AIMC Topic: Japan
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Artificial intelligence assessment of tissue-dissection efficiency in laparoscopic colorectal surgery.
Langenbeck's archives of surgery
Feb 22, 2025
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
Feb 17, 2025
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
Feb 8, 2025
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 ...
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
Jan 30, 2025
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
Jan 29, 2025
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
Jan 23, 2025
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...
Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage.
Critical care (London, England)
Jan 20, 2025
BACKGROUND: Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategie...
Constructing a fall risk prediction model for hospitalized patients using machine learning.
BMC public health
Jan 20, 2025
STUDY OBJECTIVES: This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model's predictions.
Comparative analysis of kidney function prediction: traditional statistical methods vs. deep learning techniques.
Clinical and experimental nephrology
Jan 15, 2025
BACKGROUND: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinica...
Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study.
Clinical and experimental hypertension (New York, N.Y. : 1993)
Jan 8, 2025
OBJECTIVES: Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. We investigated the predictive ability of several ML models for the devel...
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