Identify Event Sequences Using Time Series Joins

Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows.


BuildStatus Windows buildstatus

Overview

Constellation contains a set of functions for applying multidimensional, time window based logic to time series data frames of arbitrary length. Constellation was developed to enable rapid and flexible identification of series of events that occur in hospitalized patients. The functions have been abstracted for general purpose use with time series data. Constellation extends and provides a friendly API to rolling joins and overlap joins implemented in data.table. Three datasets (labs, vitals, and orders) with randomly synthesized time series data for a cohort of 100 patients are included to facilitate testing of functions.

There are five functions included in constellation to build complex features from time series data:

  • value_change() identify increases or decreases in a value within a given time window
  • constellate() identify time stamps when a series of events occurs within a given time window
  • constellate_criteria() identify which events occur within a given time window for every measurement time stamp
  • bundle() identify which events occur within a given time window of a given event
  • incidents() identify distinct, incident episodes that must be separated in time by a minimum of a given time window

The constellate_criteria() and bundle() function are similar, but the bundle() function is anchored around a specific event table. The bundle() function identifies events that occur within a given time window of a specific event data frame that is supplied to the function. On the other hand, the constellate_criteria() function identifies events that occur within a given time window of any event data frame that is supplied to the function. The first data frame passed to the bundle() function is used as an anchor to search through the subsequent data frames passed to the function. The order of data frames is significant and passing different data frames as the first argument will generate different results. On the other hand, the order in which you pass data frames to the constellate_criteria() function is insignificant. Passing data frames in different orders will generate equivalent results.

Constellation can be used to build point-based scores for time series data (via constellate_criteria()), identify particular sequences of events that occur near each other (via constellate()), identify when specific changes occur for a given parameter (via value_change()), identify individual events that occur around a specified time stamp (via bundle()), and distinguish between eveents that are separated by a specified time window (via incidents()).

If you are new to constellation, the best place to start is the vignette("constellation", "identify_sepsis"). You can also view the sepsis vignette on CRAN.

Installation

You can install constellation from CRAN with:

install.packages("constellation")
library(constellation)

You can install the development version of constellation from github with:

devtools::install_github("marksendak/constellation")

If you have any questions, comments, or feedback, please email [email protected].

Example

Below are several variations of finding systolic blood pressure drops of 40 over a 6 hour period.

Examine systolic blood pressure data:

library(constellation)
 
systolic_bp <- vitals[VARIABLE == "SYSTOLIC_BP"]
systolic_bp[, RECORDED_TIME := as.POSIXct(RECORDED_TIME, format = "%Y-%m-%dT%H:%M:%SZ", tz = "UTC")]
head(systolic_bp)
#>    PAT_ID       RECORDED_TIME    VALUE    VARIABLE
#> 1: 108546 2010-02-25 05:36:15 110.6677 SYSTOLIC_BP
#> 2: 108546 2010-02-25 08:41:56 116.0423 SYSTOLIC_BP
#> 3: 108546 2010-02-25 10:30:53 119.2235 SYSTOLIC_BP
#> 4: 108546 2010-02-25 11:05:43 102.9899 SYSTOLIC_BP
#> 5: 108546 2010-02-25 11:48:29 122.1348 SYSTOLIC_BP
#> 6: 108546 2010-02-25 12:14:18 119.7529 SYSTOLIC_BP

Identify the first systolic blood pressure drop per patient:

systolic_bp_drop <- value_change(systolic_bp, value = 40, direction = "down",
    window_hours = 6, join_key = "PAT_ID", time_var = "RECORDED_TIME", 
    value_var = "VALUE", mult = "first")
head(systolic_bp_drop)
#>    PAT_ID PRIOR_RECORDED_TIME PRIOR_VALUE CURRENT_RECORDED_TIME
#> 1: 108546 2010-02-25 15:45:29    139.9967   2010-02-25 20:42:35
#> 2: 112374 2010-03-09 18:18:13    160.4919   2010-03-09 20:48:09
#> 3: 113163 2010-07-27 15:50:35    170.2034   2010-07-27 19:21:58
#> 4: 124042 2010-11-24 21:34:57    163.8912   2010-11-25 02:03:14
#> 5: 135995 2010-11-21 01:51:09    157.9432   2010-11-21 03:26:00
#> 6: 146478 2010-08-27 16:07:47    179.3603   2010-08-27 21:03:05
#>    CURRENT_VALUE
#> 1:      80.07446
#> 2:     107.87212
#> 3:     116.22419
#> 4:     116.66625
#> 5:     111.55469
#> 6:     132.99234

Identify the last systolic blood pressure drop per patient:

systolic_bp_drop <- value_change(systolic_bp, value = 40, direction = "down",
    window_hours = 6, join_key = "PAT_ID", time_var = "RECORDED_TIME", 
    value_var = "VALUE", mult = "last")
head(systolic_bp_drop)
#>    PAT_ID PRIOR_RECORDED_TIME PRIOR_VALUE CURRENT_RECORDED_TIME
#> 1: 108546 2010-07-01 15:31:31    164.9851   2010-07-01 21:03:04
#> 2: 112374 2010-03-15 15:15:53    164.1634   2010-03-15 17:30:06
#> 3: 113163 2010-07-30 19:12:15    160.1682   2010-07-30 22:33:10
#> 4: 124042 2010-12-04 18:34:18    167.2564   2010-12-04 22:46:57
#> 5: 135995 2010-11-27 04:47:15    127.5603   2010-11-27 06:43:05
#> 6: 146478 2010-09-03 15:14:43    182.1690   2010-09-03 16:18:28
#>    CURRENT_VALUE
#> 1:     114.67968
#> 2:     115.95783
#> 3:     111.89387
#> 4:     118.81151
#> 5:      81.90537
#> 6:     138.28222

Identify all systolic blood pressure drops per patient:

systolic_bp_drop <- value_change(systolic_bp, value = 40, direction = "down",
    window_hours = 6, join_key = "PAT_ID", time_var = "RECORDED_TIME", 
    value_var = "VALUE", mult = "all")
head(systolic_bp_drop)
#>    PAT_ID PRIOR_RECORDED_TIME PRIOR_VALUE CURRENT_RECORDED_TIME
#> 1: 108546 2010-02-25 15:45:29    139.9967   2010-02-25 20:42:35
#> 2: 108546 2010-03-01 15:57:24    136.8654   2010-03-01 16:07:00
#> 3: 108546 2010-03-02 19:59:20    129.0167   2010-03-03 00:46:35
#> 4: 108546 2010-03-02 20:49:00    110.1830   2010-03-03 00:46:35
#> 5: 108546 2010-03-04 00:18:41    137.8095   2010-03-04 04:23:54
#> 6: 108546 2010-03-04 02:13:39    130.3280   2010-03-04 04:23:54
#>    CURRENT_VALUE
#> 1:      80.07446
#> 2:      88.88972
#> 3:      69.94551
#> 4:      69.94551
#> 5:      82.16874
#> 6:      82.16874

Why constellation?

In clinical medicine, there are a subset of conditions that are defined by a sequence of related events that unfold over time. These conditions are described as a “constellation of signs and symptoms.”

Another piece of medical jargon that made it into the package is the concept of a treatment bundle. The bundle() function was originally designed to calculate the time stamp at which a group of treatments is delivered for every patient within a specified amount of time of developing a condition.

Duke Institute for Health Innovation

constellation was originally developed to support a machine learning project at the Duke Institute for Health Innovation to predict sepsis.

News

Reference manual

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install.packages("constellation")

0.2.0 by Mark Sendak, a year ago


https://github.com/marksendak/constellation


Browse source code at https://github.com/cran/constellation


Authors: Mark Sendak [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports data.table

Suggests knitr, rmarkdown, ggplot2, devtools, testthat


See at CRAN