Biomedical subjects

Paolo Terenziani

Publications and source records attributed to Paolo Terenziani.

7 recordsLinked to original sources

PubMed2006

Extending temporal databases to deal with telic/atelic medical data.

OBJECTIVE: In this paper, we aim at defining a general-purpose data model and query language coping with both "telic" and "atelic" medical data. BACKGROUND: In the area of Medical Informatics, there is an increasing realization that temporal information plays a crucial role, so that suitable database models and query languages are needed to store and support it. However, despite the wide range of approaches in the area, in this paper we show that a relevant class of medical data cannot be properly dealt with. METHODOLOGY: We first show that data models based on the "point-based" semantics, which is (implicitly or explicitly) assumed by the totality of temporal database approaches, have several limitations when dealing with "telic" data. We then propose a new model (based on the "interval-based" semantics) to cope with such data, and extend the query language accordingly. RESULTS: We propose a new three-sorted model and a query language to properly deal with both "telic" and "atelic" medical data (as well as non-temporal data). Our query language is flexible, since it allows one to switch from "atelic" to "telic" data, and vice versa. CONCLUSION: In this paper, we demonstrate the feasibility of a database approach copying with both telic and atelic data as needed in several (medical) applications.

Artificial Intelligence↗

PubMed2006

Towards a comprehensive treatment of repetitions, periodicity and temporal constraints in clinical guidelines.

OBJECTIVE: In this paper, we define a principled approach to represent temporal constraints in clinical guidelines and to reason (i.e., perform inferences in the form of constraint propagation) on them. We consider different types of constraints, including composite and repeated actions, and propose different types of temporal functionalities (e.g., temporal consistency checking). BACKGROUND: Constraints about actions, durations, delays and periodic repetitions of actions are an intrinsic part of most clinical guidelines. Although several approaches provide expressive temporal formalisms, only few of them deal with the related temporal reasoning issues. METHODOLOGY: We first propose a temporal representation formalism and two temporal reasoning algorithms. Then, we consider the trade-off between the expressiveness of the formalism and the computational complexity of the algorithms, in order to devise a correct, complete and tractable approach. Finally, we show how the algorithms can be exploited to provide clinical guideline systems with different types of temporal facilities. RESULTS: Our approach offers several advantages. During the guideline acquisition phase, it enables to represent temporal constraints, and to check their consistency. In the execution phase, it checks the consistency between the execution times of the actions and the constraints in the guidelines, and provides query answering and simulation facilities.

Algorithms↗

PubMed2004

A context-adaptable approach to clinical guidelines.

One of the most relevant obstacles to the use and dissemination of clinical guidelines is the gap between the generality of guidelines (as defined, e.g., by physicians' committees) and the peculiarities of the specific context of application. In particular, general guidelines do not take into account the fact that the tools needed for laboratory and instrumental investigations might be unavailable at a given hospital. Moreover, computer-based guideline managers must also be integrated with the Hospital Information System (HIS), and usually different DBMS are adopted by different hospitals. The GLARE (Guideline Acquisition, Representation and Execution) system addresses these issues by providing a facility for automatic resource-based adaptation of guidelines to the specific context of application, and by providing a modular architecture in which only limited and well-localised changes are needed to integrate the system with the HIS at hand.

Computer Graphics↗

PubMed2004

Towards a flexible integration of clinical guideline systems with medical ontologies and medical information systems.

The integration of a computer-based system dealing with clinical guidelines with a medical ontology can provide several advantages, including standardization and knowledge sharing. Furthermore, in order to operate in the clinical practice, guideline systems must also interact with the hospital databases to retrieve patients' data. Unfortunately, currently there seems not to be any "standard" consensus model either for the medical ontology or for (the conceptual structure of) patient databases (even if several interesting proposals have been carried out). In this paper we show how we are extending the GLARE guideline manager in order to strictly interact with both a medical ontology and a patient Database, in such a way that GLARE is not committed to any specific ontology and database (i.e., different ontologies and/or databases can be used).

Humans↗

PubMed2004

The GLARE approach to clinical guidelines: main features.

In this paper, we present GLARE, a domain-independent prototypical system for acquiring, representing and executing clinical guidelines. GLARE has been built within a 7-year project with Azienda Ospedaliera San Giovanni Battista in Turin (one of the largest hospitals in Italy) and has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure. GLARE is characterized by the adoption of advanced Artificial Intelligence (AI) techniques, to support medical decision making and to manage temporal knowledge.

Decision Making, Computer-Assisted↗

PubMed2003

Temporal consistency checking in clinical guidelines acquisition and execution: the GLARE's approach.

GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent system for the acquisition, representation and execution of clinical guidelines. Temporal constraints play an important role within clinical guidelines (e.g. to specify therapies). The treatment of such constraints is one of the distinguishing features of GLARE. During acquisition, GLARE supports (i) the representation and (ii) the check of the consistency of the temporal constraints. Moreover, it (iii) automatically checks that the times of execution of specific actions respect the general temporal constraints described in the guideline. Such a treatment of temporal constraints involves the extension of various Artificial Intelligence techniques.

Algorithms↗

PubMed2002

Supporting physicians in taking decisions in clinical guidelines: the GLARE "what if" facility.

GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent system for the acquisition, representation and execution of clinical guidelines. GLARE is unique in its approach to supporting the decision-making process of users/physicians faced with various alternatives in the guidelines. In many cases, the best alternative cannot be determined on the basis of "local information" alone (i.e., by considering just the selection criteria associated with the decision at hand), but must also take into account information stemming from relevant alternative pathways. Exploitation of "global information" available in the various pathways is made possible by GLARE through the "what if" facility, a form of hypothetical reasoning which allows users to gather relevant decision parameters (e.g., costs, resources, times) from selected parts of the guideline in a semi-automatic fashion. In particular, the extremely complex task of coping with temporal information involves the extension and adaptation of various techniques developed by the Artificial Intelligence (AI) community.

Decision Making, Computer-Assisted↗