Poster: Intelligent fuzzifier-based cluster validation for incomplete longitudinal digital trial data

Digital technology has huge potentials in transforming clinical trial research. One common issue in digital clinical trials for long-term behavioral treatments is incomplete longitudinal data, as subjects’ behavior changes over time. In this paper, we aim to improve the fuzzy clustering accuracy and stability of digital clinical trials by intelligently searching for the optimal fuzzifier, which is the key to identify the optimal number of overlapped clusters for incomplete longitudinal data. Our findings showed that integrating optimal fuzzifier searching with cluster validation can streamline the clustering process, thus enabling the intelligent fuzzy clustering procedure.