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Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease

Item Type:Article
Title:Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease
Creators Name:Ploner, T. and Heß, S. and Grum, M. and Drewe-Boss, P. and Walker, J.
Abstract:OBJECTIVE: We propose a data-driven method to detect temporal patterns of disease progression in high-dimensional claims data based on gradient boosting with stability selection. MATERIALS AND METHODS: We identified patients with chronic obstructive pulmonary disease in a German health insurance claims database with 6.5 million individuals and divided them into a group of patients with the highest disease severity and a group of control patients with lower severity. We then used gradient boosting with stability selection to determine variables correlating with a chronic obstructive pulmonary disease diagnosis of highest severity and subsequently model the temporal progression of the disease using the selected variables. RESULTS: We identified a network of 20 diagnoses (e.g. respiratory failure), medications (e.g. anticholinergic drugs) and procedures associated with a subsequent chronic obstructive pulmonary disease diagnosis of highest severity. Furthermore, the network successfully captured temporal patterns, such as disease progressions from lower to higher severity grades. DISCUSSION: The temporal trajectories identified by our data-driven approach are compatible with existing knowledge about chronic obstructive pulmonary disease showing that the method can reliably select relevant variables in a high-dimensional context. CONCLUSION: We provide a generalizable approach for the automatic detection of disease trajectories in claims data. This could help to diagnose diseases early, identify unknown risk factors and optimize treatment plans.
Keywords:Gradient Boosting, Stability Selection, Claims Data, Disease Trajectory, Chronic Obstructive Pulmonary Disease
Source:Statistical Methods in Medical Research
ISSN:0962-2802
Publisher:Sage Publications
Date:10 July 2020
Official Publication:https://doi.org/10.1177/0962280220938088
PubMed:View item in PubMed

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