Structural Degradation Modeling Framework for Sparse Data Sets With an Application on Alzheimer’s Disease.

The rapid development of information technologies provided unprecedented big data environments for condition monitoring and degradation analyses. However, the available big data sets are often sparse with a limited number of observations per recorded unit. For example, in many healthcare systems, data are collected from a large number of patients, but the available observations from each patient are quite limited. Unfortunately, most of the existing approaches for data-driven degradation modeling may not work well in this scenario as they either pool the information from the population or require rich historical observations in each unit. To address the challenges in “sparse data environments,” this paper proposes a structural degradation modeling framework (SDM). The SDM is inspired by the recommender system, which provides recommendations about specific items for the user. In addition, it is also tailored to the needs of degradation modeling. In particular, the framework takes into consideration: 1) the available data from the unit of interest; 2) the population characteristics; 3) the relationship between the available units; and 4) the precision of the available units. Simulation studies and a case study that involves the Alzheimer’s disease (AD) neuroimaging initiative data set are conducted, which shows satisfactory performance of the proposed method. Note to Practitioners—This paper proposes a framework for modeling and predicting the degradation level and/or condition of units with time. Our framework is particularly useful where many units have missing and/or limited degradation observations. Essentially, our proposed method integrates two important ideas: 1) leveraging the available data from the unit of interest to improve the modeling fitting of the individual unit over the observed time domain and 2) considering the relationship between the available units to extract proper and accurate population characteristics to address the challenge of limited observations. The proposed approach is validated via simulation studies as well as a healthcare case study based on AD. In the future research, we will further explore the extension of the proposed method such as considering more generic degradation models and optimal parameter tunings. [ABSTRACT FROM AUTHOR]