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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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