ArticleScientific reports2022
Using random forest to identify longitudinal predictors of health in a 30-year cohort study.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
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- Artificial intelligence-assisted design and optimization of stimuli-responsive nanocarriers for smart drug delivery.Materials today. Bio · 2026Review
- Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors.Journal of neuro-oncology · 2026Article
- Machine learning-based prediction and identification of determinants of teenage pregnancy in ten East African countries.Scientific reports · 2026Article
- Genomic risk model to implement precision prostate cancer screening in clinical care: the ProGRESS study.Nature cancer · 2026Article
- Physical and emotional health among nurses in protracted crisis settings in Lebanon and Jordan: A cross-sectional study.PloS one · 2026Article
- Baseline Serum Metabolites as Predictors of Teplizumab Response in Individuals with Type 1 Diabetes.medRxiv : the preprint server for health sciences · 2025Article
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- Association between daily dose of dipeptidyl peptidase-4 inhibitors and change in glycated hemoglobin in patients with type 2 diabetes: interpretation of mixed-effects machine-learning models using electronic medical records.BMC pharmacology & toxicology · 2025Article
- Artificial Intelligence in Environment and Human Health: Progress, Opportunities and Challenges.Current environmental health reports · 2025Review
- Childhood living conditions as predictors of self-rated health status in middle-aged and older adults: Evidence from a machine learning analysis in 27 high-income countries.medRxiv : the preprint server for health sciences · 2025Article
- Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Regularized regression outperforms trees for predicting cognitive function in the Health and Retirement Study.Machine learning with applications · 2025Article
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- Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence.Scientific reports · 2025Article
- Effects of Sampling Frequency on Human Activity Recognition with Machine Learning Aiming at Clinical Applications.Sensors (Basel, Switzerland) · 2025Article
- Global research trends on the human exposome: a bibliometric analysis (2005-2024).Environmental science and pollution research international · 2025Review
- Dengue dynamics, predictions, and future increase under changing monsoon climate in India.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the wealth of exposome data from longitudinal cohort studies that is currently available, the need for methods to adequately analyze these data is growing. We propose an approach in which machine learning is used to identify longitudinal exposome-related predictors of health, and illustrate its potential through an application. Our application involves studying the relation between exposome and self-perceived health based on the 30-year running Doetinchem Cohort Study. Random Forest (RF) was used to identify the strongest predictors due to its favorable prediction performance in prior research. The relation between predictors and outcome was visualized with partial dependence and accumulated local effects plots. To facilitate interpretation, exposures were summarized by expressing them as the average exposure and average trend over time. The RF model's ability to discriminate poor from good self-perceived health was acceptable (Area-Under-the-Curve = 0.707). Nine exposures from different exposome-related domains were largely responsible for the model's performance, while 87 exposures seemed to contribute little to the performance. Our approach demonstrates that ML can be interpreted more than widely believed, and can be applied to identify important longitudinal predictors of health over the life course in studies with repeated measures of exposure. The approach is context-independent and broadly applicable.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.