Biomedical subjects

G Lanzola

Publications and source records attributed to G Lanzola.

12 recordsLinked to original sources

PubMed2001

Flexible guideline-based patient careflow systems.

Workflow Management Systems integrate domain and organisational knowledge to support business processes. When applied to the medical environment, they can be termed "Careflow Management Systems", and may be used to manage care delivery by enhancing co-operation among healthcare professionals. This paper focuses on care delivery based on clinical practice guidelines. Healthcare organisations are very different from industrial or commercial companies: their main goal is not profit, but maintaining and improving the health of the public. Therefore, outcomes are difficult to measure. Firstly, physicians, while playing a variety of roles, are quite independent decision-makers; secondly, the object of the process, i.e. the patient, may be involved in choosing treatment options, and may be treated by different institutions. For these reasons, the standard functionality of typical Workflow Management Systems must be strongly enhanced in order to cope with healthcare delivery needs. A major issue is accounting for exceptions. In most non-clinical settings this is not a problem because processes are very well defined and can often be easily controlled by some higher authority. As explained above, this does not happen in healthcare organisations. Responsibilities are widely shared, and health care professionals may be non-compliant with guidelines for a variety of reasons. The paper presents a classification of possible exceptions, and shows how the sequence of tasks described by a guideline may be altered, at the implementation level, in order to meet actual user needs, while maintaining guideline intentions as much as possible. A terminology server is also exploited towards this end. This work illustrates a prototype of a Careflow Management System based on an international guideline for ischemic stroke treatment, developed by the American Heart Association.

Artificial Intelligence↗

PubMed1999

A framework for building cooperative software agents in medical applications.

Exploiting the information technology may have a great impact on improving cooperation and interoperability among the different professionals taking part to the process of delivering health care services. New paradigms are therefore being devised considering software systems as autonomous agents able to help professionals in accomplishing their duties. To this aim those systems should encapsulate the skills for solving a given set of tasks and possess the social ability to cooperate in order to fetch the required information and knowledge. This paper illustrates a methodology facilitating the development of interoperable intelligent software agents for medical applications and proposes a generic computational model for implementing them. That model may be specialized in order to support all the different information and knowledge related requirements of a Hospital Information System. The architecture is being tested for implementing a prototype system able to coordinate the joint efforts of the professionals involved in managing patients affected by Acute Myeloid Leukemia.

Computer Simulation↗

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

Building patient workflow management systems by integrating medical and organizational knowledge.

Patient management is a distributed activity involving general practitioners, clinicians, analysts, nurses, etc. Thus an integrated Patient Workflow Management System (WfMS), based on a detailed model of both the organizational and medical knowledge, could heavily improve Health Care System's performance in terms of collaborative work and resource utilization. A set of tools was developed to improve 1) acquisition of medical knowledge represented through clinical practice Guideline, and 2) acquisition of organizational knowledge describing the work process.

Computer Simulation↗

PubMed1998

Towards cooperative patient management through organizational and medical ontologies.

Within knowledge and data engineering a new research paradigm is emerging based on the Multi-Agent System (MAS) architectural framework, allowing human and software agents to interoperate and thus cooperate within common application areas. In such a framework, knowledgeable agents of heterogeneous nature, that possess diverse but at least partially compatible or inter-translatable conceptual views, or ontologies, modeling both their own expertise and the external environment, make somehow available their information resources or problem-solving abilities for cooperative processes addressing the construction of a new agent or the achievement of some common goal through a correlated execution of tasks. In this paper, we restrict our analysis to the case of an organization of cognitive agents, illustrated with examples from a prototypical healthcare MAS, that is, a so-called Distributed Healthcare Information System (D-HIS). The prototype makes use of an ontological library written in the standard language Ontolingua. An ongoing application of the methodology to the main problem of Clinical Practice Guidelines (GLs) computer-based dissemination and enforcement is described.

Artificial Intelligence↗

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↗

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↗

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↗

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↗

PubMed1991

A specialized framework for Medical Diagnostic Knowledge Based Systems.

To have a knowledge based system (KBS) exhibiting an intelligent behavior, it must be endowed even with knowledge able to represent the expert's strategies, other than with domain knowledge. 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 that may be directly translated and implemented into a program. This paper describes a Specialized Framework for Medical Diagnostic Knowledge Based Systems able to 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 proved to be helpful in describing the diagnostic process in terms of the tasks by which it is composed of.

Artificial Intelligence↗

PubMed1990

NEOANEMIA: a knowledge-based system emulating diagnostic reasoning.

Medical diagnosis can be modeled in terms of the classical notions of abduction, deduction, and induction. Abduction is making a preliminary guess that allows one to establish a set of plausible diagnostic hypotheses, followed by deduction for exploring their consequences and induction for testing the hypotheses with available patient data or for planning the acquisition of new data. Such a description of diagnostic reasoning at a knowledge level helps the construction of an expert system by fashioning the adopted expert system building tool to reflect the structure of the problem rather than by fitting the problem to the tool. To this aim, reasoning strategies need to be represented abstractly, separate from medical facts and relations, to make the design more transparent and explainable.

Anemia↗