Data-driven Personalized Applications in Networks

A network models relationships. For a network that either encodes or supports internal information sharing activities, a better understanding of the network may enable data-driven applications (e.g., social network based recommendation), and boost both descriptive and predictive modeling of information flow in itself. In a multi-faceted manner, we propose in this thesis to contribute to several challenges that arise in the development of personalized applications in the general area of information and networks: 1) articulation of new patterns (and associated metrics) for individual user behavior and network structure; 2) exploitation of new forms of feature vector representations derived from large datasets integrating users and network structure; 3) modeling the space of information flow with network science models and in particular, the prediction of direction, outlier, and outcome for information flow; 4) improving the transparency of a network-based recommender system to enable exploration of the underlying information space. The proposed methodologies combine machine learning models, network analysis and statistical analysis, which can successfully address open problems in the field. They are validated on a range of real data and show practical significance in providing widely applicable models and displaying increased accuracy over useful baselines

Referral paths in the U.S. physician network

Abstract In this paper, we analyze the millions of referral paths of patients’ interactions with the healthcare system for each year in the 2006-2011 time period and relate them to U.S. cardiovascular treatment records. For a patient, a “referral path” records the chronological sequence of physicians encountered by a patient (subject to certain constraints on the times between encounters). It provides a basic unit of analysis in a broader referral network that encodes the flow of patients and information between physicians in a healthcare system. We consider referral networks defined over a range of interactions as well as the characteristics of referral paths, producing a characterization of the various networks as well as the physicians they comprise. We further relate these metrics and findings to outcomes in the specific area of cardiovascular care. In particular, we match a referral path to occurrences of Acute Myocardial Infarction (AMI) and use the summary measures of the referral path to predict the treatment a patient receives and medical outcomes following treatment. Some referral path features are more significant with respect to their ability to boost a tree-based predictive model, and have stronger correlations with numerical treatment outcome variables. The patterns of referral paths and the derived informative features illustrate the potential for using network science to optimize patient referrals in healthcare systems for improved treatment outcomes and more efficient utilization of medical resources

Effects of Rosuvastatin and Atorvastatin on Renal Function

Background: Several clinical trials have reported inconsistent findings for the effects of rosuvastatin (RSV) and atorvastatin (AN) on renal function. The aim of this meta-analysis was to investigate the effects of these 2 statins on glomerular filtration rate (GFR) and proteinuria respectively, and determine which is better. Methods and Results: PubMed, CENTRAL, Web of Knowledge, and ClinicalTrials.gov website were searched for randomized controlled trials. Eligible studies reported GFR and/or proteinuria during treatment with RSV or ATV compared with control (placebo, no statins, or usual care), or RSV compared with AN head to head. Trials that enrolled dialysis participants and teenagers were excluded. Statistical heterogeneity was assessed using the 12 statistic, and pooled results using the random-effects model. The standardized mean differences (SMD) and ratio of means (ROM) were measured, respectively, to analyze GFR and proteinuria. Sixteen trials with a total number of 24,278 participants were identified. Compared with control, changes in the SMD of GFR were 0.04 (95% confidence interval [CI]: 0.01-0.07) and 0.59 (95%CI: 0.12-1.06) for RSV and ATV, respectively. The ROMs of proteinuria were 0.59 (95%CI: 0.46-0.74) for RSV vs. the control group, and 1.23 (95%CI: 1.05-1.43) in the head-to-head comparison. Conclusions: Both RSV and ATV improve GFR, and ATV seems to be more effective in reducing proteinuria. The validity and clinical significance require high-quality intensive studies with composite clinic endpoints of kidney and death. (Circ J 2012; 76: 1259-1266)http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000303369800032&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Cardiac & Cardiovascular SystemsSCI(E)21ARTICLE51259-12667