These documents describe the libraries and applications
These include the following:
fact-modelsdirectory contains tools for defining data models as Dhall types as well as Rust and Haskell libraries with tools for working with these data models in other applications.
A Dhall package containing types and utilities for working with codelists.
See TODO: link to docs.
To understand the data models, it may help to have an intuition of how the data models fit into the event data theory. Though the theory may have application in many scientific domains, the origin of the event data theory is rooted in epidemiology study design. Time is an essential element in all epidemiological study designs. Often a study defines an index: the time from which a study subject is observed for outcomes of interest. An event contains on two bits of information: what occurred and when that what occurred. For example, in order to determine whether a subject should be flagged as having a covariate of diabetes diagnosis (the feature), we need to do know that a subject (a) was diagnosed with diabetes and (b) that the diagnosis occurred before the study’s index time.
Many schemas for health data standardize
different types of what occurred into a relational database.
You can think of each model in
as a database schema.
The event data theory works for any model.
In essence, an event is simply an interval of time paired with a context,
where the context contains information about what occurred in the interval.
Specifically, a context carries facts about what occurred,
concepts (or tags) based on the facts that tell us what the event means,
and may also carry information about origin of the event from a source dataset.
The models defined in
fact-models can be used within an event’s context.