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Author

Nagisa Shiomi

Institution: NTT (Japan)
ORCID: 0009-0001-8885-3096
Observed ORCID: 0009-0001-8885-3096
OpenAlex ID: A5043496451
Predicting rapid decline in kidney function among type 2 diabetes patients: A machine learning approach
2024 · Eri Nakahara, Kayo Waki, et al. · Heliyon · 3
Identifying factors associated with locomotive syndrome using machine learning methods: The third survey of the research on osteoarthritis/osteoporosis against disability study
2024 · Eri Nakahara, Toshiko Iidaka, et al. · Geriatrics and gerontology international/Geriatrics & gerontology international · 3
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Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development
2025 · Hisashi Kurasawa, Kayo Waki, et al. · JMIR Medical Informatics · 1
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Predicting Rapid Decline in Kidney Function Among Type 2 Diabetes Patients from Laboratory Tests: A Machine Learning Approach
2024 · Eri Nakahara, Kayo Waki, et al. · SSRN Electronic Journal
B-17-14 A Method of Improving Position Measurement Accuracy by the Correction of Signal Strength Considering the Height of the Wi-Fi access points
2015 · Ryo Sakagawa, Tomonori Mori, et al. · Proceedings of the Society Conference of IEICE
B-19-30 A study on precision improvement of matching status between MGC and MG
2009 · Takashi Utahara, Noritaka Horikome, et al. · Proceedings of the IEICE General Conference
Failure-Proof Voice-over-IP
2002 · Toshiyuki Oka, Etsuko Katayama, et al. · IEICE technical report. Speech
A study of restart service processing method for VoIP service.
2001 · Hiroyuki Nakamura, Makoto Kondo, et al. · Proceedings of the Society Conference of IEICE
Works count: 8
Citation count: 7
H-index: 2
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