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M Stefanelli

Publications and source records attributed to M Stefanelli.

At least 37 records · Page 2Linked to original sources
PubMed2001

The socio-organizational age of artificial intelligence in medicine.

The increasing pressure on Health Care Organizations (HCOs) to ensure efficiency and cost-effectiveness, balancing quality of care and cost containment, will drive them towards a more effective management of medical knowledge derived from research findings. The relation between science and health services has until recently been too casual. The primary job of medical research has been to understand the mechanisms of disease and produce new treatments, not to worry about the effectiveness of the new treatments or their implementation. As a result many new treatments have taken years to become part of routine practice, ineffective treatments have been widely used, and medicine has been opinion rather than evidence based. This results in suboptimal care for patients. Knowledge management technology may provide effective approaches in speeding up the diffusion of innovative medical procedures whose clinical effectiveness have been proved: the most interesting one is represented by computer-based utilization of evidence-based clinical guidelines. As researchers in Artificial Intelligence in Medicine (AIM), we are committed to foster the strategic transition from opinion to evidence-based decision making. Reviews of the effectiveness of various methods of guideline dissemination show that the most predictable impact is achieved when the guideline is made accessible through computer-based and patient specific reminders that are integrated into the clinician's workflow. However, the traditional single doctor-patient relationship is being replaced by one in which the patient is managed by a team of health care professionals, each specializing in one aspect of care. Such shared care depends critically on the ability to share patient-specific information and medical knowledge easily among them. Strategically there is a need to take a more clinical process view of health care delivery and to identify the appropriate organizational and information infrastructures to support this process. Thus, the great challenge for AIM researchers is to exploit the astonishing capabilities of new technologies to disseminate their tools to benefit HCOs by assuring the conditions of knowledge management and organizational learning at the fullest extent possible. To achieve such a strategic goal, a guideline can be viewed as a model of the care process. It must be combined with an organization model of the specific HCO to build patient careflow management systems. Artificial intelligence can be extensively used to design innovative tools to support all the development stages of those systems. However, exploiting the knowledge represented in a guideline to build them requires to extend today's workflow technology by solving some challenging problems.

Artificial Intelligence↗
PubMed2001

Simulation of a stroke unit careflow.

This paper describes the development and use of a simulation model representing part of the medical practice within a Stroke Unit. In particular, we modelled the medical activities as described in a guideline for the ischemic stroke treatment, adopted by the Stroke Unit of our hospital. The Petri net formalism has been chosen for the model representation. The numerical parameters have been estimated both using a database of about 100 patients collected during the last two years, and eliciting knowledge from the neurologists. A commercial tool was used for performing simulations, while ad-hoc routines were written for tailoring the result presentation to the specific context. We consider simulation a very useful preliminary step for the subsequent implementation of a patient workflow (careflow) management system. In fact, simulation is based on the process model (the clinical practice guideline) and on the organisation model (human and technological resources), so allowing to detect bottlenecks in the care delivery organisation and to find the optimal resource allocation. For example, we show that simulation has been able to find some of the causes of the delay in the patients treatment, and accordingly, to suggest changes in the organisation.

Brain Ischemia↗
PubMed2000

Diabetic patients management exploiting case-based reasoning techniques.

In this paper we propose a case-based decision support tool, designed to help physicians in 1st type diabetes therapy revision through the intelligent retrieval of data related to past situations (or 'cases') similar to the current one. A case is defined as a set of variable values (or features) collected during a visit. We defined taxonomy of prototypical patients' conditions, or classes, to which each case should belong. For each input case, the system allows the physician to find similar past cases, both from the same patient and from different ones. We have implemented a two-steps procedure; (1) it finds the classes to which the input case could belong; (2) it lists the most similar cases from these classes, through a nearest neighbor technique, and provides some statistics useful for decision taking. The performance of the system has been tested on a data-base of 147 real cases, collected at the Policlinico S. Matteo Hospital of Pavia. The tool is fully integrated in the web-based architecture of the EU funded Telematic management of Insulin Dependent Diabetes Mellitus (T-IDDM) project.

Case-Control Studies↗
PubMed2000

Guideline-based careflow systems.

This paper describes a methodology for achieving an efficient implementation of clinical practice guidelines. Three main steps are illustrated: knowledge representation, model simulation and implementation within a health care organisation. The resulting system can be classified as a 'guideline-based careflow management system'. It is based on computational formalisms representing both medical and health care organisational knowledge. This aggregation allows the implementation of a guideline, not only as a simple reminder, but also as an 'organiser' that facilitates health care processes. As a matter of fact, the system not only suggests the tasks to be performed, but also the resource allocation. The methodology initially comprehends a graphical editor, that allows an unambiguous representation of the guideline. Then the guideline is translated into a high-level Petri net. The resources, both human and technological necessary for performing guideline-based activities, are also represented by means of an organisational model. This allows the running of the Petri net for simulating the implementation of the guideline in the clinical setting. The purpose of the simulation is to validate the careflow model and to suggest the optimal resource allocation before the careflow system is installed. The final step is the careflow implementation. In this phase, we show that the 'workflow management' technology, widely used in business process automation, may be transferred to the health care setting. This requires augmenting the typical workflow management systems with the flexibility and the uncertainty management, typical of the health care processes. For illustrating the proposed methodology, we consider a guideline for the management of patients with acute ischemic stroke.

Artificial Intelligence↗
PubMed2000

Intelligent analysis of clinical time series: an application in the diabetes mellitus domain.

This paper describes the application of a method for the intelligent analysis of clinical time series in the diabetes mellitus domain. Such a method is based on temporal abstractions and relies on the following steps: (i) 'pre-processing' of raw data through the application of suitable filtering techniques: (ii) 'extraction' from the pre-processed data of a set of abstract episodes (temporal abstractions); and (iii) 'post-processing' of temporal abstractions; the post-processing phase results in a new set of features that embeds high level information on the patient dynamics. The derived features set is used to obtain new knowledge through the application of machine learning algorithms. The paper describes in detail the application of this methodology and presents some results obtained on simulated data and on a data-set of four diabetic patients monitored for > 1 year.

Artificial Intelligence↗
PubMed2000

A multi-modal reasoning methodology for managing IDDM patients.

We present a knowledge management and decision support methodology for insulin dependent diabetes mellitus (IDDM) patients care. Such methodology exploits the integration of case based reasoning (CBR) and rule based reasoning (RBR), with the aim of helping physicians during therapy planning, by overcoming the intrinsic limitations shown by the independent application of the two reasoning paradigms. RBR provides suggestions on the basis of a situation detection mechanism that relies on formalized prior knowledge; CBR is used to specialize and dynamically adapt the rules on the basis of the patient's characteristics and of the accumulated experience. When the case library is not representative of the overall population, only RBR is applied to define a therapy for the input situation, which can then be retained, enriching the case library competence. The paper reports the first evaluation results, obtained both on simulated examples and on real patients. This work was developed within the EU funded telematic management of insulin dependent diabetes mellitus (T-IDDM) project, and is fully integrated in its web-based architecture.

Bayes Theorem↗
PubMed2000

[Radionuclide imaging in oral implantations. Personal experience in maxillary sinus elevation].

PURPOSE: Presently nuclear medicine techniques are not very popular in oral implantology, but they can play an interesting role in this surgical field too. In particular bone scan with 99mTc-MDP allows to evaluate the function of oral implants and the survival of bone grafts. We report our experience with skull bone scan in maxillary sinus lifting. MATERIAL AND METHODS: We performed a three-year follow-up on 13 patients treated with inlay-one stage uni- or bilateral sinus lifting with a mixture composed of 90% bovine bone powder and of 10% small bone splinters and autogenous fibrin glue. We performed imaging studies and quantitated implant MDP uptake from the mean values at the surgical site to the 5th neck vertebral ratio (M/V index). The M/V index was also statistically compared with the one measured in 13 patients with severe resorption of distal upper dental arches (bone height less than 0.5 cm) and in 63 patients with normal dental status. Scintigraphic data were interpreted in the light of clinical, radiological and histologic findings. RESULTS: All oral implants appeared to be fixed and radiographs showed good positioning and bone adhesion; bone height exceeded 1 cm. Peri implant biopsy material was formed by normal mature bone tissue without bovine bone granules, necrotic areas and inflammatory cells. The highest bone activity (M/V index: 1.54-2.57) was observed 1-4 months after sinus lifting. Then MDP uptake decreases and 18 months after surgery radionuclide uptake in maxillary arches is homogeneous, with M/V values of 0.81-0.88. The average M/V value in the 18 surgical sites was clearly higher than in the resorbed (1.44 vs 0.64; Kruskall-Wallis ANOVA test; Dunn's method; p < 0.05) and normal (1.44 vs 0.73; p < 0.05) maxillary arches. DISCUSSION AND CONCLUSIONS: 99mTc-MDP can show the transformation of newformed into mature bone and then allows in vivo visualization of implant osteointegration. The importance of our work lies in the use of radionuclide imaging to assess both the function of oral implants inserted by a complex surgical technique and the expected bioactive properties of the filling mixture. Nevertheless the strong active autogenous compounds do not permit to detect the real agent of local bone induction. An interesting finding is that implant stability is biologically gained 18 months after intervention and that the uptake index facilitates comparison of sequential scans and confirmation of local bone growth. Therefore high MDP uptake by the implant 2-3 years after surgery can suggest mechanical or septic bone injury which can lead to implant failure if not treated promptly. In conclusion we believe that quantitative bone scan is a valid diagnostic tool in the follow-up of oral implants inserted with sinus lifting, though our experience needs confirmation on larger series and new research is warranted to understand the real mechanisms of assisted bone regeneration.

Adult↗
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↗
PubMed1999

Protocol-based reasoning in diabetic patient management.

We propose a system for teleconsultation in Insulin Dependent Diabetes Mellitus (IDDM) management, accessible through the use of the net. The system is able to collect monitoring data, to analyze them through a set of tools, and to suggest a therapy adjustment in order to tackle the identified metabolic problems and to fit the patient's needs. The therapy revision has been implemented through the Episodic Skeletal Planning Methodi, it generates an advice and employs it to modify the current therapeutic protocol, presenting to the physician a set of feasible solutions, among which she can choose the new one.

Adult↗
PubMed1999

Decision support systems in health economics.

This article describes a system addressed to different health care professionals for building, using, and sharing decision support systems for resource allocation. The system deals with selected areas, namely the choice of diagnostic tests, the therapy planning, and the instrumentation purchase. Decision support is based on decision-analytic models, incorporating an explicit knowledge representation of both the medical domain knowledge and the economic evaluation theory. Application models are built on top of meta-models, that are used as guidelines for making explicit both the cost and effectiveness components. This approach improves the transparency and soundness of the collaborative decision-making process and facilitates the result interpretation.

Computer Simulation↗
PubMed1999

Supporting decisions in diabetic patients management through case-based retrieval.

In this paper we present a tool for the intelligent retrieval of past cases to support Insulin Dependent Diabetes Mellitus patients therapy revision. This tool was designed for assisting physicians during periodical monitoring visits. Each case is represented through i) a collection of features that describe the patient's clinical state at the decision time, i.e. the control visit, ii) the decision taken in terms of therapy revision and iii) the outcome obtained on the metabolic control during the following monitoring period. A new case is first classified into a protypical monitoring situation, and then similar cases are retrieved and shown to the user. This tool is fully integrated in a Web-based distributed system for Diabetes Management, developed within in the EU project T-IDDM.

Decision Support Techniques↗
PubMed1998

A distributed system for diabetic patient management.

This paper describes a telemedicine system for diabetic patients management, presenting its architecture, the technical solutions adopted and the methodologies on which it is based. The system, designed to provide decision support in a distributed environment, is composed of two modules, a Patient Unit and a Medical Unit, connected by telecommunication services. We outline how the two modules can interact to perform an effective monitoring and a cooperative control of glucose metabolism. In particular, we detail the data analysis tasks performed by the two units and how the results are exploited to assist patients and physicians in revising and adjusting the therapeutic protocol. We will finally describe the current prototypical implementation of the system that uses HTTP as the communication protocol and HTML pages as the graphical user interface.

Diabetes Mellitus, Type 1↗
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

Seizure exacerbation and developmental regression with carbamazepine.

BACKGROUND: Unexpected exacerbation of seizures may occur following initiation of treatment with carbamazepine (CBZ). We reviewed the occurrence of such reactions in our patient population at a tertiary care children's hospital. METHODS: A retrospective analysis of our clinic database identified 129/691 (18.6%) patients with epilepsy treated with CBZ, as monotherapy. 38/129 children were later switched to another drug. In 11/38 (28.5%) clinical and/or EEG deterioration was observed. Two patients identified at another institution with similar exacerbation were also included in our analysis. We report on the findings in these 13 cases. RESULTS: Two groups were identified: Group I--6 patients with normal neurological exam, normal EEG background, and a diagnosis of idiopathic generalized epilepsy. Group II--7 patients with an abnormal neurological exam and/or abnormal EEG background. Following introduction of CBZ therapy, worsening of preexisting seizures, appearance of new seizure types, behavioral regression, and accompanying EEG deterioration were reported in both groups. Dramatic improvement in seizure control occurred, following withdrawal of CBZ and substitution of another anticonvulsant. CONCLUSION: Physicians treating epilepsy must be aware that CBZ can exacerbate seizures, and cause developmental regression in children. Careful patient selection, when choosing CBZ as treatment, and prompt recognition of clinical deterioration and intervention, may help avoid or reverse these paradoxical reactions.

Adolescent↗
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

Organizational & medical ontologies for co-operative patient management.

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 this paper, we discuss an ontological foundation for a prototypical health care MAS, that is, a so-called Distributed Healthcare Information System (D-HIS), viewed as an organization of cognitive agents and making use of an ontological library written in the standard language Ontolingua.

Computer Communication Networks↗
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↗