An R package for clustering cohort patients by medical history or creating data-driven medical topics (clusters of concept_ids corresponding to the same medical topic).
There is also a javascript D3 interactive visulisation available for viewing the results.
- Aids the creation of topics (sets of similar concept ids)
- Takes a cohort as input and feature definitions (set of individual concept_ids or topics).
- Extracts the necessary data from a database in OMOP Common Data Model format.
- Performs kmeans, generalised low rank models or concensus clustering
- Includes functions for evaluating clusters and exporting into JSON format
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| Example Javascript Plot |
patientCluster is an R package, with some functions implemented using h2o (http://h2o-release.s3.amazonaws.com/h2o/rel-lambert/5/docs-website/Ruser/Rinstall.html).
Requires R (version ? or higher). Installation on Windows requires RTools. Libraries used in patientCluster require Java.
- h2o
- DatabaseConnector
- SqlRender
- On Windows, make sure RTools is installed.
- The DatabaseConnector, h2o and SqlRender packages require Java. Java can be downloaded from http://www.java.com.
- Install h2o as describe here: http://h2o-release.s3.amazonaws.com/h2o/rel-lambert/5/docs-website/Ruser/Rinstall.html
- In R, use the following commands to download and install patientCluster:
install.packages("devtools")
library(devtools)
install_github("ohdsi/SqlRender")
install_github("ohdsi/DatabaseConnector")
install_github("ohdsi/patientCluster")
library("patientCluster")
library("h2o")
h2o.init(nthreads = -1, max_mem_size = '16g')- Vignette: Cluster examples
- Package manual: patientCluster.pdf
- Developer questions/comments/feedback: OHDSI Forum
- We use the GitHub issue tracker for all bugs/issues/enhancements
patientCluster is licensed under Apache License 2.0
patientCluster is being developed in R Studio.
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