Data Science in Healthcare
Methodds provides expertise in applied Data Science methods for healthcare. We use state-of-the-art machine learning approaches as well as traditional statistical methods to help organizations unlock the full potential of their data.
Case Example
Type 2 Diabetes Mellitus (T2DM) affects about 10% of the population and its prevalence is expected to increase in the next 50 years due to a increased sedentary lifestyle as well as life expectancy. Nonetheless, disease progression is still poorly understood and little is known about the different conditions that patients develop over the course of the disease.
Therefore, in order to understand the onset of new comorbidities in patients with T2DM, we used a novel Bayesian nonparametric model to find the latent structure of the disease profile of patients from a nation-wide Real World Database. This model identified very clear clusters and distinct disease trajectories with many differences in the characteristics of the patients within each cluster.
This work is published as pre-print form in:
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