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

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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., Heß, S., Grum, M., 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, Factual Databases, Health Insurance, Risk Factors, Severity of Illness Index
Source:	Statistical Methods in Medical Research
ISSN:	0962-2802
Publisher:	Sage Publications
Volume:	29
Number:	12
Page Range:	3684-3694
Date:	1 December 2020
Official Publication:	https://doi.org/10.1177/0962280220938088
PubMed:	View item in PubMed

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