AIMC Topic: East Asian People

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An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study.

Chinese medical journal
BACKGROUND: Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk amon...

Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study.

European journal of nutrition
PURPOSE: The previous studies that examined the effectiveness of unsupervised machine learning methods versus traditional methods in assessing dietary patterns and their association with incident hypertension showed contradictory results. Consequentl...

Using machine learning to predict UK and Japanese secondary students' life satisfaction in PISA 2018.

The British journal of educational psychology
BACKGROUND: Life satisfaction is a key component of students' subjective well-being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them emp...

Artificial intelligence-based forensic sex determination of East Asian cadavers from skull morphology.

Scientific reports
Identification of unknown cadavers is an important task for forensic scientists. Forensic scientists attempt to identify skeletal remains based on factors including age, sex, and dental treatment remains. Forensic scientists commonly consider skull o...

Language Artificial Intelligences' Communicative Performance Quantified Through the Gricean Conversation Theory.

Cyberpsychology, behavior and social networking
This study pragmatically investigates an artificial intelligence (AI) speaker (AIS)'s verbal communicative performance based on real AI-human conversation data. Specifically, this study explores Grice's conversation theory, which enables the categori...

A Chinese telemedicine-dialogue dataset annotated for named entities.

BMC medical informatics and decision making
BACKGROUND: A large collection of dialogues between patients and doctors must be annotated for medical named entities to build intelligence for telemedicine. However, since most patients involved in telemedicine deliver related named entities in info...

Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort.

Endocrine
BACKGROUND: The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model m...

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of ...

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning.

Frontiers in endocrinology
BACKGROUND: Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic un...

Comparing the performance of machine learning and conventional models for predicting atherosclerotic cardiovascular disease in a general Chinese population.

BMC medical informatics and decision making
BACKGROUND: Accurately predicting the risk of atherosclerotic cardiovascular disease (ASCVD) is crucial for implementing individualized prevention strategies and improving patient outcomes. Our objective is to develop machine learning (ML)-based mode...