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OHDSI.ORG

OHDSI – Observational Health Data Sciences and Informatics

Darr; Skip to Main Content. Mission, Vision & Values. The Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. All our solutions are open-source. OHDSI has established an international network of researchers and observational health databases with a central coordinating center housed at Columbia University. Read more about us.

http://www.ohdsi.org/

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Patrick Ryan

105 Co●●●●●●dge Ct

Au●●on , Pennsylvania, 19403

US

1.91●●●●2723
pb●●@cornell.edu

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Patrick Ryan

105 Co●●●●●●dge Ct

Au●●on , Pennsylvania, 19403

US

1.91●●●●2723
pb●●@cornell.edu

View this contact

Patrick Ryan

105 Co●●●●●●dge Ct

Au●●on , Pennsylvania, 19403

US

1.91●●●●2723
pb●●@cornell.edu

View this contact

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OHDSI – Observational Health Data Sciences and Informatics | ohdsi.org Reviews
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Darr; Skip to Main Content. Mission, Vision & Values. The Observational Health Data Sciences and Informatics (or OHDSI, pronounced Odyssey) program is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. All our solutions are open-source. OHDSI has established an international network of researchers and observational health databases with a central coordinating center housed at Columbia University. Read more about us.
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4 support for ohdsi
5 collaborators
6 standards
7 software tools
8 methods
9 research
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OHDSI – Observational Health Data Sciences and Informatics | ohdsi.org Reviews

https://ohdsi.org

Darr; Skip to Main Content. Mission, Vision & Values. The Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. All our solutions are open-source. OHDSI has established an international network of researchers and observational health databases with a central coordinating center housed at Columbia University. Read more about us.

INTERNAL PAGES

ohdsi.org ohdsi.org
1

Software – OHDSI

https://www.ohdsi.org/analytic-tools

Darr; Skip to Main Content. Mission, Vision & Values. Scientific best practices are only useful if the scientific community can actually use them. Within the OHDSI community, we believe one of the best ways to ensure adoption and consistent application of scientific best practices is by embedding them in open-source software and making the software freely available for all to use. See for yourself, try any of these demos of open source analytics tools:. Extract, transform, and load. 8211; epidemiologic d...

2

Who We Are – OHDSI

https://www.ohdsi.org/who-we-are

Darr; Skip to Main Content. Mission, Vision & Values. OHDSI is a multi-stakeholder, interdisciplinary collaborative. Mission, Vision & Values. Join us for the OHDSI Annual Symposium. Sept 23-24 in Washington DC!

3

Collaborators – OHDSI

https://www.ohdsi.org/who-we-are/collaborators

Darr; Skip to Main Content. Mission, Vision & Values. Eun Kyoung Ahn, RN, PhD. Assistant Professor, Department of Nursing. Ayad Ali, PhD. Global Patient Safety, Eli Lilly and Company. Charles Bailey, MD, PhD. Assistant Professor of Pediatrics, Perelman School of Medicine at the University of Pennsylvania. Children’s Hospital of Philadelphia. Juan M. Banda, PhD. Maura Beaton, MS. Rimma Belenkaya, MS, MA. Memorial Sloan Kettering Cancer Center. Tomas Bergvall, PhD. WHO Uppsala Monitoring Centre. William Du...

4

Data Holders – OHDSI

https://www.ohdsi.org/who-we-serve/data-holders

Darr; Skip to Main Content. Mission, Vision & Values. There are many organizations which are trying to learn from observational health data:. Insurance companies have claims data that can tell them about their beneficiaries. Health systems like hospitals and doctor’s offices have electronic health records that provide clinical care for their patients. Pharmaceutical companies and academic institutions conduct registries in which they are collecting non-interventional data. Sept 23-24 in Washington DC!

5

CALYPSO for study population evaluation – OHDSI

https://www.ohdsi.org/analytic-tools/calypso-for-study-population-evaluation

Darr; Skip to Main Content. Mission, Vision & Values. CALYPSO for study population evaluation. CALYPSO for study population evaluation. CALYPSO (Criteria Assessment Logic for Your Population Study in Observational data) is a web user interface to define, instantiate and evaluate a study population and the implications of inclusion criteria. Checkout the interactive Calypso live demo. GitHub Repository: https:/ github.com/OHDSI/Calypso. Join us for the OHDSI Annual Symposium. Sept 23-24 in Washington DC!

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outcomesinsights.com outcomesinsights.com

Welcome to Our Blog – Outcomes Insights

http://www.outcomesinsights.com/2015/06/18/welcome-to-our-blog

Epidemiology, Health Economics, and Bioinformatics. Analyses of Observational Data. Welcome to Our Blog. Welcome to Our Blog. Welcome to Our Blog. Our blog will span the breadth of expertise we have within the company. Topics may include traditional programming (e.g., Ruby, C, Go, Java, Javascript, etc.), statistical programming (R, SAS, Stata, etc.), reimbursement and coding, market research, survey implementation, statistics, biomedical informatics, OHDSI. June 18th, 2015. About the Author: Mark Danese.

outcomesinsights.com outcomesinsights.com

Our Team – Outcomes Insights

http://www.outcomesinsights.com/our-team

Epidemiology, Health Economics, and Bioinformatics. Analyses of Observational Data. Dr Danese is a collaborator with the Observational Health Data Science and Informatics (OHDSI) collaborative, helping to develop open-source tools to improve the conduct of observational studies. He is also a member of the following organizations: the American Statistical Association, Academy Health, the Society for Medical Decision Making, the International Society for Pharmacoepidemiology, the American Medical Infor...

vivo.health.unm.edu vivo.health.unm.edu

Lambert, Christophe G.

https://vivo.health.unm.edu/display/n25960

Skip to Main Content. CGLambert [at] unm [dot] edu. Lambert, Christophe G. Associate Professor of Medicine. Associate Professor of Medicine, Department of Internal Medicine. Associate Professor of Medicine, Center for Global Health. Associate Professor of Medicine, Translational Informatics Division. Program. The OHDSI/OMOP common data model. In addition I perform bioinformatic analyses of genomics datasets with current projects in pediatric Malaria in collaboration with Dr. DJ Perkins in the Center ...

jane.biosemantics.org jane.biosemantics.org

Journal / Author Name Estimator

http://jane.biosemantics.org/faq.php

Journal / Author Name Estimator. Is it safe to send my abstract to Jane? How does Jane work? How often is the data behind Jane updated? Which journals are included in Jane? Which authors are included in Jane? Which papers are included in Jane? What is the Article Influence score? I would like to include Jane in my application. Is it possible to access Jane programmatically? Is it safe to send my abstract to Jane? How does Jane work? For the computer geeks: we use the open source search engine Lucene.

pedsnet.org pedsnet.org

PEDSnet Common Data Model - PEDSnet

https://pedsnet.org/data/common-data-model

A Pediatric Learning Health System. Data Security and Privacy. Conducting multi-institutional clinical research requires a research platform that allows participating research partners to use the data resources in a predictable and efficient way. Common data models are one approach to achieving this aim. PEDSnet makes data available in two common data models, the PEDSnet CDM and the PCORnet CDM. PEDSnet Common Data Model. Collaborative's OMOP common data model. As this spider-web shows, the PEDSnet CDM i...

computationalhealthcare.com computationalhealthcare.com

Computational Healthcare

http://www.computationalhealthcare.com/library

Platform used for developing Computational Healthcare. Load data from large HCUP and State healthcare datasets. Compute aggregate statistics in a privacy preserving manner. Build machine learning models for predicting outcomes. Benchmark and Interpret model performance. Protocol Buffers are used to represent patients, visits as well as aggregate statistics. LevelDB allows use of removable encrypted drives for at-rest security. Installation and Supported databases. Loading and Exploring data. This databas...

out-inc.com out-inc.com

Our Team – Outcomes Insights

http://www.out-inc.com/our-team

Epidemiology, Health Economics, and Bioinformatics. Analyses of Observational Data. Dr Danese is a collaborator with the Observational Health Data Science and Informatics (OHDSI) collaborative, helping to develop open-source tools to improve the conduct of observational studies. He is also a member of the following organizations: the American Statistical Association, Academy Health, the Society for Medical Decision Making, the International Society for Pharmacoepidemiology, the American Medical Infor...

schuemie.net schuemie.net

Schuemie.net

http://www.schuemie.net/index.php

Moved to Hong Kong. For the next year we'll be living in Hong Kong, working with several academic partners in the region including Hong Kong University. Part of my job here is to continue the work on the OHDSI. We bought a sailing ship to roam the seven seas. Starting this year, I have a new job! I've joined Janssen R&D. Where I will continue to work closely together with my OMOP colleages, as well as the folks at the Epidemiology Analytics department. Old versus new in New York. Starting in New York.

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OHDSI – Observational Health Data Sciences and Informatics

Darr; Skip to Main Content. Mission, Vision & Values. The Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. All our solutions are open-source. OHDSI has established an international network of researchers and observational health databases with a central coordinating center housed at Columbia University. Read more about us.

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