FAIR Phenotyping with APHRODITE

Tracking #: 588-1568

Responsible editor: 

Paul Groth

Submission Type: 

Position Paper


Electronic phenotyping over the years has been evolving from simple to complex rule-based definitions, and more recently entering the machine learning age with probabilistic phenotype models. With the added complexity comes the additional need to have consistent and reproducible phenotype definitions for maintenance, replicability and community sharing. In this work we introduce how to construct probabilistic phenotype definitions with Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE) that follow the FAIR principles to improve their reproducibility and quality. By using a centralized repository and creating a standard list of meta-data elements, we aim to guide probabilistic phenotype definition developers with a FAIR-compatible standard. By developing this standard within the Observational Health Data Sciences (OHDSI) initiative, we aim to ensure community wide compatibility and maximum reproducibility.



  • Reviewed

Special issue (if applicable): 

FAIR Data, Systems and Analysis

Data repository URLs: 

Date of Submission: 

Sunday, June 16, 2019

Date of Decision: 

Monday, July 29, 2019



Solicited Reviews:

1 Comment

Meta-Review by Editor

Overall, the idea of sharing phenotype information in a FAIR way is a good one. However, it was unclear how the paper contributed beyond what the authors have already published. Furthermore, the implementation status of the actual Github based systems was unclear from the paper - what was planned and what actually has been implemenented?

Paul Groth (https://orcid.org/0000-0003-0183-6910)