Elpida T. Keravnou

Intelligent Data Analysis in Medicine and Pharmacology

From the Publisher: Intelligent Data Analysis in Medicine and Pharmacology consists of selected (and thoroughly revised) papers presented at the First International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-96) held in Budapest in August 1996 as part of the 12th European Conference on Artificial Intelligence (ECAI-96). IDAMAP-96 was organized with the motivation to gather scientists and practitioners interested in computational data analysis methods applied to medicine and pharmacology, aimed at narrowing the increasing gap between excessive amounts of data stored in medical and pharmacological databases on the one hand, and the interpretation, understanding and effective use of stored data on the other hand. Besides the revised Workshop papers, the book contains a selection of contributions by invited authors. The expected readership of Intelligent Data Analysis in Medicine and Pharmacology is researchers and practitioners interested in intelligent data analysis, data mining, and knowledge discovery in databases, particularly those who are interested in using these technologies in medicine and pharmacology. Researchers and students in artificial intelligence and statistics should find this book of interest as well. Finally, much of the presented material will be interesting to physicians and pharmacologists challenged by new computational technologies, or simply in need of effectively utilizing the overwhelming volumes of data collected as a result of improved computer support in their daily professional practice.

Position paper: Temporal representation and reasoning in medicine: Research directions and challenges

OBJECTIVE The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. BACKGROUND Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. METHODOLOGY The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. RESULTS We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader--including those who are unfamiliar with the topic--to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. CONCLUSIONS We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research.

What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems

First-generation expert systems have significant limitations, often attributed to their not being sufficiently deep . However, a generally accepted answer to “What is a deep expert system?” is still to be given. To answer this question one needs to answer “Why do first-generation systems exhibit the limitations they do?” thus identifying what is missing from first-generation systems and therefore setting the design objectives for second-generation (i.e. deep) systems. Several second-generation architectures have been proposed; inherent in each of these architectures is a definition of deepness. Some of the proposed architectures have been designed with the objective of alleviating a subset, rather than the whole set, of the first-generation limitations. Such approaches are prone to local, non-robust solutions. In this paper we analyze the limitations (under the categories: human-computer interaction, problem-solving flexibility, and extensibility) of the first-generation expert systems thus setting design goals for second-generation systems. On the basis of this analysis proposed second-generation architectures are reviewed and compared. The paper concludes by presenting requirements for a generic second-generation architecture.

A temporal reasoning framework used in the diagnosis of skeletal dysplasias

When gallium phosphide is etched with hot phosphoric acid from the surface of a crystal having a (1 1 1) plane, the etched surface becomes a flat and smooth plane inclined at an angle of 45 DEG to 55 DEG to the (1 1 1) plane. Accordingly, when an electroluminescent diode is manufactured by forming a p-n junction on a gallium phosphide crystal having a (1 1 1) plane and etching the crystal with a hot concentrated phosphoric acid etching solution to form a mesa structure, the side faces of the resulting crystal becomes inclined to the plane of the junction at an angle of nearly 45 DEG so that the visible rays generated in the p-n junction are totally reflected on the side faces, thus markedly increasing the intensity of emitted rays in the direction of the optical axis perpendicular to the principal plane of the p-n junction.

Deep and shallow models in medical expert systems

Abstract In the context of medical expert systems a deep system is often used synonymously with a system that models some kind of causal process or function. We argue that although causality might be necessary for a deep system it is not sufficient on its own. A deep system must manifest the expectations of its user regarding its flexibility as a problem solver and its human-computer interaction (dialogue structure and explanation structure). These manifestations are essential for the acceptability of medical expert systems by their users. We illustrate our argument by evaluating a representative sample of medical expert systems. The systems are evaluated from the perspective of how explicitly they incorporate their particular models of expertise and how understandably they progress towards solutions. The dialogue and explanation structures of these systems are also evaluated. The results of our analysis show that there is no strong correlation between causality and acceptability. On the basis of this we propose that a deep system is one that properly explicates its underlying model of human expertise.

Temporal diagnostic reasoning based on time-objects

Time is essential in diagnostic problem-solving. However, as with other commonsense tasks, time representation and reasoning is not a trivial undertaking. This probably explains why time has either been ignored or implicitly represented and used in the majority of diagnostic systems, medical or otherwise. Durations, temporal uncertainty and multiple temporal granularities are necessary requirements for medical problem-solving. Most general theories of time proposed in the literature do not address all these requirements, and some do not address any. The paper discusses time representation and reasoning in medical diagnostic problem-solving, building from a generic temporal ontology which covers the above temporal requirements. Much of what is discussed, however, is applicable to non-medical domains as well. It is argued that the diagnostic concepts (patient data, disorders, therapeutic-actions) are naturally modelled as time-objects. The resulting representation treats time as an integral dimension to these concepts, with special status. Time-object-based representations for generic hypotheses (disorders, actions) are discussed and illustrated; in the case of disorders the representation covers both an associational model and a causal-associational model. A central function of diagnostic problem-solving is deciding the compatibility of hypotheses with regard to a patient model. In this respect the paper discusses temporal and contextual screening of triggered hypotheses as well as accountings and conflicts between time-objects.

Modelling Medical Concepts as Time-Objects

Time is intrinsically related to medical problem-solving in general. Modelling time from the perspective of computer-based, competent, solution derivation of real-life medical problems is a challenging undertaking. Starting from the premise that temporal reasoning is an integral aspect of medical problem-solving, necessary requirements for medical temporal reasoning are listed, the notion of a time-object is proposed as an appropriate ontological primitive for modelling medical concepts, and generic models for patient data, disorders, and actions based on time-objects are discussed.

A Multidimensional and Multigranular Model of Time for Medical Knowledge-Based Systems

In temporal reasoning there are two interrelated issues; how to model time per se and how to model occurrences. In medical temporal reasoning the need for multiple granularities and multiple conceptual temporal contexts arises in relation to a model of time. Some occurrence can then be expressed with respect to different temporal contexts. This paper presents a multidimensional and multigranular model of time for knowledge-based problem solving, primarily for medical applications. Both the conceptual issues and the design issues underlying the implementation of the proposed model are discussed. The presented model of time has been developed in the context of a time ontology for medical knowledge engineering, whose principal primitives are the time-axis and the time-object. The notion of a time-axis constitutes the primitive for the proposed model of time, while the notion of a time-object aims to integrate time with other essential forms of knowledge, such as structural and causal knowledge, in the expression of different types of occurrences, thus resulting in the integral embodiment of time in such occurrences. The notion of a time-object and the overall ontology of occurrences is given only a cursory mention in this paper. The focus of the paper is the time model. More specifically, the paper presents the notion of a time-axis in the context of the overall time ontology and discusses at length the two classes of time-axes, namely the atomic axes and the spanning axes. The assertion language which has been developed, for the entire ontology, for the expression of axioms (deductive rules and integrity constraints), attribute constraints and propagation methods is presented and illustrated. The implementation of the time model in terms of a layered object-based system is also presented.

Temporal Abstraction of Medical Data: Deriving Periodicity

Temporal abstraction, the derivation of abstractions from time-stamped data, is one of the central processes in medical knowledge-based systems. Important types of temporal abstractions include periodic occurrences, trends, and other temporal patterns. This chapter discusses the derivation of periodic abstractions at a theoretical, domain-independent level, and in the context of a specific temporal ontology.

Intelligent Data Analysis In Medicine And Pharmacology: An Overview

Extensive amounts of data gathered in medical and pharmacological databases request the development of specialized tools for storing and accessing of data, data analysis, and effective use of stored data. Intelligent data analysis methods provide the means to overcome the resulting gap between data gathering and data comprehension. This chapter starts by presenting our view on the relation of intelligent data analysis to knowledge discovery in databases and data mining, and gives arguments why we have decided to use the term intelligent data analysis in the title of this book. It then discusses the needs and goals of intelligent data analysis in medicine and pharmacology. Next, it gives an overview of book chapters, characterizing them with respect to the intelligent data analysis methods used, as well as their application areas. Finally, it presents the overall purpose of this book.