Roque Marín

Fuzzy Logic in Medicine

A Call for a Stronger Role for Fuzzy Logic in Medicine.- Fuzzy Information Granulation of Medical Images. Blood Vessel Extraction from 3-D MRA Images.- Breast Cancer Classification Using Fuzzy Central Moments.- Awareness Monitoring and Decision-Making for General Anaesthesia.- Depth of Anesthesia Control with Fuzzy Logic.- Intelligent Alarms for Anaesthesia Monitoring Based on a Fuzzy Logic Approach.- Fuzzy Clustering in Medicine: Applications to Electrophysiological Signal Processing.- Fuzzy Logic in a Decision Support System in the Domain of Coronary Heart Disease Risk Assessment.- A Model-based Temporal Abductive Diagnosis Model for an Intensive Coronary Care Unit.- A Fuzzy Model for Pattern Recognition in the Evolution of Patients.- Mass Assignment Methods for Medical Classification Diagnosis.- Acquisition of Fuzzy Association Rules from Medical Data.

A model and a language for the fuzzy representation and handling of time

Abstract In this paper we present a model for the representation and handling of fuzzy temporal references. We define the concepts of date, time extent, and interval, according to the formalism of possibility theory. We introduce relations between the temporal entities dates and intervals, interpreted as constraints on the distance between dates and projected onto Fuzzy Temporal Constraint Satisfaction Networks. We introduce a language for the representation and manipulation of temporal entities and relations, which captures some of the terms we use in our expressions in the natural language and therefore, it is a flexible and powerful interface for those systems in which the representation of fuzzy temporal information is necessary. Our approach permits a common interpretation of qualitative and quantitative temporal relations, facilitating the relativization of the meaning of the temporal relations to each specific application context and the verification of relations between temporal entities.

ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences

In this paper, we propose a new algorithm, called ClaSP for mining frequent closed sequential patterns in temporal transaction data. Our algorithm uses several efficient search space pruning methods together with a vertical database layout. Experiments on both synthetic and real datasets show that ClaSP outperforms currently well known state of the art methods, such as CloSpan.

Obtaining solutions in fuzzy constraint networks

Abstract We propose three methods for obtaining solutions in fuzzy constraint networks and study their application to the problem of ordering fuzzy numbers. The techniques proposed may be classified as defizziification functions which are applicable to any set of mutually dependent fuzzy numbers in which the dependence relationships are represented by means of metric constraints. We suggest the use of these techniques for ordering linked variables in an efficient manner, and discuss their behavior regarding several quality criteria. The first application realm of these techniques is temporal reasoning.

Temporal similarity measures for querying clinical workflows

OBJECTIVE In this paper, we extend a preliminary proposal and discuss in a deeper and more formal way an approach to evaluate temporal similarity between clinical workflow cases (i.e., executions of clinical processes). More precisely, we focus on (i) the representation of clinical processes by using a temporal conceptual workflow model; (ii) the definition of ad hoc temporal constraint networks to formally represent clinical workflow cases; (iii) the definition of temporal similarity for clinical workflow cases based on the comparison of temporal constraint networks; (iv) the management of the similarity of clinical processes related to the Italian guideline for stroke prevention and management (SPREAD). BACKGROUND Clinical processes are composed by clinical activities to be done by given actors in a given order satisfying given temporal constraints. This description means that clinical processes can be seen as organizational processes, and modeled by workflow schemata. When a workflow schema represents a clinical process, its cases represent different instances derived from dealing with different patients in different situations. With respect to all the cases related to a workflow schema, each clinical case can be different with respect to its structure and to its temporal aspects. Clinical cases can be stored in clinical databases and information retrieval can be done evaluating the similarity between workflow cases. METHODOLOGY We first describe a possible approach to the conceptual modeling of a clinical process, by using a temporally extended workflow model. Then, we define how a workflow case can be represented as a set of activities, and show how to express them through temporal constraint networks. Once we have built temporal constraint networks related to the cases to compare, we propose a similarity function able to evaluate the differences between the considered cases with respect to the order and duration of corresponding activities, and with respect to the presence/absence of some activities. RESULTS In this work, we propose an approach to evaluate temporal similarity between workflow cases. The proposed approach can be used (i) to query clinical databases storing clinical cases representing activities related to the management of different patients in different situations; (ii) to evaluate the quality of the service comparing the similarity between a (possibly synthetic) case, perceived as the good one with respect to a given clinical situation, and the other clinical cases; and (iii) to retrieve a particular class of cases similar to an interesting one.

Temporal similarity by measuring possibilistic uncertainty in CBR

Similarity is an essential concept in case-based reasoning (CBR). In domains in which time plays a relevant role, CBR systems require good temporal similarity measures to compare cases. Temporal cases are traditionally represented by a set of temporal features, defining time series and temporal event sequences. In the particular situation where these features are not homogeneous (i.e. combination of qualitative and quantitative information), systems find difficulties in performing the CBR cycle. Furthermore, temporal similarity measures cannot directly apply the efficient time series techniques, requiring new approaches to deal with these heterogeneous sequences. To this end, recent proposals are focused on direct matching between pairs of features within sequences, mainly based on classical distances. However, three limitations to the traditional approaches have been identified: (1) they do not consider the implicit temporal relations amongst all features of the sequence (ignoring a large amount of temporal information); (2) they ignore the uncertainty produced in any process of analogy; (3) they are designed to compare pairs of sequences, limiting their use to basic aspects of the Retrieval step of CBR (no benefits on other CBR steps). Temporal constraint networks have proved to be useful tools for temporal representation and reasoning, and can be easily extended to manage imprecision and uncertainty. An approach to solve similarity problems could be the transformation of these heterogeneous sequences into uncertain temporal relations, obtaining a temporal constraint network. The overall uncertainty of this network can be considered as an effective indicator of the sequences similarity. Therefore, this paper proposes a non-classical approach to measure temporal similarity of cases which are heterogeneous temporal event sequences. Given two or more sequences, the temporal similarity is measured by describing a unique temporal scenario of possibilistic temporal relations and calculating the uncertainty produced.

Fuzzy constraint networks for signal pattern recognition

This paper deals with representation and reasoning on information concerning the evolution of a physical parameter by means of a model based on the Fuzzy Constraint Satisfaction Problem formalism, and with which it is possible to define what we call Fuzzy Temporal Profiles (FTP). Based on fundamentally linguistic information, this model allows the integration of knowledge on the evolution of a set of parameters into a knowledge representation scheme in which time plays a fundamental role.The FTP model describes the behaviour of a physical parameter on the basis of a set of signal events, and which allows the evolution of the parameter between each pair of events to be modelled as signal episodes. Given the fundamentally linguistic nature of the information represented, the consistency analysis of this information is an essential task. Nevertheless, the obtention of the minimal representation of the network that defines an FTP is an NP-hard problem. In spite of this, we supply algorithms guaranteeing local levels of consistency that allow to correct a large proportion of the errors committed by a human expert in the linguistic description of the profile. Furthermore, we propose a new topology whose consistency can be guaranteed in polynomial time. We also study the applicability of this model in the recognition of signal patterns.

Fuzzy logic in a patient supervision system

A patient supervision system in progress for intensive and coronary care units, focused on patients with acute myocardial infarct is briefly described particularly regarding the role that fuzzy logic is playing in its design, and why this is so.

MODELING THE REPRESENTATION OF TIME FROM A FUZZY PERSPECTIVE

In this work we present a model for the representation of imprecise temporal information. The model permits the representation of absolute and relative temporal references and includes qualitative and quantitative relationships. We also present an efficient algorithm that combines the information represented, minimizes imprecisions, and detects inconsistencies. We discuss the practical use of the model through examples in different domains.

Medical knowledge management for specific hospital departments

Medical knowledge representation and management is concerned with how to organise the often vague clinical experience of medical staff required for computable models. However, few knowledge management and acquisition tools have entered routine use, since such tools are not perceived by physicians as part of the clinical information process. An attempt to partially solve this problem, we identify two key aspects of knowledge representation and management tasks. The first is to adopt a medical knowledge standardisation to provide a consistent terminology control and to simplify the integration between knowledge management tools and the health information system. The second is to establish an effective knowledge acquisition process in specific medical fields by adapting knowledge acquisition tools. Therefore, the main goal of this work is to define computational models and to design mechanisms for the effective acquisition and management of medical knowledge in real-life hospital departments. To this end, we analyse the representation of medical knowledge (based on deep-causal models) and the development of knowledge management tools (based on ontologies), integrated within the information processing activities of the clinical user. Finally, we illustrate its applicability in the Intensive Care Unit and Pediatry scenarios.