of complex web-based applications, by overcoming the limitations that normally affect the adequacy of the web for this purpose. Finally, we present an outline of a system for the management of diabetic patients built using the LISPWEB environment.

Artificial Intelligence↗
PubMed1998

A randomized study comparing methylprednisolone plus chlorambucil versus methylprednisolone plus cyclophosphamide in idiopathic membranous nephropathy.

To assess whether chlorambucil or cyclophosphamide may have a better therapeutic index in patients with idiopathic membranous nephropathy, we compared two regimens based on a 6-mo treatment, alternating every other month methylprednisolone with chlorambucil or methylprednisolone with cyclophosphamide. Patients with biopsy-proven membranous nephropathy and with a nephrotic syndrome were randomized to be given methylprednisolone (1 g intravenously for 3 consecutive days followed by oral methylprednisolone, 0.4 mg/kg per d for 27 d) alternated every other month either with chlorambucil (0.2 mg/kg per d for 30 d) or cyclophosphamide (2.5 mg/kg per d for 30 d). The whole treatment lasted 6 mo; 3 mo with corticosteroids and 3 mo with one cytotoxic drug. Among 87 patients followed for at least 1 yr, 36 of 44 (82%; 95% confidence interval [CI], 67.3 to 91.8%) assigned to methylprednisolone and chlorambucil entered complete or partial remission of the nephrotic syndrome, versus 40 of 43 (93%; 95% CI, 80.9 to 98.5%) assigned to methylprednisolone and cyclophosphamide (P = 0.116). Of patients who attained remission of the nephrotic syndrome, 11 of 36 in the chlorambucil group (30.5%) and 10 of 40 in the cyclophosphamide group (25%) had a relapse of the nephrotic syndrome between 6 and 30 mo. The reciprocal of plasma creatinine improved in the cohort groups followed for 1 yr for both treatment groups (P < 0.01) and remained unchanged when compared with basal values in the cohort groups followed for 2 and 3 yr. Six patients in the chlorambucil group and two in the cyclophosphamide group did not complete the treatment because of side effects. Four patients in the chlorambucil group but none in the cyclophosphamide group suffered from herpes zoster. One patient per group developed cancer. It is concluded that in nephrotic patients with idiopathic membranous nephropathy both treatments may be effective in favoring remission and in preserving renal function for at least 3 yr.

Adolescent↗
PubMed1998

Mining biomedical time series by combining structural analysis and temporal abstractions.

This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM.

Algorithms↗
PubMed1997

DT-Planner: an environment for managing dynamic decision problems.

The problem of formulating plans under uncertainty and coping with dynamic decision problems is a major task of both artificial intelligence and control theory applications in medicine. In this paper we will describe a software package, called DT-Planner, designed to represent and solve dynamic decision problems that can be modelled as Markov decision processes, by exploiting a novel graphical formalism, called influence view. An influence view is a directed acyclic graph that depicts the probabilistic relationships between the problem state variables in a generic time transition; additional variables, called event variables, may be added, in order to describe the conditional independencies between state variables. By using the specified conditional independence structure, an influence view may allow a parsimonious specification of a Markov decision process. DT-Planner lets the user specify and manage models through a user-friendly graphical interface, and implements efficient for policy determination algorithms. DT-Planner is written in C with Open Interface libraries and can be obtained, for non commercial use, via anonymous ftp without charge.

Algorithms↗
PubMed1997

In vivo study of the kinetics of thiamine and its phosphoesters in the deafferented rat cerebellum.

The effects of chemical (CD) and surgical (SD) deafferentation of the cerebellum on different steps of the metabolism of thiamine (Th), thiamine monophosphate (ThMP) and thiamine pyrophosphate (ThPP) were evaluated in vivo in rats. CD was carried out by i.p. injection of 3-acetylpyridine, followed by harmaline and niacinamide. SD was carried out by complete dissection of the peduncles of the left cerebellar hemisphere. Under steady state condition the radioactivity of Th and its phosphoesters was determined in plasma and whole cerebellum after an i.p. injection of thiazole-[2(14)C]-thiamine (30 micrograms:1.25 micro Ci). Analytical data were processed by using an improved mathematical compartmental model, which allowed the calculation of fractional rate constants (FRC), turnover rates (TR) and turnover times (TT). Both CD and SD caused a significant reduction of TR values for Th phosphorylation to ThPP, dephosphorylation of ThPP to ThMP and Th, and ThMP, but not Th, release. TT for all Th compounds were increased compared to controls, indicating a general slowing of thiamine metabolism in the deafferented cerebellum. These results indicate an imbalance in the thiamine metabolism resulting from the impaired activity of cerebellar neurons. The possible implications of the changes in rate of Th compound turnover with respect to biochemical changes in cerebellar ataxia are discussed.

Afferent Pathways↗
PubMed1996

Learning temporal probabilistic causal models from longitudinal data.

Medical problems often require the analysis and interpretation of large collections of longitudinal data in terms of a structural model of the underlying physiological behavior. A suitable way to deal with this problem is to identify a temporal causal model that may effectively explain the patterns observed in the data. Here we will concentrate on probabilistic models, that provide a convenient framework to represent and manage underspecified information; in particular, we will consider the class of Causal Probabilistic Networks (CPN). We propose a method to perform structural learning of CPNs representing time-series through model selection. Starting from a set of plausible causal structures and a collection of possibly incomplete longitudinal data, we apply a learning algorithm to extract from the data the conditional probabilities describing each model. The models are then ranked according to their performance in reconstructing the original time-series, using several scoring functions, based on one-step ahead predictions. In this paper we describe the proposed methodology through an example taken from the diabetes monitoring domain. The selection process is applied to a set of input-output models that generalize the class of ARX models, where the inputs are the insulin and meal intakes and the outputs are the blood glucose levels. Although the physiological process underlying this particular application is characterized by strong non-linearities and low data reliability, we show that it is possible to obtain meaningful results, in terms of conditional probability learning and model ranking power.

Algorithms↗
PubMed1996

Distributed intelligent data analysis in diabetic patient management.

This paper outlines the methodologies that can be used to perform an intelligent analysis of diabetic patients' data, realized in a distributed management context. We present a decision-support system architecture based on two modules, a Patient Unit and a Medical Unit, connected by telecommunication services. We stress the necessity to resort to temporal abstraction techniques, combined with time series analysis, in order to provide useful advice to patients; finally, we outline how data analysis and interpretation can be cooperatively performed by the two modules.

Computer Communication Networks↗
PubMed1995

Adaptive controllers for intelligent monitoring.

The project we describe here is aimed at assisting out-patients affected by Insulin Dependent Diabetes Mellitus. Our approach exploits the usual scheme of diabetic patients management, based on (i) a periodic evaluation of patients' metabolic control performed by the physician, and (ii) patient-tailored tables for self-adjustments of insulin dosages. Following this scheme we have defined a system built on a two-level architecture. The High Level Module exploits both medical knowledge and clinical information in order to assess an insulin protocol, defined in terms of insulin timing, type, and total amount. The High Level Module exchanges information with the Low Level Module in order to define the control actions to be taken at the low level, as well as to periodically evaluate protocol adequacy on the basis of patient data. The goal of the Low Level Module, whose characteristics can be adaptively modified by the High Level Module, is to suggest the next insulin dosage, depending on the actual blood glucose measurement and a certain pre-defined insulin delivery protocol. The Level Control Module is based on an adaptive controller, consisting of a Fuzzy Set Controller and an ARX (Autoregressive eXogenous input) Model. The scheme here presented may be conveniently viewed in a telemedicine context, in which the low level controller is implemented on a portable device communicating to the high level controller, implemented on a remote computer. A preliminary assessment has been performed, analyzing a data set of 60 patients provided by the American Association of Artificial Intelligence, Artificial Intelligence in Medicine Subgroup, and the implementation of the system is currently in progress.

Computer Simulation↗
PubMed1994

Experimental subarachnoid hemorrhage: events related to anti-oxidant enzymatic systems and eicosanoid peroxide enhancement.

Experimental and clinical studies have emphasized the role of free radicals in the pathogenesis of vasospasm and neurological dysfunction after subarachnoid hemorrhage (SAH). Increases in both enzymatic (arachidonic acid cascade and eicosanoid peroxide production) and non-enzymatic (thiobarbituric acid reactive substances production) lipid peroxidation were found, pointing out the key role of arachidonic acid cascade in impairing membrane functionality in the post-hemorrhage brain. The aim of this work is to investigate whether a correlation exists between time-dependent modifications of eicosanoid peroxide production ("ex vivo" release of leukotriene C4 = LTC4) and antioxidant enzymatic systems in the brain after experimental subarachnoid hemorrhage in the rat. The release of the LTC4 is significantly enhanced at 1, 6 and 48 hours after SAH induction. Cu-Zn superoxide dismutase (SOD) activity is significantly reduced at 6 and 48 hours after SAH induction; Mn-SOD activity is significantly affected at 1, 6 and 48 hours after the hemorrhage. GSH-Px activity is significantly reduced only in the late phase (48 hours) after SAH. The linear regression of statistical analysis, performed to investigate any possible relationship among time-dependent modifications shows that the "ex vivo" release of LTC4 is significantly related to the decreasing trend of MnSOD activity (p < 0.001). The present results suggest that after SAH, a deficit in endogenous anti-oxidant defenses may play a role in making the brain more susceptible to lipid peroxidative events.

Animals↗