ReviewInternational archives of occupational and environmental health2021
Contributions and limitations of using machine learning to predict noise-induced hearing loss.
Review in International archives of occupational and environmental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applying Machine Learning Models to Predict Occupational Noise-Induced Hearing Loss: A Systematic Review and Meta-analysis.Noise & healthPooled it
- Prediction of risk of hearing loss by industry noise from cross-sectional and longitudinal data.Communications medicine · 2026Article
- AI-Driven Innovations in Hearing Health: A Review of Artificial Intelligence Applications in Audiology and Hearing Technologies.Current aging science · 2026Review
- The constrained disorder principle emphasizes the importance of variability boundaries for systems to function effectively.Journal of medicine and life · 2025Article
- Path Loss Prediction Model of 5G Signal Based on Fusing Data and XGBoost-SHAP Method.Sensors (Basel, Switzerland) · 2025Article
- Effects of air pollution and noise exposure on occupational hearing loss in oil workers: a prospective cohort study.BMC public health · 2025Article
- Advancements in Robotics and AI Transforming Surgery and Rehabilitation.Journal of pharmacy & bioallied sciences · 2025Article
- Article
- Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.Scientific reports · 2025Article
- Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods.Scientific reports · 2025Article
- Applications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025Article
- Evaluating Prediction Models with Hearing Handicap Inventory for the Elderly in Chronic Otitis Media Patients.Diagnostics (Basel, Switzerland) · 2024Article
- A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population.BMC public health · 2024Article
- Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study.Frontiers in neuroscience · 2024Article
- Quantum computing and machine learning for Arabic language sentiment classification in social media.Scientific reports · 2023Article
- A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images.JAMA otolaryngology-- head & neck surgery · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
purposeNoise-induced hearing loss (NIHL) is a global issue that impacts people's life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of different ML techniques and the procedure of model construction.
methodsThe authors searched PubMed, EMBASE and Scopus on November 26, 2020.
resultsEight studies were recruited in the current review following defined inclusion and exclusion criteria. Sample size in the selected studies ranged between 150 and 10,567. The most popular models were artificial neural networks (n = 4), random forests (n = 3) and support vector machines (n = 3). Features mostly correlated with NIHL and used in the models were: age (n = 6), duration of noise exposure (n = 5) and noise exposure level (n = 4). Five included studies used either split-sample validation (n = 3) or ten-fold cross-validation (n = 2). Assessment of accuracy ranged in value from 75.3% to 99% with a low prediction error/root-mean-square error in 3 studies. Only 2 studies measured discrimination risk using the receiver operating characteristic (ROC) curve and/or the area under ROC curve.
conclusionIn spite of high accuracy and low prediction error of machine learning models, some improvement can be expected from larger sample sizes, multiple algorithm use, completed reports of model construction and the sufficient evaluation of calibration and discrimination risk.
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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 OpenQuestion graph.