Search Results - "Nohara, Yasunobu"

Explanation of machine learning models using shapley additive explanation and application for real data in hospital by Nohara, Yasunobu, Matsumoto, Koutarou, Soejima, Hidehisa, Nakashima, Naoki

“…When using machine learning techniques in decision-making processes, the interpretability of the models is important. In the present paper, we adopted the…”
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Association of serum bilirubin levels with risk of cancer development and total death by Inoguchi, Toyoshi, Nohara, Yasunobu, Nojiri, Chinatsu, Nakashima, Naoki

“…Serum levels of bilirubin, a strong antioxidant, may influence cancer risk. We aimed to assess the association between serum bilirubin levels and cancer risk…”
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Stroke Prognostic Scores and Data-Driven Prediction of Clinical Outcomes After Acute Ischemic Stroke by Matsumoto, Koutarou, Nohara, Yasunobu, Soejima, Hidehisa, Yonehara, Toshiro, Nakashima, Naoki, Kamouchi, Masahiro

“…BACKGROUND AND PURPOSE—Several stroke prognostic scores have been developed to predict clinical outcomes after stroke. This study aimed to develop and validate…”
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Postnatal care could be the key to improving the continuum of care in maternal and child health in Ratanakiri, Cambodia by Kikuchi, Kimiyo, Yasuoka, Junko, Nanishi, Keiko, Ahmed, Ashir, Nohara, Yasunobu, Nishikitani, Mariko, Yokota, Fumihiko, Mizutani, Tetsuya, Nakashima, Naoki

“…In South-East Asia, the maternal and child mortality rate has declined over the past decades; however, it varies among and within the countries in the region,…”
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Non-linear association between body weight and functional outcome after acute ischemic stroke by Wakisaka, Kayo, Matsuo, Ryu, Matsumoto, Koutarou, Nohara, Yasunobu, Irie, Fumi, Wakisaka, Yoshinobu, Ago, Tetsuro, Nakashima, Naoki, Kamouchi, Masahiro, Kitazono, Takanari

“…This study aimed to determine whether body weight is associated with functional outcome after acute ischemic stroke. We measured the body mass index (BMI) and…”
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Effect of treatment modality and cerebral vasospasm agent on patient outcomes after aneurysmal subarachnoid hemorrhage in the elderly aged 75 years and older by Ido, Keisuke, Kurogi, Ryota, Kurogi, Ai, Nishimura, Kunihiro, Arimura, Koichi, Nishimura, Ataru, Ren, Nice, Kada, Akiko, Matsuo, Ryu, Onozuka, Daisuke, Hagihara, Akihito, Takagishi, So, Yamagami, Keitaro, Takegami, Misa, Nohara, Yasunobu, Nakashima, Naoki, Kamouchi, Masahiro, Date, Isao, Kitazono, Takanari, Iihara, Koji

“…We sought to examine whether the effect of treatment modality and drugs for cerebral vasospasm on clinical outcomes differs between elderly and non-elderly…”
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Communications Through Contemporary Tools of Information and Communication Technology: Cross-sectional Study Evaluating Health Among Separated Family Members by Nishikitani, Mariko, Ariyoshi, Mie, Nohara, Yasunobu, Umihara, Junko

“…Background: The number of single-living workers separated from their spouses and families has been increasing due to the need to create a balance between life…”
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Machine learning for classification of postoperative patient status using standardized medical data by Yamashita, Takanori, Wakata, Yoshifumi, Nakaguma, Hideki, Nohara, Yasunobu, Hato, Shinji, Kawamura, Susumu, Muraoka, Shuko, Sugita, Masatoshi, Okada, Mihoko, Nakashima, Naoki, Soejima, Hidehisa

“…Background and objective: Real-world evidence is defined as clinical evidence regarding the use and potential benefits or risks of a medical product derived…”
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Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow by Matsumoto, Koutarou, Nohara, Yasunobu, Sakaguchi, Mikako, Takayama, Yohei, Fukushige, Shota, Soejima, Hidehisa, Nakashima, Naoki

“…Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning…”
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Portable health clinic for sustainable care of mothers and newborns in rural Bangladesh by Kikuchi, Kimiyo, Sato, Yoko, Izukura, Rieko, Nishikitani, Mariko, Kato, Kiyoko, Morokuma, Seiichi, Nessa, Meherun, Nohara, Yasunobu, Yokota, Fumihiko, Ahmed, Ashir, Islam-Maruf, Rafiqul, Nakashima, Naoki

“…Regular health checkups are important for mothers and newborns to detect health problems at an early stage; however, this is often difficult in…”
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Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study by Matsumoto, Koutarou, Nohara, Yasunobu, Sakaguchi, Mikako, Takayama, Yohei, Fukushige, Syota, Soejima, Hidehisa, Nakashima, Naoki, Kamouchi, Masahiro

“…Background:Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in…”
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Empowering the Healthcare Worker Using the Portable Health Clinic by Kai, Eiko, Rebeiro-Hargrave, Andrew, Inoue, Sozo, Nohara, Yasunobu, Maruf, Rafiqul Islam, Nakashima, Naoki, Ahmed, Ashir

“…We present a remote healthcare consultancy system that enables healthcare workers to identify noncommunicable diseases in unreached communities. The healthcare…”
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A functional learning health system in Japan: Experience with processes and information infrastructure toward continuous health improvement by Soejima, Hidehisa, Matsumoto, Kautarou, Nakashima, Naoki, Nohara, Yasunobu, Yamashita, Takanori, Machida, Jiro, Nakaguma, Hideki

“…Introduction and definition of the term Learning Health System (LHS) appears to have occurred initially around 2007. Prior to this and the introduction of…”
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Inhibition of dynamin‐related protein 1‐filamin interaction improves systemic glucose metabolism by Kato, Yuri, Ariyoshi, Kohei, Nohara, Yasunobu, Matsunaga, Naoya, Shimauchi, Tsukasa, Shindo, Naoya, Nishimura, Akiyuki, Mi, Xinya, Kim, Sang Geon, Ide, Tomomi, Kawanishi, Eiji, Ojida, Akio, Nakashima, Naoki, Mori, Yasuo, Nishida, Motohiro

“…Background and purpose Maintaining mitochondrial quality is attracting attention as a new strategy to treat diabetes and diabetic complications. We previously…”
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Impact of a learning health system on acute care and medical complications after intracerebral hemorrhage by Matsumoto, Koutarou, Nohara, Yasunobu, Wakata, Yoshifumi, Yamashita, Takanori, Kozuma, Yukio, Sugeta, Rui, Yamakawa, Miki, Yamauchi, Fumiko, Miyashita, Eri, Takezaki, Tatsuya, Yamashiro, Shigeo, Nishi, Toru, Machida, Jiro, Soejima, Hidehisa, Kamouchi, Masahiro, Nakashima, Naoki

“…Introduction Patients with stroke often experience pneumonia during the acute stage after stroke onset. Oral care may be effective in reducing the risk of…”
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Development of Machine Learning Prediction Models for Self-Extubation After Delirium Using Emergency Department Data by Matsumoto, Koutarou, Nohara, Yasunobu, Sakaguchi, Mikako, Takayama, Yohei, Yamashita, Takanori, Soejima, Hidehisa, Nakashima, Naoki

“…Delirium is common in the emergency department, and once it develops, there is a risk of self-extubation of drains and tubes, so it is critical to predict…”
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Extracting Predictive Indicator for Prognosis of Cerebral Infarction Using Machine Learning Techniques by Nohara, Yasunobu, Matsumoto, Koutarou, Nakashima, Naoki

“…Identifying important predicative indicators for prognosis is useful since these factors help for understanding diseases and determining treatments for…”
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Abstract TP298: Measuring Quality of Care for Acute Ischemic Stroke Treated With Acute Reperfusion Therapy in Japan: A Nationwide Quality Improvement Initiative of the J-aspect Study by Ren, Nice, Nishimura, Ataru, Nishimura, Kunihiro, Arimura, Koichi, Kurogi, Ai, Matsuo, Ryu, Kurogi, Ryota, Nakashima, Naoki, Nohara, Yasunobu, Kitazono, Takanari, Iihara, Koji

“…Abstract only Background and Purpose: There is no consensus about efficiently measuring quality indicators (QIs) of acute ischemic stroke (AIS) in Japan. To…”
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Predictive Performance of Machine Learning-Based Models for Poststroke Clinical Outcomes in Comparison With Conventional Prognostic Scores: Multicenter, Hospital-Based Observational Study by Irie, Fumi, Matsumoto, Koutarou, Matsuo, Ryu, Nohara, Yasunobu, Wakisaka, Yoshinobu, Ago, Tetsuro, Nakashima, Naoki, Kitazono, Takanari, Kamouchi, Masahiro

“…Although machine learning is a promising tool for making prognoses, the performance of machine learning in predicting outcomes after stroke remains to be…”
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Interpreting Medical Information Using Machine Learning and Individual Conditional Expectation by Nohara, Yasunobu, Wakata, Yoshifumi, Nakashima, Naoki

“…Recently, machine-learning techniques have spread many fields. However, machine-learning is still not popular in medical research field due to difficulty of…”
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