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The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients.

Objective: Our objective was to develop an efficient way to search for radiology information on the World-Wide Web (WWW)., Materials and Methods: With the Harvest Information Discovery and Access System and a DECstation 3000 computer, each week we gather and update WWW documents that relate to radiology. To limit the size of the gathered documents, certain data (such as image files) are omitted. To date, we have gathered from 20 main radiology sites. By accessing our server, thousands of documents can be efficiently searched for specific radiology information., Results: At no charge, individuals with access to the WWW can request a search of our server for specific radiology information. We return to the requester in HyperText Markup Language a document with results of the search included as embedded pointers. Our server address, or uniform resource locator, is http://glimpse.cs.arizona.edu/radiology.html., Conclusion: Use of a server dedicated to radiology is an efficient way to search the internet for radiology resources, such as teaching files. We provide access to many sites with one search, and we select those sites to ensure that only relevant and timely information is gathered. We also index that information.