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Biomedical subjects

R Bellazzi

Publications and source records attributed to R Bellazzi.

At least 19 recordsLinked to original sources
PubMed2006

A proposed semantic framework for diabetes education content management, customisation and delivery within the M2DM project.

M2DM (multi access services for telematic management of diabetes mellitus, ) is an EU-funded telemedicine project that aims at increasing the quality of diabetes care by improving communication between patients and caregivers. As part of this project, we have undertaken the initial work of describing the necessary requirements (framework) of an advanced educational component for M2DM in accordance with the latest Semantic Web concepts. This paper describes our proposed semantic framework for educational content management, customisation and delivery. A big internet challenge today is to find and push situation and user-specific quality knowledge to users based on their actual individual needs, circumstances and profiles at any given time. We believe that the semantic framework presented in this paper could be a good step towards meeting this challenge. Benefits for users, both developers and end users, of adopting such framework are also discussed. The ideas discussed in this paper could be easily adapted to other similar services besides M2DM and to different health topics besides diabetes mellitus.

Computer-Assisted Instruction↗
PubMed2006

The M2DM Project--the experience of two Italian clinical sites with clinical evaluation of a multi-access service for the management of diabetes mellitus patients.

OBJECTIVES: This paper presents a multi-access service for the management of diabetes mellitus patients and the results of its assessment in two Italian clinical sites. METHODS: The service was evaluated for one year in order to prove the advantages of these kind of systems from different points of view. In this paper the clinical, usability and technical outcomes are presented. RESULTS: The evaluation results show that, thanks to the high flexibility of the implemented service, the telemedicine management of diabetes patients is feasible, well accepted by patients and clinically effective. However, in Italy the problem of quantifying the reimbursement rate of telematic services and the impact they have on the organization are factors that may hamper their introduction in routine clinical practice. CONCLUSIONS: The evaluation study showed that the telemedicine intervention has been satisfactory both for physicians because it allows to constantly monitor the patients' blood glucose level and for patients because it strengthens their motivation to self-monitor the metabolic situation.

Adult↗
PubMed2004

Clustering gene expression data with temporal abstractions.

This paper describes a new technique for clustering short time series coming from gene expression data. The technique is based on the labelling of the time series through temporal trend abstractions and a consequent clustering of the series on the basis of their labels. Clustering is performed at three different levels of aggregation of the original time series, so that the results are organized and visualized as a three-levels hierarchical tree. Results on simulated and on yeast data are shown. The technique appears robust and efficient and the results obtained are easy to be interpreted.

Algorithms↗
PubMed2003

Three thiamine analogues differently alter thiamine transport and metabolism in nervous tissue: an in vivo kinetic study using rats.

Thiamine (T) analogues pyrithiamine, oxythiamine or amprolium in amounts 10-1000 times higher than labelled T, were i.p. injected into rats together with 14C-T (30 microg; 46.25 KBq). The radioactivity associated with T and its phosphoesters in the plasma and cerebral cortex, brainstem, cerebellum, and sciatic nerve were determined at time intervals from 0.25 to 240 h from injection. The experimental data obtained were processed with a mathematical compartmental model that calculated the fractional rate constants. These are the amount of content in a given compartment that is replaced in 1 h and expressed in per hour. The results showed that all three analogues inhibited thiamine entry from plasma. Instead, oxythiamine enhanced T phosphorylation to T pyrophosphate (TPP); amprolium and oxythiamine enhanced TPP dephosphorylation to monophosphate (TMP); pyrithiamine reduced TPP dephosphorylation and TMP formation, while none of the analogues modified TMP dephosphorylation to T. In conclusion, in living rats, the action of T analogues was much more complex than could be expected from their structure and action in vitro.

Amprolium↗
PubMed2002

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.

Blood Glucose Self-Monitoring↗
PubMed2002

Nonparametric AUC estimation in population studies with incomplete sampling: a Bayesian approach.

The estimation of the AUC in a population without frequent and/or fixed individual samplings is of interest because the number of plasma samples can often be limited due to technical, ethical and cost reasons. Non-linear mixed effect models can provide both population and individual estimates of AUC based on sparse sampling protocols; however, appropriate structural models for the description of the pharmacokinetics are required. Nonparametric solutions have also been proposed to estimate the population AUC and the associated error when particular sampling protocols are adopted. However, they do not estimate the individual AUCs and lack flexibility. Also a semiparametric method has been proposed for addressing the problem of sparse sampling in reasonably well designed studies. In this work, we propose and evaluate a nonparametric Bayesian scheme for AUC estimation in population studies with arbitrary sampling protocols. In the stochastic model representing the whole population, the individual plasma concentration curves and the "mean" population curve are described by random walk processes, allowing the application of the method to the reconstruction of any kind of "regular" curves. Population and individual AUC estimation are performed by numerically computing the posterior expectation through a Markov chain Monte Carlo algorithm.

Algorithms↗
PubMed2001

A hybrid input-output approach to model metabolic systems: an application to intracellular thiamine kinetics.

Models of the dynamics of complex metabolic systems offer potential benefits to the deep comprehension of the system under study as well as for the performance of certain tasks. Unfortunately, dynamic modeling of a great deal of metabolic systems may be problematic due to the incompleteness of the available knowledge about the underlying mechanisms and to the lack of an adequate observational data set. In theory, a valid alternative to classical structural modeling through ordinary differential equations could be represented by input-output approaches. But, in practice, such methods, which learn the nonlinear dynamics of the system from input-output data, fail when the experimental data set is poor either in size or in quality. Such a situation is not rare in the case of metabolic systems. This paper deals with a hybrid approach which aims at overcoming the problems addressed above. More specifically, it allows us to solve the identification problems of the intracellular thiamine kinetics in the intestine tissue. The method, which is half way between the structural and input-output approach, uses the outcomes of the simulation of a qualitative structural model to build a good initialization of a fuzzy system identifier. Such an initialization allows us to efficiently cope with both the incompleteness of knowledge and the inadequacy of the available data set, and to derive an input-output model of the intracellular thiamine kinetics in the intestine tissue. The comparison of the predictions of the intracellular thiamine kinetics obtained by the application of such a model with those obtained by traditional approaches, namely compartmental models, neural networks, and fuzzy systems, highlighted a better performance of our model. As the structural assumptions are relaxed, we obtained a model slightly less informative than a purely structural one but robust enough to be used as a simulator. The paper also discusses the interpretative potential offered by such a model, as tested on diabetic subjects.

Animals↗
PubMed2001

Compartmental model identification based on an empirical Bayesian approach: the case of thiamine kinetics in rats.

Compartmental models are a very popular tool for the analysis of experiments in living systems. There are three main aspects that have to be taken into account: the degree of detail of the model, its a priori identifiability and the a posteriori (numerical) identifiability. In some cases, where standard approaches are adopted, the models can be either a priori or a posteriori unidentifiable. The paper proposes model identification within a Bayesian framework, to solve a posteriori unidentifiability problems. In particular, a stochastic simulation algorithm is proposed to perform a Bayesian identification of compartmental models, and an empirical Bayesian technique is proposed to propagate information among multiple experiments. The power of this methodology was demonstrated by evaluating the kinetics of thiamine under several experimental conditions. The complexity of the existing model (nine parameters) and limited experimental data (8/12 for each model) caused a posteriori identifiability problems when standard approaches were adopted. The application of the methodology identifies all 28 models (four tissues under seven different conditions).

Animals↗
PubMed2001

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.

Computer Security↗
PubMed2001

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.

Artificial Intelligence↗
PubMed2001

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.

Algorithms↗
PubMed2001

Intelligent knowledge retrieval for decision support in medical applications.

Knowledge management and retrieval are key issues to be addressed in the medical domain, where a large amount of information is generally available, and where the expert's skills need to be properly shared across the organisation, with the aim of improving the quality of care. Case Based Reasoning (CBR) is a very well suited methodology for the Knowledge Management task, when knowledge is in the operative form. Nevertheless, also well-assessed, formalised medical knowledge, such as clinical guidelines, should be made available to physicians in order to optimise their reasoning process. To take advantage of both knowledge types, we have defined a Multi Modal Reasoning methodology, that integrates CBR and Rule Based Reasoning, for supporting context detection, information retrieval and therapy revision in diabetes care.

Artificial Intelligence↗
PubMed2001

Improving dialysis services through information technology: from telemedicine to data mining.

This paper discusses the issues related to use of Information Technology (IT) solutions in dialysis, and describes the implementation of some of them in a medium size dialysis center. First, starting from the analysis of the organization of public-health nephrology services, the potential role of IT is highlighted. Second, the main directions for IT exploitation in dialysis, namely telemedicine and automated monitoring of dialysis sessions are discussed. Third, the on-field implementation of these services is described, together with some preliminary results. The work here presented shows how IT may improve dialysis services by ameliorating quality and reducing costs.

Ambulatory Care Facilities↗
PubMed2000

Hamming clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation therapy.

The aim of the study is to demonstrate the usefulness of a new, non-linear classifier method, called Hamming clustering (HC), in selecting prognostic variables affecting overall survival in patients with head and neck cancer. In particular, the aim is to identify whether tumour proliferation parameters can be predictive factors of response in a set of 115 patients that receive either alternating chemo-radiotherapy or accelerated or conventional radiotherapy. HC is able to generate a set of understandable rules underlying the study objective; it can also select a subset of input variables that represent good prognostic factors. HC has been compared with other standard classifiers, providing better results in terms of classification accuracy. In particular, HC obtains the best accuracy of 74.8% (sensitivity of 51.1% and specificity of 91.2%) about survival. The rules found show that, besides the classical, well-known variables concerning the tumour dimension and the involved lymphonodes, some biological parameters, such as DNA ploidy, are also useful as predictive factors.

Aged↗
PubMed2000

Diabetic patients management exploiting case-based reasoning techniques.

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.

Case-Control Studies↗
PubMed2000

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.

Artificial Intelligence↗
PubMed2000

A multi-modal reasoning methodology for managing IDDM patients.

We present a knowledge management and decision support methodology for insulin dependent diabetes mellitus (IDDM) patients care. Such methodology exploits the integration of case based reasoning (CBR) and rule based reasoning (RBR), with the aim of helping physicians during therapy planning, by overcoming the intrinsic limitations shown by the independent application of the two reasoning paradigms. RBR provides suggestions on the basis of a situation detection mechanism that relies on formalized prior knowledge; CBR is used to specialize and dynamically adapt the rules on the basis of the patient's characteristics and of the accumulated experience. When the case library is not representative of the overall population, only RBR is applied to define a therapy for the input situation, which can then be retained, enriching the case library competence. The paper reports the first evaluation results, obtained both on simulated examples and on real patients. This work was developed within the EU funded telematic management of insulin dependent diabetes mellitus (T-IDDM) project, and is fully integrated in its web-based architecture.

Bayes Theorem↗