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README.md

comorbidity-miner DOI

This repository contains the scripts necessary to run an R Shiny app that mines healthcare data for association rules and sequential patterns and allows exploratory analysis by healthcare workers and researchers using different concept heirarchies developed by the biomedical informatics community. The app currently supports MIMIC-III and AEOLUS data as well as ICD9-CM codes, CCS single- and multi-level categories, and SNOMED-CT concepts, but it can handle any patient-level data. CDC National Vital Statistics and ICD10-CM codes and mappings will be integrated in the near future.

Accessing the Data

Some of the publicly available data are provided, while restricted data and very large data will need to be downloaded. The app is data-input-agnostic, meaning that data can be loaded from CSV files or from a database connection. Supported databases include SQL, mySQL, PostgreSQL, and sqlite.

MIMIC-III

MIMIC contains de-identified hospital records and requires researchers to complete a CITI training course and formally request access to the database. Instructions to access MIMIC can be found here.

AEOLUS

AEOLUS is normalized FDA Adverse Event Reporting data and is to be used for public health research. AEOLUS CSV files and database loading instructions can be found here.

Mapping files

The HCUP files that map ICD9-CM codes to CCS categories are provided in the data/icd_grouping directory. More information can be found here.

Download the files that map ICD9-CM codes to SNOMED-CT concepts here.

Running the app

The raw data are read into memory in Global.R, massaged in preprocessing scripts customized to each data source, and then fed through the rest of the R Shiny pipeline. If reading in data from CSVs, make sure the MIMIC data sit in data/mimic_tables, AEOLUS data sit in data/aeolus_v1, and the SNOMED-CT mapping files sit in data/icd_grouping. If connecting to databases, update the authentication information in Global.R:

  # mimic database
  mimic <- src_mysql(dbname = 'mimic', host = 'localhost', user = 'mimic_user', password = 'mimic')
  
  # aeolus database
  aeolus_db <- src_mysql(dbname = 'aeolus', host = 'host', user = 'aeolus_user', password = 'aeolus')

Once the data are loaded, run the preprocessing scripts, which will create a temporal and association TXT file for each of your data sources in data/input.

Initialize the app by clicking "Run App" in the top right corner of the editor panel in R Studio. It will take a few minutes to launch in your browser.

Using the app

Navigate the app using the tabs in the sidebar. Adjust the Settings to explore a new cohort and make sure to Write Cohort to File before moving on to other tabs. Below, we have selected patients from the MIMIC-III database who have been diagnosed with anxiety and mood disorders and who have multiple hospital visits on record. These settings generate a subset of patients whose charts show at least one ICD9 code from CCS Multi-level 2 category 5.2 (Anxiety Disorders) and at least one ICD9 code from CCS Multi-level 2 category 5.8 (Mood Disorders). The sequences/associations granularity dropdown changes the semantic groupings used for data mining and output. Here, CCS Multi-level 2 has been selected, which will result in more general sequences and association rules than ICD9-CM codes, but less general than CCS Multi-level 1 or ICD9-Chapter groupings.

ScreenShot

  • A demo with simulated data will be coming in the near future.
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