A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data

Multimodality datasets are becoming increasingly common in various domains to provide complementary information for predictive analytics. In health care, diagnostic imaging of different kinds contains complementary information about an organ of interest, which allows for building a predictive model to accurately detect a certain disease. In manufacturing, multi-sensory datasets contain complementary information about the process and product, allowing for more accurate quality assessment. One significant challenge in fusing multimodality data for predictive analytics is that the multiple modalities are not universally available for all samples due to cost and accessibility constraints. This results in a unique data structure called Incomplete Multimodality Dataset (IMD) for which existing statistical models fall short. We propose a novel Incomplete-Multimodality Transfer Learning (IMTL) model that builds a predictive model for each sub-cohort of samples with the same missing modality pattern, and meanwhile couples the model estimation processes for different sub-cohorts to allow for transfer learning. We develop an Expectation-Maximization (EM) algorithm to estimate the parameters of IMTL and further extend it to a collaborative learning paradigm that is specifically valuable for patient privacy preservation in health care applications of the IMTL. We prove two advantageous properties of IMTL: the ability for out-of-sample prediction and a theoretical guarantee for a larger Fisher information compared with models without transfer learning. IMTL is applied to diagnosis and prognosis of the Alzheimer’s Disease (AD) at an early stage of the disease called Mild Cognitive Impairment (MCI) using incomplete multimodality imaging data. IMTL achieves higher accuracy than competing methods without transfer learning.