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Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study.

Scientific reports
We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models ...

Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction.

Scientific reports
This study looked at novel data sources for cardiovascular risk prediction including detailed lifestyle questionnaire and continuous blood pressure monitoring, using ensemble machine learning algorithms (MLAs). The reference conventional risk score c...

[New definition of precision nutrition: concept and implementation].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]
The following 10 to 15 years will be a key strategic period for China to improve national nutrition and health. As people's understanding of health and disease continues to deepen, health was defined as a series of signs that can maintain physiologic...

Classifying the lifestyle status for Alzheimer's disease from clinical notes using deep learning with weak supervision.

BMC medical informatics and decision making
BACKGROUND: Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle status changes and interventions. Analyzing electronic health records (EHRs) of patients with AD can help us better und...

Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type.

International journal of environmental research and public health
The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning ...

Assessing the accuracy and completeness of artificial intelligence language models in providing information on methotrexate use.

Rheumatology international
We aimed to assess Large Language Models (LLMs)-ChatGPT 3.5-4, BARD, and Bing-in their accuracy and completeness when answering Methotrexate (MTX) related questions for treating rheumatoid arthritis. We employed 23 questions from an earlier study rel...

Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention.

Human brain mapping
Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain mor...

Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: Lifestyle-related diseases (LSDs) impose a substantial economic burden on patients and health care services. LSDs are chronic in nature and can directly affect the heart and lungs. Therapeutic interventions only based on sy...

The Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint.

JMIR nursing
Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of su...

Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires.

Scientific reports
Cardiovascular and cerebrovascular diseases (CCVD) are prominent mortality causes in Japan, necessitating effective preventative measures, early diagnosis, and treatment to mitigate their impact. A diagnostic model was developed to identify patients ...