Dealing with Data Scarcity in Rare Diseases: Dynamic Bayesian Networks and Transfer Learning to Develop Prognostic Models of Amyotrophic Lateral Sclerosis

Abstract

The extremely low prevalence of rare diseases exacerbates many of the typical challenges to prognostic model development, resulting, at the same time, in low data availability and difficulties in procuring additional data due to, e.g., privacy concerns over the risk of patient reidentification. Yet, developing prognostic models with possibly limited in-house data is often of interest for many applications (e.g., prototyping, hypothesis confirmation, exploratory analyses).

Several options exist beyond simply training a model with the available data: data from a larger database might be acquired; or, lacking that, to sidestep limitations to data sharing, one might resort to simulators based, e.g., on dynamic Bayesian networks (DBNs). Additionally, transfer learning techniques might be applied to integrate external and in-house data sources.

Here, we compare the effectiveness of these strategies in developing a predictive model of 3-year mortality in amyotrophic lateral sclerosis (ALS, a rare neurodegenerative disease with <0.01% prevalence) using the in-house dataset of a single ALS clinic in Milan, Italy (N = 116). We test several combinations of direct and transfer-learning-mediated development based on additional real data from the Italian PARALS register (N = 568). We also train two DBNs, one for each dataset, and use them to simulate large numbers of virtual subjects whose variables are linked by the same probabilistic relationships as in the real data.

We show that, compared to a baseline model developed on the smaller dataset (AUROC = 0.633), the largest performance increase was obtained using data simulated using a DBN trained on the larger PARALS register (AUROC = 0.734).