Medical informatics

Use of the Internet can increase patients' understanding about their medical conditions and offers opportunities to strengthen the patient-physician relationship, increase patient satisfaction, and improve health outcomes. However, physicians vary widely in the extent to which they accept patient online medical information seeking and make it part of the patient-physician relationship. This paper explores factors impacting the extent to which physicians advocate (encourage, speak in favor, or are supportive of) patient internet use. Specifically, using social cognitive theory as a theoretical base, this study develops a model of the determinants of physician advocation of patient use of the internet for information about medical conditions and treatments. Survey data collected from a random sample of 179 physicians licensed to practice medicine in Florida is used to test the proposed model. Proxy efficacy for patient internet use, social efficacy for enlisting patient internet use, performance outcomes expectations, and personal outcome expectations are shown to be significant determinants of physician professional advocation of patient internet use. In addition to its direct impact, proxy efficacy is shown to influence intention to advocate patient internet use indirectly thru social efficacy and outcome expectations, demonstrating the key role of this construct in the proxy agency model. Self-efficacy, in contrast, is not found to be a significant factor. Overall, the results support the proposed model of technology use.

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.

Microarray expression data which contains the expression levels of a large number of simultaneously observed genes have been used in many scientific research and clinical studies. Due to its high dimensionalities, selecting a small number of genes has shown to be beneficial for many tasks such as building prediction models from the microarray expression data or gene regulatory network discovery. Traditional gene selection methods, however, fail to take the class distribution into the selection process. In biomedical science, it is very common to have microarray expression data which is severely biased with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). These sample sets with biased distributions require special attention from researchers for identification of genes responsible for a particular disease. In this thesis, we propose three filtering techniques, Higher Weight ReliefF, ReliefF with Differential Minority Repeat and ReliefF with Balanced Minority Repeat to identify genes responsible for fatal diseases from biased microarray expression data. Our solutions are evaluated on five well-known microarray datasets, Colon, Central Nervous System, DLBCL Tumor, Lymphoma and ECML Pancreas. Experimental comparisons with the traditional ReliefF filtering method demonstrate the effectiveness of the proposed methods in selecting informative genes from microarray expression data with biased sample distributions.