Biomedical subjects

Gianpaolo Molino

Publications and source records attributed to Gianpaolo Molino.

6 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↗

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

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

Nitric oxide level profile in human liver transplantation.

The aim of this study was to monitor nitric oxide blood levels at various times intraoperatively and following liver transplantation in humans. Nitric oxide production was assessed directly as circulating nitrosyl-hemoglobin adducts by electron paramagnetic resonance spectroscopy in 22 patients undergoing orthotopic liver transplantation. Two significant peaks in nitrosylhemoglobin levels were detected at 5 and 60 min after reperfusion (5.02 +/- 3.33 arbitrary units and 5.75 +/- 4.19, respectively, vs 3.33 +/- 2.28 under basal state; P < 0.05 for both comparisons). Postoperative nitrosyl-hemoglobin levels remained elevated, up to 5.42 +/- 0.89 arbitrary units (P < 0.05 vs basal values). Neither soluble intercellular adhesion molecule-1 or soluble endothelial-leukocyte adhesion molecule concentrations were altered intraoperatively. Only the former was significantly raised after transplantation. Neutrophil elastase levels showed an early increase and remained high throughout surgery, returning to basal values after transplantation. No correlations were found among studied parameters. These data suggest that nitric oxide may play a role in ischemia-reperfusion phases in human liver transplantation. Mechanisms other than leukocyte-endothelial adhesion and neutrophil activation seem to affect nitric oxide production under these conditions.

Adult↗

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↗