Kazuya Fujihara
Biographic Data
| ID | 5502184 |
|---|---|
| NAME | Kazuya Fujihara |
| GIVEN NAMES | Kazuya |
| FAMILY NAME | Fujihara |
| SIGNATURE | FUJIHARA K |
| AFFILIATIONS | Niigata University |
| ORCID | 0000-0001-6725-4169 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Demographic and dietary determinants of the association between dietary fibre intake and obesity in Japanese adults with type 2 diabetes
Objective: To evaluate the association between dietary fibre intake and obesity in Japanese outpatients with type 2 diabetes, as well as the demographic and dietary factors influencing this association. Design: Cross-sectional study with nationwide registry data on Japanese type 2 diabetes outpatients (2014–2019). Diet was self-reported via a FFQ. Obesity was defined as BMI ≥ 25 kg/m 2 . Setting: Clinics throughout Japan. Participants: 1565 Japan…
Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data
Background Chronic kidney disease (CKD) is characterized by a decreased glomerular filtration rate or renal injury (especially proteinuria) for at least 3 months. The early detection and treatment of CKD, a major global public health concern, before the onset of symptoms is important. This study aimed to develop machine learning models to predict the risk of developing CKD within 1 and 5 years using health examination data. Methods Data were coll…
Machine learning approach to predict body weight in adults
The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning mode…
Impact on short-term glycaemic control of initiating diabetes care versus leaving diabetes untreated among individuals with newly screening-detected diabetes in Japan
BACKGROUND: The impact of early initiation of diabetes care soon after the identification of hyperglycaemia rather than leaving diabetes untreated on changes in glycaemic control has not been fully clarified. We aimed to quantify the effect of initiating and continuing diabetes care compared with not starting management of diabetes on short-term changes in glycaemic control among the Japanese with newly screening-detected diabetes. METHODS: We re…
Impact on short-term glycaemic control of initiating diabetes care versus leaving diabetes untreated among individuals with newly screening-detected diabetes in Japan
BACKGROUND: The impact of early initiation of diabetes care soon after the identification of hyperglycaemia rather than leaving diabetes untreated on changes in glycaemic control has not been fully clarified. We aimed to quantify the effect of initiating and continuing diabetes care compared with not starting management of diabetes on short-term changes in glycaemic control among the Japanese with newly screening-detected diabetes. METHODS: We re…
Impact on short-term glycaemic control of initiating diabetes care versus leaving diabetes untreated among individuals with newly screening-detected diabetes in Japan
BACKGROUND: The impact of early initiation of diabetes care soon after the identification of hyperglycaemia rather than leaving diabetes untreated on changes in glycaemic control has not been fully clarified. We aimed to quantify the effect of initiating and continuing diabetes care compared with not starting management of diabetes on short-term changes in glycaemic control among the Japanese with newly screening-detected diabetes. METHODS: We re…
Machine learning approach to predict body weight in adults
The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning mode…
Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data
Background Chronic kidney disease (CKD) is characterized by a decreased glomerular filtration rate or renal injury (especially proteinuria) for at least 3 months. The early detection and treatment of CKD, a major global public health concern, before the onset of symptoms is important. This study aimed to develop machine learning models to predict the risk of developing CKD within 1 and 5 years using health examination data. Methods Data were coll…
Demographic and dietary determinants of the association between dietary fibre intake and obesity in Japanese adults with type 2 diabetes
Objective: To evaluate the association between dietary fibre intake and obesity in Japanese outpatients with type 2 diabetes, as well as the demographic and dietary factors influencing this association. Design: Cross-sectional study with nationwide registry data on Japanese type 2 diabetes outpatients (2014–2019). Diet was self-reported via a FFQ. Obesity was defined as BMI ≥ 25 kg/m 2 . Setting: Clinics throughout Japan. Participants: 1565 Japan…
Internal Medicine (3 works) · Medicine (3 works) · Diabetes mellitus (2 works) · Endocrinology (2 works) · Type 2 diabetes (2 works) · Artificial Intelligence (1 work) · Artificial Intelligence in Healthcare (1 work) · Chronic Kidney Disease and Diabetes (1 work) · Coastal and Marine Management (1 work) · Computer Science (1 work)