All abstracts for the 2017 OHDSI Symposium should be uploaded here. For more information about submission guidelines, please check out our 2016 call for posters page or contact the OHDSI manager at symposium@ohdsi.org

OHDSI Symposium - 2017 Abstract Submission Template (Abstracts due August 30th, 2017)

1. You will need an OHDSI Wiki account in order to upload your abstract submission or you authenticate with your GitHub or Google Credentials from the Login Page. If you don't already have an OHDSI Wiki account then please register using the following Google Form: https://goo.gl/forms/ciyMGiP0emC1PsPG2

(You will receive your new OHDSI Wiki account information via email within one business day)

2. Log into the OHDSI Wiki using the link in the top right corner of this Wiki page.

3. Click on the “Media Manager” link in the top right corner of the screen

4. Click on the “Upload” tab and select the file you wish to upload

5. Come back to the this page and select the “edit this page” icon on the right side of the screen

6. Select the “add images and other files” icon just above the editable box

7. Select your uploaded file. Be sure to include you name next to your submission.

Need more help? Check out our how-to submit guide with screenshots here.

• Ronquillo, Jay - Mobile Health (iPhone) App for Precision Diabetes Research Cohort Data Collection Using OMOP CDM
• Alvarado, Jose - Biomedical Search Engine
• Alvarado, Jose - Relational to Graph Database: Migration
• Sena, Anthony - Large Scale Risk Identification System for Proactive Safety Surveillance
• Reps, Jenna - Feasibility of using basic heuristics to simplify patient level prediction models
• Chandran, Urmila - Does it matter if I stay or go? Predicting patient-level attrition to evaluate study generalizability
• Defalco, Frank - Dataprint: A Novel Visualization Tool for Database Comparison
• Knoll, Chris - Cohort Characterization in ATLAS with Standardized Feature Generation
• Golbaz, Marzieh - Highly scalable patient-at-a-time transformation of observational databases into OMOP CDM v5 format using cloud-based open source tools
• Kim, Yeesuk - Comparative effectiveness of alendronate and raloxifene in reducing the risk of hip fracture in women with osteoporosis
• Schuemie, Martijn - A benchmark for population-level estimation methods
• Schuemie, Martijn - Empirical evaluation of the OHDSI Methods Library
• Swerdel, Joel - Predictive Modeling of Incident Heart Failure in Subjects with Newly Diagnosed Atrial Fibrillation
• Tian, Yuxi - Synthetic and Negative Control Evaluation Framework for Large-Scale Propensity Score Survival Analysis
• Johnston, Stephen - Development and validation of a model to predict cessation of antihyperglycemic medication after laparoscopic bariatric surgery among patients with type 2 diabetes
• van Mulligen, Erik - Annotating Dutch free-text patient records with OHDSI standard vocabulary concepts
• Sedlmayr, Martin - Towards an OHDSI-based Data-Sharing Infrastructure in Germany
• Rohit, Nigam - Learning Effective Clinical Treatment Pathways for Type-2 Diabetes
• Levine, Matthew - Terminology Information Loss and Gain
• Hilton, Charity - A Unified Chart Review Tool Integrating OMOP CDM and Unstructured Data
• Parr, Sharidan - Machine Learning to Classify Laboratory Tests in a National Electronic Health Record System Database
• Yuan,Chi -Criteria2Query: Automatically Transforming Clinical Research Eligibility Criteria Text to OMOP Common Data Model (CDM)-based Cohort Queries
• Jaehyeong, Cho AEGIS: Application for Epidemiological Geographic Information System
• Seng Chan, You Comparison of combination treatment in hypertension
• Doyeop, Kim GEMINI (GEneral exaMINing and visualizing application for paired Institution) - Automated data characteristic visualization tool for comparison of health information between institutions
• Makadia, Rupa Comparability Assessment of Cohorts with and without Laboratory Values
• Harshal, Soni Demonstrate Reusability and Effectiveness of Using an Intermediate Layer for OMOP CDM Conversion
• Harshal, Soni Automating CDM Conversion Using Machine Learning
• Harshal, Soni How effective is OMOP CDM Conversion for Claims Data?
• Qin, Ye RWE Case Studies with OMOP CDM in pharmaceutical product lifecycle
• Qin, Ye Predictive enrichment in trial design
• Qin, Ye Integrated evidence/insight platform supported by OMOP CDM and methods library
• Jung, Sungjae A regression analysis method for distributed large-scale data analysis
• Blacketer, Clair Creating a Framework for Evaluating Open Healthcare Claims
• Kyu-pyo, Kim CDM extension for loading and utilization of clinical genomic data
• Londhe, Ajit The Impact of Data Quality Annotations on Observational Data Research
• Fairbanks, James QueryGarden: growing healthy applications in well prepared SQL.
• Van Speybroeck, Michel Assessment of the conversion of ten European Databases to the OMOP CDM mapping and evaluation of the use of OHDSI tools in that process.
• Rijnbeek, Peter The European Observational Health Data Sciences and Informatics Initiative
• Pan, Xiaoyong Hybrid deep learning for predicting hypertensive disorder onset using temporal and non-temporal data
• Rijnbeek, Peter Conversion of a Dutch general practitioner’s database to the OMOP-CDM: The IPCI Database
• Rijnbeek, Peter Do we really need ‘Big Data” for patient-level predictive modelling?
• Moinat, Maxim Transforming Electronic Health Records from Swedish registers to the OMOP CDM
• Seng Chan, You Development of Clinically Informing application based on Recurrent Neural Network (CIReNN)
• Abedtash, Hamed Assessing Mapping Performance from HL7 Continuity of Care Documents to the OMOP CDM
• Gini, Rosa Obtaining estimates of outcome validity from a small set of parameters: the component strategy from the ADVANCE project
• Hajagos, Janos Mapping Common Data Model data tables to an HDF5 file for reproducible machine learning workflows
• Boland, Mary Regina boland_seawas_jamia_2017_abstract.pdf
• Kashyap, Mehr and Seneviratne, Martin High-throughput phenotyping using imperfectly-labeled training data
• Cepeda, M Soledad, Reps, Jenna and Ryan, Patrick Predicting Who Will Develop Treatment Resistant Depression after Being Newly Diagnosed With Depression
• Huser, Vojtech Facilitating analysis of measurements data though stricter model conventions: Exploring units variability across sites
• FitzHenry, Fern, final_pilot_of_nlp_common_data_model.pdf
• Sumathipala, Yohan and Huser Vojtech ysumathipala_medical_event_seasonality_ohdsi.pdf|Classification of temporal trends in healthcare research databases in support of data quality and hypothesis generation}}
• Aize, Cao quality_assurance_of_demographics_consistency_between_veterans_affairs_and_medicare_data.pdf
• Starr, Richard Process for Expediting ETL to the OMOP Common Data Model.
• Rost, Melissa Implementing Real-Time Patient Level Predictions Using PLP Models
• Azimi, Mohammad A JSON-Based Implementation of the OMOP Common Data Model Can Enable Scalable Low-Cost Analytics on Larger Datasets
• Choi, Myung - OMOP on FHIR for Data Analytic Application Platform
• Evans, Lee Broadsea API - OHDSI methods as a cloud service
• Abdul-Basser, Taha A Demonstration of Several Analytic Uses Cases with OHDSI Components Deployed on Google Cloud
• Feeney, Kristin Building Deep Learning Models with the OMOP CDM