ArticleHealth information science and systems2025
Exploring the application of machine learning to identify the correlations between phthalate esters and disease: enhancing nursing assessments.
Article in Health information science and systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05892029 (Using Machine Learning Algorithms to Develop a Risk Prediction Model for Phthalate-ester-induced Diseases and Suggestions for Improved Nursing Assessment), which is not on this map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Using Machine Learning Algorithms to Develop a Risk Prediction Model for Phthalate-ester-induced Diseases and Suggestions for Improved Nursing Assessment
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Literature Review of Reproductive Toxicological Studies on Phthalates.International journal of molecular sciences · 2025Pooled it
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Article
- From Exposure to Atherosclerosis: Mechanistic Insights into Phthalate-Driven Ischemic Heart Disease and Prevention Strategies.Life (Basel, Switzerland) · 2026Review
- The impact of Di(2-ethylhexyl) phthalate on human organ development: mechanisms and clinical relevance review.Frontiers in public health · 2026Review
- The Effect of Naturally Acquired Immunity on Mortality Predictors: A Focus on Individuals with New Coronavirus.Biomedicines · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Health risks associated with phthalate esters depend on exposure level, individual sensitivities, and other contributing factors. Purpose: This study employed artificial intelligence algorithms while applying data mining techniques to identify correlations between phthalate esters [di(2-ethylhexyl) phthalate, DEHP], lifestyle factors, and disease outcomes. Methods: We conducted exploratory analysis using demographic and laboratory data collected from the Taiwan Biobank. The study developed a prediction model to examine the relationship between phthalate esters and the risk of developing certain diseases based on various artificial intelligence algorithms, including logistic regression, artificial neural networks, and Bayesian networks. Results: The results indicate that phthalate esters exhibited a greater impact on bone and joint issues than heart problems. We observed that DEHP metabolites, such as mono(2-carboxymethylhexyl) phthalate, mono-n-butyl phthalate, and monoethylphthalate, leave higher residue in females than in males, with statistically significant differences. Monoethylphthalate levels were lower in individuals who exercised regularly than those who did not, indicating statistically significant differences. Conclusions: This study's findings can serve as a valuable reference for clinical nursing assessments regarding diseases related to osteoporosis, arthritis, and musculoskeletal pain. Medical professionals can enhance care quality by considering factors beyond patients' essential physical assessment items.Trial Registration: This study was registered under NCT05892029 on May 5, 2023, retrospectively.
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Registered trials
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 Socratic graph.