no code implementations • 21 Jul 2023 • Aya Nakamura, Ryosuke Kojima, Yuji Okamoto, Eiichiro Uchino, Yohei Mineharu, Yohei Harada, Mayumi Kamada, Manabu Muto, Motoko Yanagita, Yasushi Okuno
This framework enables learning, visualizing, and clustering of temporal changes in patient latent states related to disease progression.
no code implementations • 31 May 2022 • Kazuki Nakamura, Eiichiro Uchino, Noriaki Sato, Ayano Araki, Kei Terayama, Ryosuke Kojima, Koichi Murashita, Ken Itoh, Tatsuya Mikami, Yoshinori Tamada, Yasushi Okuno
Here, we present the health-disease phase diagram (HDPD), which represents a personal health state by visualizing the boundary values of multiple biomarkers that fluctuate early in the disease progression process.
no code implementations • 30 Oct 2020 • Kazuki Nakamura, Ryosuke Kojima, Eiichiro Uchino, Koichi Murashita, Ken Itoh, Shigeyuki Nakaji, Yasushi Okuno
A key point of the framework is the evaluation of the "actionability" for personal health improvements by using a surrogate Bayesian model in addition to a high-performance nonlinear ML model.