1,721,093 research outputs found
Supporting decisions in medical applications: the Knowledge Management perspective
No full textIn 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
A knowledge-based web server as a development environment for web-based knowledge servers
No full textIntegrating case based and rule based reasoning in a decision support system: evaluation with simulated patients
No full textWe present a Web-based knowledge management and decision support system for Type I Diabetes patients' care. The tool exploits the integration of two methodologies, Case Based Reasoning and Rule Based Reasoning, and supports physicians in the definition of therapeutic strategies. Such a work is being integrated in the EU funded T-IDDM project architecture. In this paper we report a first evaluation obtained on simulated patients
Exploiting multi-modal reasoning for knowledge management and decision support: an evaluation study
No full textWe present the first evaluation results : of a knowledge management and decision support system for Type I diabetes patients' care. Such system, meant to help physicians in therapy revision, relies on the integration of Rule Based Reasoning and Case Based Reasoning, and exploits both explicit and implicit knowledge. Reliability was positively judged by a group of expert diabetologists; an increase in its performances is foreseen as new knowledge will be acquired, through the system usage in clinical practice
Building telemedicine systems for supporting decisions in diabetes care: a report from a running experience
No full textThis paper describes some issues that should be investigated to implement telemedicine systems designed for effectively supporting decisions in diabetic patients management, namely situation assessment, information sharing, and knowledge management. The solutions and experiences carried on in this field within a European Union (EU)-funded project, called T-IDDM (Telematic Management of Insulin Dependent Diabetes Mellitus), are reported
Case-based retrieval to support the treatment of end stage renal failure patients
No full textObjective: 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
Temporal data mining for the quality assessment of hemodialysis services
No full textObjective: This paper describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services, on the basis of the time series
automatically collected during hemodialysis sessions.
Methods: Intelligent data analysis and temporal data mining techniques are applied to gain insight and to discover knowledge on the causes of unsatisfactory clinical results. In particular, two new methods for association rule discovery and temporal rule discovery are applied to the time series. Such methods exploit several preprocessing techniques, comprising data reduction, multi-scale filtering and temporal abstractions.
Results: We have analyzed the data of more than 5800 dialysis sessions coming from 43 different patients monitored for 19 months. The qualitative rules associating the
outcome parameters and the measured variables were examined by the domain
experts, which were able to distinguish between rules confirming available background
knowledge and unexpected but plausible rules.
Conclusion: The new methods proposed in the paper are suitable tools for knowledge discovery in clinical time series. Their use in the context of an auditing system for dialysis management helped clinicians to improve their understanding of the patients’ behavior
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