Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study
Takenori Inomata, Masahiro Nakamura, Jaemyoung Sung, Akie Midorikawa‐Inomata, Masao Iwagami, Kenta Fujio, Yasutsugu Akasaki, Yuichi Okumura, Keiichi Fujimoto, Atsuko Eguchi, Maria Miura, Ken Nagino, Hurramhon Shokirova, Jun Zhu, Mizu Kuwahara, Kunihiko Hirosawa, Reza Dana, Akira Murakami
Abstract
Multidimensional integrative data analysis of digital phenotyping is crucial for elucidating the pathologies of multifactorial and heterogeneous diseases, such as the dry eye (DE). This crowdsourced cross-sectional study explored a novel smartphone-based digital phenotyping strategy to stratify and visualize the heterogenous DE symptoms into distinct subgroups. Multidimensional integrative data were collected from 3,593 participants between November 2016 and September 2019. Dimension reduction via Uniform Manifold Approximation and Projection stratified the collected data into seven clusters of symptomatic DE. Symptom profiles and risk factors in each cluster were identified by hierarchical heatmaps and multivariate logistic regressions. Stratified DE subgroups were visualized by chord diagrams, co-occurrence networks, and Circos plot analyses to improve interpretability. Maximum blink interval was reduced in clusters 1, 2, and 5 compared to non-symptomatic DE. Clusters 1 and 5 had severe DE symptoms. A data-driven multidimensional analysis with digital phenotyping may establish predictive, preventive, personalized, and participatory medicine.