Biomedical subjects

M Stefanelli

Publications and source records attributed to M Stefanelli.

At least 55 records · Page 3Linked to original sources

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

An ontology-based framework for guideline-driven medical practice.

This paper describes a general framework for clinical practice guidelines development, dissemination and use. We propose an ontological description of the medical knowledge and of the organizational context, in order to produce clinical guidelines which, on one hand, can be widely shared between different institutions and, on the other, can be efficiently tailored to consider the peculiarities of each clinical context.

Acute Disease↗

PubMed1997

An ontology-based multi-agent architecture for distributed health-care information systems.

Managing patients in a shared-care context is a knowledge-intensive activity. To support cooperative work in medical care, computer technology should both augment the capabilities of individual specialists and enhance their ability of interacting with each other and with computational resources. Thus, a major shift is needed from centralized first generation health-care information systems to distributed environments composed of several interconnected agents, cooperating in maintaining a full track of the patient clinical history and supporting health-care providers in all the phases of the patient-management process. This paper outlines a general methodology to make architectural choices while designing or integrating new software components into a distributed health-care information system. A particular stress is laid on the specification of shared conceptual models, or ontologies, providing agents committing to them with the common semantic foundation required for effective interoperation.

Computer Communication Networks↗

PubMed1997

Ontology and terminology servers in agent-based health-care information systems.

A new research paradigm is emerging based on the multi-agent system architectural framework, allowing human and software agents to interoperate and thus cooperate within common application areas. Within a multi-agent system, the different "views of the world" of knowledgeable agents are to be bridged through their commitment to common ontologies and terminologies. We developed a general methodology for the design or integration of new components into a Health-care Information System conceived as a network of software and human agents. In our view, ontological and terminological services are entrusted to dedicated agents, namely ontology and terminology servers, allowing the configuration of suitable application ontologies for distributed applications. The role is described that such servers, operatively coordinated in order to preserve semantic coherence, should play within a distributed Health-care Information System.

Computer Communication Networks↗

PubMed1997

A patient workflow management system built on guidelines.

To provide high quality, shared, and distributed medical care, clinical and organizational issues need to be integrated. This work describes a methodology for developing a Patient Workflow Management System, based on a detailed model of both the medical work process and the organizational structure. We assume that the medical work process is represented through clinical practice guidelines, and that an ontological description of the organization is available. Thus, we developed tools 1) for acquiring the medical knowledge contained into a guideline, 2) to translate the derived formalized guideline into a computational formalism, precisely a Petri Net, 3) to maintain different representation levels. The high level representation guarantees that the Patient Workflow follows the guideline prescriptions, while the low level takes into account the specific organization characteristics and allow allocating resources for managing a specific patient in daily practice.

Artificial Intelligence↗

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

A case study in ontology library construction.

The goal of our work is to facilitate the development of medical knowledge-based systems by providing a library of reusable ontologies. The availability of such a library reduces the amount of knowledge acquisition required to create knowledge bases of new applications, and makes it easier to connect a knowledge-based system to existing data bases. This article presents a case study in constructing such a library. The emphasis is on studying the principles that underly the internal structure of the library as well as on the process of constructing and using the library. We envision that, in the future, application ontologies can be constructed by the selection and refinement of generic ontologies and domain ontologies from such a library.

Artificial Intelligence↗

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↗

PubMed1995

An ignorant belief network to forecast glucose concentration from clinical databases.

Ignorant Belief Networks (IBNs) are a class of Bayesian Belief Networks (BBNs) able to reason on the basis of incomplete probabilistic information and to incrementally refine the precision of the inferred probabilities as more information becomes available. In this paper, we will describe how can be used to develop a system able to forecast blood glucose concentration in patients affected by insulin dependent diabetes mellitus (IDDM). The major difference between our approach and the traditional ones is that probability distributions over the IBN are not provided by some human expert or by the current literature but they are directly extracted from a clinical database of IDDM patients. This choice capitalizes on the large amount of information generated by the daily control of blood glucose and allows the system to improve the accuracy of predictions as more information becomes available. We will show how, even with a very small subset of the information needed to specify a BBN, the IBN is able to carry out predictions about the future blood glucose concentration in a patient by explicitly taking into consideration the level of ignorance embedded in the network.

Artificial Intelligence↗

PubMed1995

Knowledge-acquisition tools for medical knowledge-based systems.

Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

Algorithms↗

PubMed1994

GAMES II Project: a general architecture for medical knowledge-based systems.

GAMES II aims at developing a comprehensive and commercially viable methodology to avoid problems ordinarily occurring in KBS development. GAMES II methodology proposes to design a KBS starting from an epistemological model of medical reasoning (the Select and Test Model). The design is viewed as a process of adding symbol level information to the epistemological model. The architectural framework provided by GAMES II integrates the use of different formalisms and techniques providing a large set of tools. The user can select the most suitable one for representing a piece of knowledge after a careful analysis of its epistemological characteristics. Special attention is devoted to the tools dealing with knowledge acquisition (both manual and automatic). A panel of practicing physicians are assessing the medical value of such a framework and its related tools by using it in a practical application.

Artificial Intelligence↗

PubMed1994

A framework for building and simulating qualitative models of compartmental systems.

This paper describes a framework, called QCMF (Qualitative Compartmental Modeling Framework), which assists the user in formulating models of a pathophysiological system and in analyzing their behaviors through the simulation of the effects of a variety of pathogenetic mechanisms and therapeutical treatments. QCMF has adopted the compartmental theory as modeling ontology: a system is represented as a finite set of interacting compartments. The user enters, through an iconic language and menus, the compartmental structure of a pathophysiological system and the definition of the kinds of functional relationships describing the interactions between compartments. Then, QCMF automatically generates a behavior model of the system. Such a model consists of a set of ordinary differential equations, which are qualitatively expressed, and is directly coded into the language which is interpreted by the simulation algorithm. The system behavior can be obtained by simulating the model starting from an initial state which describes the perturbations acting on the system. The code defining the initial state is automatically built by QCMF as well. Finally, explanations of the predicted behavior are also automatically generated.

Algorithms↗

PubMed1994

[Left valvular involvement in carcinoid: description of a case].

Right-sided valvular heart disease is a common complication of metastatic carcinoid tumors. On the contrary, left valve involvement is a rare finding. In our report we describe a patient in whom the subsequent involvement of right and left heart valves was documented by 2D and Doppler echocardiography. The patient was a 46-year-old woman who, in the last three years, complained of face flushing, postprandial diarrhea and shooting epigastric pain lasting for hours. She came at our observation for increasing dyspnoea, peripheral edemas and asthenia. 2D-echocardiography and Color Doppler revealed a severe tricuspid regurgitation and a turbulent blood flow across the pulmonary valve. An Angiographic examination confirmed the severe tricuspidal regurgitation and disclosed a mild pulmonary stenosis. These findings were associated with high 5 hydroxyndole acetic acid (5-HIAA) urinary excretion and the presence of a liver metastasizing ileal carcinoid tumor. Some months later, a new 2D echo-Doppler examination showed thickening and reduced motion of the posterior mitral leaflet, associated with regurgitation and an aortic insufficiency without evident structural valvular abnormalities. Even if carefully investigated no right-to-left shunt was found.

Angiography↗

PubMed1993

Medical diagnostic reasoning: epistemological modeling as a strategy for design of computer-based consultation programs.

The complexity of cognitive emulation of human diagnostic reasoning is the major challenge in the implementation of computer-based programs for diagnostic advice in medicine. We here present an epistemological model of diagnosis with the ultimate goal of defining a high-level language for cognitive and computational primitives. The diagnostic task proceeds through three different phases: hypotheses generation, hypotheses testing and hypotheses closure. Hypotheses generation has the inferential form of abduction (from findings to hypotheses) constrained under the criterion of plausibility. Hypotheses testing is achieved by a deductive inference (from generated hypotheses to expected findings), followed by an eliminative induction, constrained under the criterion of covering, which matches expected findings against patient's findings to select the best explanation. Hypotheses closure is a deductive-inductive type of inference very similar to the inferences operating in hypotheses testing. In this case induction matches the consequences of the generated hypotheses against the patient's characteristics or preferences under the criterion of utility. By using the language exploited in this epistemological model, it is possible to describe the cognitive tasks underlying the most influential knowledge-based diagnostic systems.

Artificial Intelligence↗

PubMed1993

European research efforts in medical knowledge-based systems.

This article describes the major projects going on in Europe in the field of Artificial Intelligence in Medicine. The important role of the Commission of the European Communities in providing the needed resources is stressed throughout the paper. Particular attention is given to the methodological and technological issues addressed by the European research teams, since the results which these teams accomplish are fundamental for a more extensive diffusion of knowledge-based systems in real medical settings. The variety of medical problems tackled shows that there is no field of medicine where the potential of advanced informatics technologies has not yet been assessed.

Artificial Intelligence↗

PubMed1993

Inferential knowledge acquisition.

This paper describes the approach we are pursuing for modeling inferential processes in knowledge-based systems. It is aimed at overcoming the lack of generality affecting many of the systems described in the literature. This mainly happens since the problem-solving method adopted by those systems is too closely tied to the particular domain problem over which the method itself has been modeled. We also describe a system called M-KAT (Medical Knowledge Acquisition Tool) which is useful in simplifying the process of acquiring inferential knowledge. M-KAT relies on an epistemological model of medical reasoning which represents a generalization of most of the problem-solving methods adopted in medical knowledge-based systems. The metarules formalism has been adopted as a mean for representing inferential knowledge and making its acquisition easier, thus allowing the computational implementation of the epistemological model of medical reasoning.

Artificial Intelligence↗

PubMed1993

Daily rhythmic variations in histamine in human blood.

We examined whether or not normal subjects have rhythmic changes of blood histamine levels. Daily predictable variations are present with 3 maxima and 3 minima and acrophase at 09.13. The significance of these changes is presently unknown.

Adult↗

PubMed1992

A specialized framework for medical diagnostic knowledge-based systems.

For a knowledge-based system (KBS) to exhibit an intelligent behavior, it must be endowed with knowledge enabling it to represent the expert's strategies. The elicitation task is inherently difficult for strategic knowledge, because strategy is often tacit, and, even when it has been made explicit, it is not an easy task to describe it in a form which may be directly translated and implemented into a program. This paper describes a Specialized Framework for Medical Diagnostic Knowledge-Based Systems that can help an expert in the process of building KBSs in a medical domain. The framework is based on an epistemological model of diagnostic reasoning which has proven to be helpful in describing the diagnostic process in terms of the tasks that it is composed of. It allows a straightforward modeling of diagnostic reasoning at the knowledge level by the domain expert, thus helping to convey domain-dependent strategies into the target KBS.

Artificial Intelligence↗