OHDSI MEETINGS THIS WEEK
Patient-level prediction (Western hemisphere) workgroup meeting - Wednesday at 12pm ET
Population-level estimation (Eastern hemisphere) workgroup - Wednesday at 3pm Hong Kong time
Architecture workgroup - Thursday at 1pm ET
Call for Sponsorship - To fund the 2017 symposium we need to raise $200K to cover operating costs of venue rental, lunch, audio/visuals and recordings fees. We’re reaching out to the entire community for sponsors who want to support this important event and allow us to build upon the success of past symposia. More details: OHDSI Symposium 2017 - call for sponsorship
2017 OHDSI Symposium - The date and location for the 2017 OHDSI Symposium has been confirmed! This year’s symposium will take place on Wednesday, October 18th at the Bethesda North Marriott. Full-day tutorial sessions will take place on October 19-20th. If you’re interested in attending, please save the date.
Symposium Registration - Registration for the symposium will open shortly. Check out the symposium website for updates: https://www.ohdsi.org/events/2017-ohdsi-symposium/
Hard times arouse an instinctive desire for authenticity.
-Coco Chanel COMMUNITY PUBLICATIONS
Predictive Analytics through Machine Learning in the clinical settings.
Viral warts (Human Papilloma Virus) as a potential risk for breast cancer among younger females.
S Atique, CH Hsieh, RT Hsiao, U Iqbal, PAA Nguyen, MM Islam, YJ Li, CY Hsu, TW Chuang and S Syed-Abdul,
Computer methods and programs in biomedicine, Jun 2017
There have been several reports on the role of human papillomavirus (HPV) in the etiology of breast cancer. To our knowledge, this is first study to use disease-disease association data-mining approach to analyzing viral warts and breast cancer to be conducted in Taiwanese population.We analyzed the Taiwan's National Health Insurance database (NHIDM data comprising of 23 million patient data) to examine the association between viral warts and female breast carcinoma. The patients were categorized into three groups: breast cancer only, viral warts only, and those with both breast cancer and viral warts. The Cox proportion hazard regression analysis was used to measure the effect of HPV on the time to breast cancer diagnosis. Multivariable analyzes and stratified analyzes using hazard ratios (HRs) were presented with 95% confidence intervals (CIs) after adjusting for age, and CCI.Among 807,578 HPV population, we identified 6014 breast cancer cases. The HPV group was associated with a significantly higher risk of developing breast cancer (HR, 1.18; 95% CI, 1.15-1.21; p< 0.001) compared with the non-HPV group. HPV patients with age group 18-39 was slightly higher risk of breast cancer occurrence (HR, 1.07; 95% CI, 1.01-1.13; p<.05). The risk of breast cancer in 10-year incidence was 7% higher for females less than 40 years and 23% for over 40 year's patients when compared with non-HPV patients of the same age group.Our study indicates that women who develop viral warts are at a significantly higher risk of developing breast cancer than women who have not diagnosed with viral warts. Thus, the presence of viral warts is a potential risk to breast cancer. Therefore, we suggest patients diagnosed with viral warts may get early screening for breast cancer.
Healthcare improvements from the unit to system levels: contributions to improving the safety and quality evidence base.
D Greenfield, U Iqbal and YJ Li,
International journal for quality in health care : journal of the International Society for Quality in Health Care, Jun 2017 01
Big data - smart health strategies. Findings from the yearbook 2014 special theme.
V Koutkias and F Thiessard,
Yearbook of medical informatics, Aug 2014 15
To select best papers published in 2013 in the field of big data and smart health strategies, and summarize outstanding research efforts.A systematic search was performed using two major bibliographic databases for relevant journal papers. The references obtained were reviewed in a two-stage process, starting with a blinded review performed by the two section editors, and followed by a peer review process operated by external reviewers recognized as experts in the field.The complete review process selected four best papers, illustrating various aspects of the special theme, among them: (a) using large volumes of unstructured data and, specifically, clinical notes from Electronic Health Records (EHRs) for pharmacovigilance; (b) knowledge discovery via querying large volumes of complex (both structured and unstructured) biological data using big data technologies and relevant tools; (c) methodologies for applying cloud computing and big data technologies in the field of genomics, and (d) system architectures enabling high-performance access to and processing of large datasets extracted from EHRs.The potential of big data in biomedicine has been pinpointed in various viewpoint papers and editorials. The review of current scientific literature illustrated a variety of interesting methods and applications in the field, but still the promises exceed the current outcomes. As we are getting closer towards a solid foundation with respect to common understanding of relevant concepts and technical aspects, and the use of standardized technologies and tools, we can anticipate to reach the potential that big data offer for personalized medicine and smart health strategies in the near future.