Building & Training a Risk Model

Risk Model Overview

A risk model is a type of machine learning model that predicts a “risk” score for a given outcome. Outcomes can be of type occurrence or regression.

Creating a Risk Model

  1. Navigate to the folder where you want your model to be stored
  2. Click the “+” icon located to the right of “Models”
  3. The following box will pop-up:
  • Type: Select "Risk"
  • Define Model and Dataset
    Build in Curia App: Select if you want to define an outcome, intervention, cohort, and time period in the app (from which Curia will generate the model population and features)
    Upload pre-compiled data: If you have a completely custom training dataset (including features) that you wish to use
  1. Enter a name for your model
  2. (Optional) Write a description of your model
  3. Click the "Create Model" button, which will open a new page
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Building in the Curia App

Outcome and Intervention

You will first be prompted to select an Outcome and Intervention

Outcome

  1. Select Outcome
    – The Point & Click workflow allows you to define diagnosis, procedure, .... outcomes by selecting specific codes
    – To model a custom outcome, select “Custom Outcome” and choose the outcome dataset you wish to use.

  2. Select Outcome Type:
    – To predict the likelihood a given binary event occurs, select Occurrence
    – To predict a continuous outcome, select Regression
    -- After selecting Regression, you must choose an aggregation type.
    -- Note that all aggregation types other than "count" will use event cost as the value to aggregate

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Generate Cohorts

  1. Set Evidence: The period of time that the covariates (features) for training are built on. This is always 12 months.

  2. Set Delays:

  • For a risk model, data delay + pre-outcome delay are added together to produce a total delay indicating the gap between the end of the evidence period and the beginning of an outcome measurement.
  • Data Delay is used to indicate any delays related to data availability; e.g. a 90 day waiting period for claims to be processed would result in a 3 month data delay.
  • Pre-outcome delay: The period of time to wait before measuring an outcome. E.g. if a known treatment such as a medication takes 1 month to produce any results, the pre-outcome delay should be 1 month.
  1. Outcome: The period of time that information is aggregated over to generate the modeling outcomes

Window

Rolling (Multiple Cohorts): Select if you wish to create more data from a rolling window of several different incremental time periods. Then select the date when the evidence period starts and the date you want your outcome period to end across your windows.

Fixed (Single Cohort): If you’re certain about your exact start and end dates for the evidence and outcome periods.

Require full outcome period data for individuals

– It is often the case that some individuals will die before the end of the outcome period (or switch to a new healthcare organization), meaning that if we are modeling on the occurrence of a specific code, this individual could have had this code, but we ran out of information on them since they aren’t in our dataset anymore.

– Checking this option counteracts the above by ensuring only individuals with data after the outcome period ends are included

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Population

New Code Filter: Add a filter that either only includes or excludes patients with specific code data in our modeling analysis

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New Demographic Filter: add a filter that either only includes or excludes patients that share some specific demographic information in our modeling analysis

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Select Dataset: This option allows you to define the cohort using a dataset. To do this, the cohort dataset must already have been uploaded to the platform. For information on this, see the Datasets Guide

  1. Once you have configured all of these elements, click "Preview Model Data" to run queries that generate the relevant dataset and output summary statistics

  2. Hit "Train Model"