Title: | Probabilistically Identify Clusters in Electronic Medical Records |
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Description: | What the package does (one paragraph). |
Authors: | Michael DeWitt [aut, cre] , Cone Health [aut, cph] |
Maintainer: | Michael DeWitt <[email protected]> |
License: | MIT + file LICENSE |
Version: | 0.0.1 |
Built: | 2024-11-11 03:14:09 UTC |
Source: | https://github.com/conedatascience/autotracer |
Simulated Patient Data Synthetic data that can be used to explore "autotracer"
autotracer_linelist
autotracer_linelist
a data.frame with 10500 rows and 9 columns:
Latitude
Longitude
Race indentifier
Unique patient indentifier (e.g. MRN)
Age of the patient in years
Biological sex of the patient
Primary spoken language of the patient
Age given as a ten year age group
Date of test or infection identification
Probabilistically estimates a likely tranmission chain using EMR derrived data.
connect_probable_cases( dat, weights_in = NULL, threshold = 30, exposure_link = NULL )
connect_probable_cases( dat, weights_in = NULL, threshold = 30, exposure_link = NULL )
dat |
the dataframe of likely connect cases with a column named "date" indicating the onset or positive test date and "patient_id", a unique identifier for the record. |
weights_in |
the weights to use for the serial interval if available |
threshold |
integer, the threshold in days at which to discard a connected case (e.g. >30 days from previous case, then discard). |
exposure_link |
string or column name, how these cases are connected |