Key result
Ensemble machine learning correctly classifies ~96% of patients, outperforming individual algorithms alone.
Why the study?
Variability in patient treatment responses to pregabalin for painful diabetic peripheral neuropathy limits effective care and improved prediction methods are needed.
Does an ensemble machine learning method improve the prediction of pregabalin treatment response in patients with painful diabetic peripheral neuropathy compared to individual methods?
Does an ensemble machine learning method improve the prediction of pregabalin treatment response in patients with painful diabetic peripheral neuropathy compared to individual methods?
Absolute Event Rate: 95.9% vs 56.7%
An ensemble machine learning approach combining multiple instance-based techniques accurately classifies patients to predict treatment response to pregabalin in painful diabetic peripheral neuropathy.
PURPOSE: Variability in patient treatment responses can be a barrier to effective care. Utilization of available patient databases may improve the prediction of treatment responses. We evaluated machine learning methods to predict novel, individual patient responses to pregabalin for painful diabetic peripheral neuropathy, utilizing an agent-based modeling and simulation platform that integrates real-world observational study (OS) data and randomized clinical trial (RCT) data. PATIENTS AND METHODS: The best supervised machine learning methods were selected (through literature review) and combined in a novel way for aligning patients with relevant subgroups that best enable prediction of pregabalin responses. Data were derived from a German OS of pregabalin (N=2642) and nine international RCTs (N=1320). Coarsened exact matching of OS and RCT patients was used and a hierarchical cluster analysis was implemented. We tested which machine learning methods would best align candidate patients with specific clusters that predict their pain scores over time. Cluster alignments would trigger assignments of cluster-specific time-series regressions with lagged variables as inputs in order to simulate "virtual" patients and generate 1000 trajectory variations for given novel patients. RESULTS: Instance-based machine learning methods (k-nearest neighbor, supervised fuzzy c-means) were selected for quantitative analyses. Each method alone correctly classified 56.7% and 39.1% of patients, respectively. An "ensemble method" (combining both methods) correctly classified 98.4% and 95.9% of patients in the training and testing datasets, respectively. CONCLUSION: An ensemble combination of two instance-based machine learning techniques best accommodated different data types (dichotomous, categorical, continuous) and performed better than either technique alone in assigning novel patients to subgroups for predicting treatment outcomes using microsimulation. Assignment of novel patients to a cluster of similar patients has the potential to improve prediction of patient outcomes for chronic conditions in which initial treatment response can be incorporated using microsimulation. CLINICAL TRIAL REGISTRIES: www.clinicaltrials.gov: NCT00156078, NCT00159679, NCT00143156, NCT00553475.
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Alexander et al. (2019) studied painful diabetic peripheral neuropathy (n=3,962). Ensemble machine learning method vs. Individual machine learning methods (k-nearest neighbor, supervised fuzzy c-means) was evaluated on Correct classification of patients. An ensemble machine learning method correctly classified 95.9% of patients in the testing dataset, performing better than k-nearest neighbor (56.7%) or supervised fuzzy c-means (39.1%) alone.
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