Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies
  1. Home
  3. Aim: An Advanced Hybrid Inference Model Combining Clinical Rules And Lifelog-based Learning For Health Risk Prediction.
  1. Home
  3. Aim: An Advanced Hybrid Inference Model Combining Clinical Rules And Lifelog-based Learning For Health Risk Prediction.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Association of hemoglobin levels with metabolic syndrome and its components in older korean males: A nationwide population-based cross-sectional study.

Medicine·2026
Same author

Associations of bilirubin, albumin, and the ALBI index with gastric cancer risk: a smoking-stratified prospective cohort study in men.

BMC gastroenterology·2026
Same author

Cardiovascular Disease Risk Among Young Adults with Disabilities: A Nationwide Cohort Study.

European journal of preventive cardiology·2026
Same author

Firmicutes/Bacteroidetes ratio of the gut microbiota and its association with abdominal obesity and insulin resistance (METS-IR) in Korean adults.

Scientific reports·2026
Same author

A human telomerase reverse transcriptase-derived peptide GV1001 rescues neurodegeneration in a mouse model of Alzheimer disease.

Experimental & molecular medicine·2026
Same author

Gami-Yukmijihwang-Tang ameliorates scopolamine-induced cognitive impairment by restoring cholinergic function and suppressing oxidative stress and neuroinflammation.

Behavioural brain research·2026
Same journal

Correction: Yalçın et al. Impact of SGLT2 Inhibitors on Cardiovascular Risk Scores, Metabolic Parameters, and Laboratory Profiles in Type 2 Diabetes. <i>Life</i> 2025, <i>15</i>, 722.

Life (Basel, Switzerland)·2026
Same journal

Correction: Schubert et al. Minimally Invasive Ablation Strategies for Renal Cell Carcinoma Patients Ineligible for Surgery. <i>Life</i> 2026, <i>16</i>, 73.

Life (Basel, Switzerland)·2026
Same journal

Blood Group Antigen Combinations and COVID-19: Complexity, Associations and Possible Clinical Relevance.

Life (Basel, Switzerland)·2026
Same journal

Beyond HPV in Eastern Europe: Genotype Distribution, Molecular Biomarkers, Vaginal Microbiome, and Implications for Cervical Cancer Prevention.

Life (Basel, Switzerland)·2026
Same journal

Therapeutic Effects of <i>Scutellaria baicalensis</i> Georgi Extract and Baicalein on Olfactory Dysfunction and Neurobehavioral Alterations in a Methimazole-Induced Injury Model.

Life (Basel, Switzerland)·2026
Same journal

The Effects of Unstable Strength Training on Lower Limb Stability in Adolescent Volleyball Players in China.

Life (Basel, Switzerland)·2026
See all related articles

Related Experiment Videos

AIM: An Advanced Hybrid Inference Model Combining Clinical Rules and Lifelog-Based Learning for Health Risk

Junbeom Lee1, Seyeon Kim1, Nam-Hyeok Kim1

  • 1School of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Republic of Korea.

Life (Basel, Switzerland)
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

The Advanced Hybrid Inference Model (AIM) aids early metabolic health risk identification using AI and clinical rules. This interpretable framework estimates biomarkers and predicts risk for better preventive strategies.

Keywords:
lifelog datalong-term and short-term factsmetabolic syndromerandom forestrisk assessmentrule-based expert systemsymbolic reasoning

Related Experiment Videos

Area of Science:

  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems
  • Metabolic Health

Background:

  • Early identification of metabolic health risks is crucial for effective preventive interventions.
  • Routine laboratory testing for metabolic health is often unavailable in general health management settings.
  • Purely data-driven AI models may lack clinical interpretability.

Purpose of the Study:

  • To introduce the Advanced Hybrid Inference Model (AIM), a clinically interpretable framework for metabolic risk screening.
  • To combine biomarker estimation, AI-based risk prediction, and rule-based interpretation for enhanced clinical utility.

Main Methods:

  • A three-stage Random Forest-centered pipeline was implemented for the AIM framework.
  • Stage 1: Estimation of metabolic biomarkers from anthropometric and demographic data.
  • Stage 2: Random Forest model for metabolic risk prediction using measured/estimated biomarkers and clinical variables.
  • Stage 3: Rule-based interpretation to translate model outputs into clinically meaningful risk messages.

Main Results:

  • Experimental validation was performed on clinically collected, class-imbalanced datasets.
  • The AIM framework demonstrated potential in identifying high-risk metabolic patterns.
  • Findings suggest AIM's utility as a screening-oriented approach.

Conclusions:

  • AIM is an exploratory clinical screening support framework, not a diagnostic tool.
  • The framework prioritizes interpretability, rule-based reasoning, and risk prioritization.
  • AIM offers a novel approach to metabolic risk assessment in resource-limited settings.