Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles
Instead of predictive modeling using costs, this is the right approach from a clinical point of view:
This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles.
And the figures below reflect these results.
Great article.
Figure 1. Seven Patient Profiles Derived From Latent Class Analysis
Figure 2. Comparison of k-Means Clustering With Latent Class Analysis (LCA)
Table 1. Baseline and 1-Year Follow-up Characteristics of the Overall Population and by Patient Profile
