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Prediction of Low Bone Mass for Japanese Female Athletes Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Attainment of optimal bone mineral density (BMD) in female athletes is a very important aspect of ensuring their well-being throughout their lives, and monitoring of BMD is essential to avoid fractures and bone-related injuries. Several tools for ass...

Cost-effectiveness of AI-based diabetic retinopathy screening in nationwide health checkups and diabetes management in Japan: A modeling study.

Diabetes research and clinical practice
AIMS: We evaluated the cost-effectiveness of artificial intelligence (AI)-based diabetic retinopathy (DR) screening in Japan. This evaluation compared the simultaneous introduction of AI in nationwide health checkups, namely "specific health check-up...

Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network-Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey.

JMIR formative research
BACKGROUND: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have estimated schizophrenia prevalence in the Japanese population and have often relied on reports from ...

Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation.

JMIR cancer
BACKGROUND: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology...

Predicting social anxiety disorder based on communication logs and social network data from a massively multiplayer online game: Using a graph neural network.

Psychiatry and clinical neurosciences
AIM: Social anxiety disorder (SAD) is a mental disorder that requires early detection and treatment. However, some individuals with SAD avoid face-to-face evaluations, which leads to delayed detection. We aim to predict individuals with SAD based on ...

Artificial intelligence assessment of tissue-dissection efficiency in laparoscopic colorectal surgery.

Langenbeck's archives of surgery
PURPOSE: Several surgical-skill assessment tools emphasize the importance of efficient tissue-dissection, whose assessment relies on human judgment and is thus subject to bias. Automated assessment may help solve this problem. This study aimed to ver...

A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records.

BMC medical informatics and decision making
BACKGROUND: The clinical information housed within unstructured electronic health records (EHRs) has the potential to promote cancer research. The National Cancer Center Hospital (NCCH) is widely recognized as a leading institution for the treatment ...

Exploration of the optimal deep learning model for english-Japanese machine translation of medical device adverse event terminology.

BMC medical informatics and decision making
BACKGROUND: In Japan, reporting of medical device malfunctions and related health problems is mandatory, and efforts are being made to standardize terminology through the Adverse Event Terminology Collection of the Japan Federation of Medical Device ...

Non-Linear Dose-Response Relationship for Metformin in Japanese Patients With Type 2 Diabetes: Analysis of Irregular Longitudinal Data by Interpretable Machine Learning Models.

Pharmacology research & perspectives
The dose-response relationship between metformin and change in hemoglobin A1c (HbA1c) shows a maximum at 1500-2000 mg/day in patients with type 2 diabetes (T2D) in the U.S. In Japan, there is little evidence on the HbA1c-lowering effect of high-dose ...

Performance Improvement of a Natural Language Processing Tool for Extracting Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records by Increasing the Amount of Training Data: Natural Language Processing Analysis and Validation Study.

JMIR medical informatics
BACKGROUND: Patients' oral expressions serve as valuable sources of clinical information to improve pharmacotherapy. Natural language processing (NLP) is a useful approach for analyzing unstructured text data, such as patient narratives. However, few...