Biomedical subjects

Stefania Montani

Publications and source records attributed to Stefania Montani.

9 recordsLinked to original sources

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↗

PubMed2005

Case-based retrieval to support the treatment of end stage renal failure patients.

OBJECTIVE: In the present paper, we describe an application of case-based retrieval to the domain of end stage renal failure patients, treated with hemodialysis. MATERIALS AND METHODS: Defining a dialysis session as a case, retrieval of past similar cases has to operate both on static and on dynamic features, since most of the monitoring variables of a dialysis session are time series. Retrieval is then articulated as a two-step procedure: (1) classification, based on static features and (2) intra-class retrieval, in which dynamic features are considered. As regards step (2), we concentrate on a classical dimensionality reduction technique for time series allowing for efficient indexing, namely discrete Fourier transform (DFT). Thanks to specific index structures (i.e. k -d trees), range queries (on local feature similarity) can be efficiently performed on our case base, allowing the physician to examine the most similar stored dialysis sessions with respect to the current one. RESULTS: The retrieval tool has been positively tested on real patients' data, coming from the nephrology and dialysis unit of the Vigevano hospital, in Italy. CONCLUSIONS: The overall system can be seen as a means for supporting quality assessment of the hemodialysis service, providing a useful input from the knowledge management perspective.

Decision Support Systems, Clinical↗

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

Telemedicine in the management of young patients with type 1 diabetes mellitus: a follow-up study.

DCCT (Diabetes Control and Complications Trial) study showed that tight metabolic control of diabetes mellitus can delay the onset and/or reduce the frequency of vascular complications. Telemedicine, i.e. telecommunications and information technologies in health care, is a useful tool to achieve the DCCT goals. Our European Community (EC) sponsored Telematic management of Insulin-Dependent Diabetes Mellitus (T-IDDM) project implements a telemedicine service through on a careful analysis of current medical practice. The system is based on two components: Patient Unit (PU) and Medical Unit (MU) connected by a Telecommunication system (TS). PU allows data collection and transmission from the patient's house to the hospital, assists self-monitoring activity and suggests insulin variations. PU communicates patient's current metabolic state the MU. MU assists the physician in periodic evaluation and suggests the prescriptions to communicate back defining a treatment protocol. TS system is based on telephone lines, relying on the Intranet technology. To test the system functionality and potential impact in type 1 diabetes clinical practice, we enrolled 6 patients (4 males and 2 females), aged 9.9-15.8 yrs, with disease duration 2.1-6.4 yrs, intensively treated. One girl run out after a 1-year follow-up HbA1c levels decreased, but not significantly. Insulin requirement reduced, significantly in 2 patients (p = 0.02 and p = 0.07). A positive correlation was between number of links and protocol changes (p = 0.01), between number of protocols changes and HbA1c decrease (p = 0.02). In pediatric patients periodical visits are necessary, but T-IDDM enables continuity of care improving access and activities. An index is represented by the high number of messages between the 2 Units, seeming weekly exchange.

Adolescent↗

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 decisions in medical applications: the knowledge management perspective.

In the medical domain, different knowledge types are typically available. Operative knowledge, collected during every day practice, and reporting expert's skills, is stored in the hospital information system (HIS). On the other hand, well-assessed, formalised medical knowledge is reported in textbooks and clinical guidelines. We claim that all this heterogeneous information should be secured and distributed, and made available to physicians in the right form, at the right time, in order to support decision making: in our view, therefore, a decision support system cannot be conceived as an independent tool, able to substitute the human expert on demand, but should be integrated with the knowledge management (KM) task. From the methodological viewpoint, case based reasoning (CBR) has proved to be a very well suited reasoning paradigm for managing knowledge of the operative type. On the other hand, rule based reasoning (RBR) is historically one of the most successful approaches to deal with formalised knowledge. To take advantage of all the available knowledge types, we propose a multi modal reasoning (MMR) methodology, that integrates CBR and RBR, for supporting context detection, information retrieval and decision support. Our methodology has been successfully tested on an application in the field of diabetic patients management.

Adolescent↗

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↗

PubMedDate not supplied

Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients.

We present a multi-modal reasoning (MMR) methodology that integrates case-based reasoning (CBR), rule-based reasoning (RBR) and model-based reasoning (MBR), meant to provide physicians with a reliable decision support tool in the context of type 1 diabetes mellitus management. In particular, we have implemented a decision support system that is able to jointly exploit a probabilistic model of the glucose-insulin system at the steady state, a RBR system for suggestion generation and a CBR system for patient's profiling. The integration of the CBR, RBR and MBR paradigms allows for an optimized exploitation of all the available information, and for the definition of a therapy properly tailored to the patient's needs, overcoming the single approaches limitations. The system has been tested both on simulated and on real patients' data.

Artificial Intelligence↗