Information storage and retrieval systems

This was prepared as a comprehensive examination of the methods used to provide
data storage and retrieval in a selected health insurance firm. A large portion
of the paper is devoted to a discussion of paper, microfilm and computer storage
as has been documented in various books, periodicals and industry publications.
The last portion is devoted to the results of a survey conducted in a large non-profit health insurance association examining the methods actually used to provide
data storage and retrieval.

A Content-Based Image Retrieval (CBIR) system is a mechanism intended to retrieve a particular image from a large image repository without resorting to any additional information about the image. Query-by-example (QBE) is a technique used by CBIR systems where an image is retrieved from the database based on an example given by the user. The effectiveness of a CBIR system can be measured by two main indicators: how close the retrieved results are to the desired image and how fast we got those results. In this thesis, we implement some classical image processing operations in order to improve the average rank of the desired image, and we also implement two object recognition techniques to improve the subjective quality of the best ranked images. Results of experiments show that the proposed system outperforms an equivalent CBIR system in QBE mode, both from the point of view of precision as well as recall.

This dissertation presents the results of research that led to the development of a complete, fully functional, image search and retrieval system with relevance feedback capabilities, called MUSE (MUltimedia SEarch and Retrieval Using Relevance Feedback). Two different models for searching for a target image using relevance feedback have been proposed, implemented, and tested. The first model uses a color-based feature vector and employs a Bayesian learning algorithm that updates the probability of each image in the database being the target based on the user's actions. The second model uses cluster analysis techniques, a combination of color-, texture-, and edge(shape)-based features, and a novel approach to learning the user's goals and the relevance of each feature for a particular search. Both models follow a purely content-based image retrieval paradigm. The search process is based exclusively on image contents automatically extracted during the (off-line) feature extraction stage. Moreover, they minimize the number and complexity of required user's actions, in contrast with the complexity of the underlying search and retrieval engine. Results of experiments show that both models exhibit good performance for moderate-size, unconstrained databases and that a combination of the two outperforms any of them individually, which is encouraging. In the process of developing this dissertation, we also implemented and tested several image features and similarity measurement combinations. The result of these tests---performed under the query-by-example (QBE) paradigm---served as a reference in the choice of which features to use in the relevance feedback mode and confirmed the difficulty in encoding the understanding of image similarity into a combination of features and distances without human assistance. Most of the code written during the development of this dissertation has been encapsulated into a multifunctional prototype that combines image searching (with or without an example), browsing, and viewing capabilities and serves as a framework for future research in the subject.

In order to improve the quality of care, there is urgent need to involve patients in their own healthcare. So to make patient centered health care system Personal Health Records are proposed as viable solution. This research discusses the importance of a Patient Centric Health Record system. Such systems can empower patients to participate in improving health care quality. It would also provide an economically viable solution to the need for better healthcare without escalating costs by avoiding duplication. The proposed system is Web-based; therefore it has high accessibility and availability. The cloud computing based architecture is used which will allow consumers to address the challenge of sharing medical data. PHR would provide a complete and accurate summary of the health and medical history of an individual by gathering data from many sources. This would make information accessible online to anyone who has the necessary electronic credentials to view the information.