https://github.com/ohdsi-studies/deadmodel
A package for implementing the death imputation model on OMOP CDM data
https://github.com/ohdsi-studies/deadmodel
death patient-level-prediction
Last synced: 2 months ago
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A package for implementing the death imputation model on OMOP CDM data
- Host: GitHub
- URL: https://github.com/ohdsi-studies/deadmodel
- Owner: ohdsi-studies
- Created: 2020-04-08T03:35:11.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-12-03T15:48:12.000Z (6 months ago)
- Last Synced: 2024-12-03T16:38:58.871Z (6 months ago)
- Topics: death, patient-level-prediction
- Language: R
- Size: 18.2 MB
- Stars: 2
- Watchers: 7
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data
=============
- Analytics use case(s): **Patient-Level Prediction**
- Study type: **Clinical Application**
- Tags: **-**
- Study lead: **Jenna Reps**
- Study lead forums tag: **[jreps](https://forums.ohdsi.org/u/jreps)**
- Study start date: **Jan 1, 2018**
- Study end date: **April 1, 2018**
- Protocol: **-**
- Publications: **[Paper](https://link.springer.com/article/10.1007/s40264-019-00827-0)**
- Results explorer: **[Shiny App](http://data.ohdsi.org/DeadImputation/)**
This package contains the DEAD risk model - using the last 365 days what is the risk that the patient with an end of observation is dead?
Features
========
- code to validate the death model on data with death status recorded
- code to create a death risk covariate
- code to predict the current alive or dead status
Technology
==========
DeadModel is an R package.
System Requirements
===================
Requires R (version 3.3.0 or higher).
Dependencies
============
* PatientLevelPrediction
Getting Started
===============
1. In R, use the following commands to download and install:
```r
install.packages("devtools")
# If you dont have PatientLevelPrediction installed uncomment the 2 lines below and run:
#devtools::install_github("OHDSI/FeatureExtraction")
#devtools::install_github("OHDSI/PatientLevelPrediction")
# When you have PatientLevelPrediction installed run:
devtools::install_github("ohdsi-studies/DeadModel")
library(DeadModel)
#==============
# EXPLORE
#==============
# To view the model coefficients:
viewDeadCoefficients()
#==============
# APPLY
#==============
# INPUTS:
dbms <- "pdw"
user <- NULL
pw <- NULL
server <- Sys.getenv('server')
port <- Sys.getenv('port')
connectionDetails <- DatabaseConnector::createConnectionDetails(dbms = dbms,
server = server,
user = user,
password = pw,
port = port)
outputLocation <- file.path(getwd(),'Death Risk')
cdmDatabaseSchema <- 'CDM database schema'
cohortDatabaseSchema <- 'cohort database schema'
cohortTable <- 'cohortTable containing people who you want to predict risk of being dead'
cohortId <- 'cohortDefinitionId for target cohort people in cohortTable'
outcomeId <- '(if externally validating model) cohortDefinitionId for dead people in cohortTable'
# code to do prediction for each patient in the cohortTable with cohort_definition_id 1
prediction <- applyDeadModel(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDatabaseSchema,
cdmDatabaseName = 'database name',
cohortDatabaseSchema = cohortDatabaseSchema,
tempEmulationSchema = NULL,
cohortTable = cohortTable,
cohortId=cohortId)
# code to externall validate the model
validation <- validateDeadModel(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDatabaseSchema,
cdmDatabaseName = 'database name',
cohortDatabaseSchema = cohortDatabaseSchema,
tempEmulationSchema = NULL,
cohortTable = cohortTable,
targetId = cohortId,
outcomeId = outcomeId)
```
License
=======
DeadModel is licensed under Apache License 2.0
Development
===========
DeadModel is being developed in R Studio.