Abstract
BackgroundThe goal of our study is to examine the impact of the lookback length when engineering features to use in developing predictive models using observational healthcare data. Using a longer lookback for feature engineering gives more insight about patients but increases the issue of left-censoring.MethodsWe used five US observational databases to develop patient-level prediction models. A target cohort of subjects with hypertensive drug exposures and outcome cohorts of subjects with acute (stroke and gastrointestinal bleeding) and chronic outcomes (diabetes and chronic kidney disease) were developed. Candidate predictors that exist on or prior to the target index date were derived within the following lookback periods: 14, 30, 90, 180, 365, 730, and all days prior to index were evaluated. We predicted the risk of outcomes occurring 1 day until 365 days after index. Ten lasso logistic models for each lookback period were generated to create a distribution of area under the curve (AUC) metrics to evaluate the discriminative performance of the models. Calibration intercept and slope were also calculated. Impact on external validation performance was investigated across five databases.ResultsThe maximum differences in AUCs for the models developed using different lookback periods within a database was < 0.04 for diabetes (in MDCR AUC of 0.593 with 14-day lookback vs. AUC of 0.631 with all-time lookback) and 0.012 for renal impairment (in MDCR AUC of 0.675 with 30-day lookback vs. AUC of 0.687 with 365-day lookback ). For the acute outcomes, the max difference in AUC across lookbacks within a database was 0.015 (in MDCD AUC of 0.767 with 14-day lookback vs. AUC 0.782 with 365-day lookback) for stroke and < 0.03 for gastrointestinal bleeding (in CCAE AUC of 0.631 with 14-day lookback vs. AUC of 0.660 with 730-day lookback).ConclusionsIn general the choice of covariate lookback had only a small impact on discrimination and calibration, with a short lookback (< 180 days) occasionally decreasing discrimination. Based on the results, if training a logistic regression model for prediction then using covariates with a 365 day lookback appear to be a good tradeoff between performance and interpretation.
Highlights
The goal of our study is to examine the impact of the lookback length when engineering features to use in developing predictive models using observational healthcare data
This study examined model performance over seven lookback periods (14, 30, 90, 180, 365, 730, and all time prior to index) for two chronic and two acute outcomes in subjects newly treated with hypertensive medications across five US databases
There was generally a positive correlation between covariate lookback and area under the curve (AUC), but differences were observed across outcomes
Summary
The goal of our study is to examine the impact of the lookback length when engineering features to use in developing predictive models using observational healthcare data. There are difficulties when developing models using temporal data from healthcare claims and electronic healthcare record databases as the data come from a diversity of sources and are recorded at irregular frequencies with data often sparsely represented. This can present issues to classifiers such as neural networks when implementing the feature engineering [1], especially if the data are not large. In this paper we focus on engineering non-temporal features
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