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Daiichiro Akiyama on Muck Rack

Daiichiro Akiyama

As seen in: Nature
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Articles

Acoustic feature selection and clinical information integration for machine learning based dialysis shunt function prediction

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By Shun Yoshida, Miho Shikata, Toshihisa Ishii, Kazuya Takahashi, Ayumu Nakashima, Daiichiro Akiyama, Kie Ohkoshi, Kohei Yamamura, Keiichi Osano, Makiko Konishi
| Nature Verified
Abstract Recent studies have suggested that analysis of hemodialysis shunt sounds can predict hemodynamics, although optimal acoustic features remain unclear. This study compared engineered features (time- and frequency-domain statistics) with perceptual features such as Mel-scale transformations for predicting blood flow volume (FV) and resistive index (RI). We examined whether integrating clinical parameter variables could improve prediction accuracy.
Open in Who Shared Issue with article?

Acoustic feature selection and clinical information integration for machine learning based dialysis shunt function prediction

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By Shun Yoshida, Daiichiro Akiyama, Kie Ohkoshi, Kohei Yamamura, Keiichi Osano, Miho Shikata, Toshihisa Ishii, Makiko Konishi, Kazuya Takahashi, Ayumu Nakashima
| Nature Verified
Abstract Recent studies have suggested that analysis of hemodialysis shunt sounds can predict hemodynamics, although optimal acoustic features remain unclear. This study compared engineered features (time- and frequency-domain statistics) with perceptual features such as Mel-scale transformations for predicting blood flow volume (FV) and resistive index (RI). We examined whether integrating clinical parameter variables could improve prediction accuracy.
Open in Who Shared Issue with article?
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