Summary of the 12 Most Common Cancers in the CancerLinQ Discovery (CLQD) Database

PURPOSE In 2014, the ASCO developed CancerLinQ (CLQ), a health technology platform for oncology. The CLQ Discovery (CLQD) database was created to make data available for research and this paper provides a summary of this database. METHODS This study described the clinical and demographic characteristics of the 12 most common cancers in the CLQD database. We included patients with a new malignant tumor diagnosis between January 1, 2013, and December 31, 2018, of the following cancers: breast, lung and bronchus, prostate, colon and rectum, melanoma of the skin, bladder, non-Hodgkin lymphoma, kidney and renal pelvis, uterus, leukemia, pancreas, and thyroid. Patients with an in-situ diagnosis were excluded. Summary statistics and Kaplan-Meier survival estimates were calculated for each tumor. RESULTS From 2013 to 2018, 491,360 patients were diagnosed with the study tumors. Breast cancer (139,506) was the most common, followed by lung and bronchus (70,959), prostate (63,303), and colon and rectum (53,504). The median age at diagnosis (years) was 61, 68, 68, and 64 in breast, lung and bronchus, prostate, and colon and rectum cohorts, respectively. Compared to the SEER 5-year overall survival estimates for several tumor types were higher in the CLQD database, possibly because of incomplete mortality capture in electronic health records. CONCLUSION This paper presents the first description of the CLQD database since its inception. CLQ will continue to evolve over time, and the breadth and depth of this data asset will continue to grow. ASCO and CLQ's long-term goal is to improve the quality of patient care and create a sustainable database for oncology researchers. These results demonstrate that CLQ built a scalable database that can be used for oncology research.

Architecture for Big Data Storage in Different Cloud Deployment Models

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

Leveraging the integrated EHR for trial matching across a nationwide network.

165 Background: The Guardian Research Network (GRN) is a nationwide consortium of integrated health systems, who share their electronic health records (EHR) to democratize clinical trial access through improvements in “process”. The GRN is a unique-in-class, free-to join, non-exclusive consortium leveraging the digital EHR---including labs, medications, demographics and non-discrete data (all text) data--- mining nightly for clinical trial candidates. Using a suite of NLP and AI tools, the GRN dramatically improves the efficiency of the clinical research staff, by electronically searching all records for the I/E criteria for trials. The GRN uses a central IRB, one contract and legal review, promising to revolutionize the trial accrual process and speed drug development. Methods: With a database of > 1.0M patients, the GRN reviews all active cancer records nightly from > 110 member hospitals to produce lists of trial candidates. Comprehensive electronic screens were filtered by manual reviews to rapidly find best candidates. Results: Our data collected over 10 mo suggest comprehensive electronic queries examining hundreds of thousands of records daily eliminate > 90% of ineligible patients in minutes. Manual review further refines eligible list. This is vastly different from current opportunistic screening approaches that examine only a tiny fraction of potential trial candidates (last week's new patients). Conclusions: The GRN has executed an all-inclusive approach to trial accrual, embedding a scalable database search technology within an integrated trial network. The novel approach seeks to exponentially expand operational capabilities of CTOs with limited staff, review all eligible patients, and solve for a large unmet need for democratizing trial access in the community. [Table: see text]

Cluster mechanism for hot data storage in HBase system

The evolution in different areas like e-business contributed to increase the number of data. So, a scalable database is required to accommodate a large number of data. Moreover, this data could be important and the number of accessing for reading or writing operations can make a pressure in some servers more than others, this pressure called unbalanced accessing. This will be achieved if a system that guarantees the distribution of this important data in useful form is used, because the pressure can causes a delay. Accordingly, when we design a multi storage node to keep this data, the pressure will transfer from a server to another. So, the proposed system searched for a satisfied solution to distribute all the users on the servers in a useful way to detect any mistake or repeating updating operation data by using identification feature. This method is applied on Hbase and called Clusterization.

Architecture for Big Data Storage in Different Cloud Deployment Models

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.