12 citations · 12 across the 5 of their papers we have counts for
5 papers
A Nationwide Japanese Medical Claims Foundation Model: Balancing Model Scaling and Task-Specific Computational Efficiency
Nanae Aratake, Taisei Tosaki, Yuji Okamoto +5
Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervised foundation models have emerged as a promising approach for leveraging large-s…
Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction
Taisei Tosaki, Eiichiro Uchino, Ryosuke Kojima +9
Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for use is often hindered by datase…
A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data
Aya Nakamura, Ryosuke Kojima, Yuji Okamoto +7
Many diseases, including cancer and chronic conditions, require extended treatment periods and long-term strategies. Machine learning and AI research focusing on electronic health…
Individual health-disease phase diagrams for disease prevention based on machine learning
Kazuki Nakamura, Eiichiro Uchino, Noriaki Sato +8
Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturi…
Health improvement framework for planning actionable treatment process using surrogate Bayesian model
Kazuki Nakamura, Ryosuke Kojima, Eiichiro Uchino +4
Clinical decision making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagno…