A quick primer on what models are and how to use them to capture patient and sample data in your biobank
Using data models helps you keep records organized, logically separated, and manageable. In this article, we will establish what data models are, how to best use them, and an example of data models in biobank scenarios.
Simply put, a data model is a categorization technique. In the BioBox systems, models are a collection of conditions related to the properties of the data record. Taken together, these conditions creates the qualifiers that a record must have to be considered part of that model.
Creating Data Model Conditions
Using any of your dictionary terms, you can set up a condition within a model. Depending on the type of dictionary term, there may be different operators you can use.
Results of applying conditions
To illustrate the effect of these conditions, consider a hypothetical data model named "High Grade Glioma" that uses the following conditions:
|WHO Grade||Greater than||3|
When applied to the following records, the membership results as follows:
|Record Name||Diagnosis||WHO Grade||Included/Excluded|
Records can belong to zero, one, or more models
The simplest version of a biobank only needs two models; Patient and Sample.
Both of these models relies only on a single dictionary term: Record Class. The outcome of these two models, given the following records are:
|Record Name||Record Class||Model Membership|