Incremental Data Mining for Active and Adaptive Knowledge Base for Patient Image Retrieval

Introduction: The general perception that the use of information technology (IT) in health care is 10 to 15 years behind that in other industrial sectors such as banking, manufacturing and airline is rapidly changing. Faced with an unprecedented era of competition and managed care, health providers are now exploring the opportunities for using IT to improve quality while simultaneously reduce the cost of health care. Clinical decision support systems and expert systems (CDSSs / ESs) focus on utilizing artificial intelligence and data mining techniques to provide fast decision support for physicians. Although several success stories about CDSSs / ESs have been reported [Freudenheim 92, Nash 94], these systems usually lack the ability to adapt to pattern changes that are embedded in new data. This is due to the fact that the traditional algorithms utilized by these systems cannot learn on an incremental basis, i.e., once they are built, they cannot adjust their structures in which the knowledge is imbedded. Lack of incremental learning ability is not a unique phenomenon in health care expert systems. In fact, most of the machine learning algorithms developed to date are limited in their ability to adjust learned rules based on new, incoming data. In the Internet Age, when new data keep coming in at a high speed, this is a serious limitation for decision support systems. The main objective of this dissertation is to develop a new incremental neural network technique in order to support decision support systems' adaptive needs. An Incremental Neural Net (INN) algorithm that utilizes hidden layer activations to incrementally learn new patterns from incoming data is proposed. We then applied it to the Image Retrieval Expert System (IRES), a clinical decision support system for radiologists in University Medical Center (UMC), University of Arizona. The performance comparison between the INN and traditional neural net approach are compared. This chapter is organized as follows: section 1.1 briefly introduces the concept of data mining and incremental learning, which serve as technical foundations for this dissertation. Section 1.2 introduces the background of IRES project and describes its adaptive need. Section 1.3 addresses research motivation and objectives. Section 1.4 provides an overview of this dissertation.Digitized from a paper copy provided by the Physiological Sciences Graduate Interdisciplinary Program

A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed

An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions