Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise: A Pilot Study

Zhao, Yixuan; Li, Jingsong; Zhang, Meibian; Lu, Yao; Xie, Hongwei; Tian, Yu; Qiu, Wei

doi:10.1097/aud.0000000000000649

Cited by 62 publications

(58 citation statements)

References 35 publications

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“…A limitation of this study is its small sample size (227 patients), compared to previous studies that included 1,220 patients [1] and 1,113 patients [11]. Furthermore, the number of predictors may not have been sufficient compared with that of a former study that used 149 potential predictors [1].…”

Section: Discussionmentioning

confidence: 91%

“…Furthermore, in another study, the accuracy of prediction of hearing outcomes was 76.6% for AdaBoost, 76.9% for RF, 81.9% for MLP, and 83.0% for SVM. In that study, 10-fold cross-validation was implemented, and the study population was 1,113 subjects from 17 factories [11]. To the best of our knowledge, our study is the first in Korea to compare the effectiveness of multiple machine learning models for predicting the prognosis of ISSNHL.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to traditional statistical techniques such as multiple-regression and logistic-regression (LR) models, researchers have attempted to predict the prognosis of ISSNHL using Bayesian cure-rate models [7][8][9][10]. Recently, some researchers have applied various machine learning techniques [1,11].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models for Predicting Hearing Prognosis in Unilateral Idiopathic Sudden Sensorineural Hearing Loss

Park

Jeong

et al. 2020

Clin Exp Otorhinolaryngol

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Objectives. Prognosticating idiopathic sudden sensorineural hearing loss (ISSNHL) is an important challenge. In our study, a dataset was split into training and test sets and cross-validation was implemented on the training set, thereby determining the hyperparameters for machine learning models with high test accuracy and low bias. The effectiveness of the following five machine learning models for predicting the hearing prognosis in patients with ISSNHL after 1 month of treatment was assessed: adaptive boosting, K-nearest neighbor, multilayer perceptron, random forest (RF), and support vector machine (SVM).Methods. The medical records of 523 patients with ISSNHL admitted to Korea University Ansan Hospital between January 2010 and October 2017 were retrospectively reviewed. In this study, we analyzed data from 227 patients (recovery, 106; no recovery, 121) after excluding those with missing data. To determine risk factors, statistical hypothesis tests (e.g., the two-sample t-test for continuous variables and the chi-square test for categorical variables) were conducted to compare patients who did or did not recover. Variables were selected using an RF model depending on two criteria (mean decreases in the Gini index and accuracy).Results. The SVM model using selected predictors achieved both the highest accuracy (75.36%) and the highest F-score (0.74) on the test set. The RF model with selected variables demonstrated the second-highest accuracy (73.91%) and F-score (0.74). The RF model with the original variables showed the same accuracy (73.91%) as that of the RF model with selected variables, but a lower F-score (0.73). All the tested models, except RF, demonstrated better performance after variable selection based on RF.Conclusion. The SVM model with selected predictors was the best-performing of the tested prediction models. The RF model with selected predictors was the second-best model. Therefore, machine learning models can be used to predict hearing recovery in patients with ISSNHL.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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Machine Learning Models for Predicting Hearing Prognosis in Unilateral Idiopathic Sudden Sensorineural Hearing Loss

Park

Jeong

et al. 2020

Clin Exp Otorhinolaryngol

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“…Different datasets can then be mapped to these data elements, making these datasets interoperable, thus easing the data integration process and meta-analysis. In the context of HI research, this will orient data analysis and enable the use of machine learning approaches with sufficient statistical power [31] for predicting disease clinical outcomes [32] and optimal therapeutic interventions, based on the disease pathophysiology mechanisms and other clinical parameters in patient records. It is expected that this HIO will contribute to fostering the subsequent HI research translation into healthcare, inferring knowledge based on patient clinical records, and the development of ontology-powered artificial intelligence medical tools helping in therapeutic interventions, prognosis, and diagnosis, as well as predictive models for an improved understanding of disease processes.…”

Section: Hio Potential Future Applicationsmentioning

confidence: 99%

The Hearing Impairment Ontology: A Tool for Unifying Hearing Impairment Knowledge to Enhance Collaborative Research

Hotchkiss

Manyisa

Adadey

et al. 2019

Genes

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Hearing impairment (HI) is a common sensory disorder that is defined as the partial or complete inability to detect sound in one or both ears. This diverse pathology is associated with a myriad of phenotypic expressions and can be non-syndromic or syndromic. HI can be caused by various genetic, environmental, and/or unknown factors. Some ontologies capture some HI forms, phenotypes, and syndromes, but there is no comprehensive knowledge portal which includes aspects specific to the HI disease state. This hampers inter-study comparability, integration, and interoperability within and across disciplines. This work describes the HI Ontology (HIO) that was developed based on the Sickle Cell Disease Ontology (SCDO) model. This is a collaboratively developed resource built around the 'Hearing Impairment' concept by a group of experts in different aspects of HI and ontologies. HIO is the first comprehensive, standardized, hierarchical, and logical representation of existing HI knowledge. HIO allows researchers and clinicians alike to readily access standardized HI-related knowledge in a single location and promotes collaborations and HI information sharing, including epidemiological, socio-environmental, biomedical, genetic, and phenotypic information. Furthermore, this ontology illustrates the adaptability of the SCDO framework for use in developing a disease-specific ontology.

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“…More frequent applications of a decision tree analysis were evaluating whether a medical and audiological practice is cost-effective, for example, implanting cochlear prosthesis [32], the pursuit of magnetic resonance imaging (MRI) with or without contrast in the workup of undifferentiated asymmetrical sensorineural hearing loss [33], and universal or selective hearing screening on newborns [34, 35]. Some sophisticated machine learning technologies (e.g., neural network multilayer perceptron, support vector machine, random forest, adaptive boosting) have also been used in the hearing healthcare area, for instance, predicting postoperative monosyllabic word recognition performance in adult cochlear implant recipients [36] or predicting noise-induced hearing loss of manufacture workers based on demographic information and working acoustical environments [37, 38]. However, to the best of our knowledge, machine learning technologies have not yet been applied in the geriatric hearing screening area for the purpose of a practical implementation.…”

Section: Introductionmentioning

confidence: 99%

A parsimonious approach for screening moderate-to-profound hearing loss in a community-dwelling geriatric population based on a decision tree analysis

Zhang

et al. 2019

BMC Geriatr

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Background: Hearing loss is one of the most common modifiable factors associated with cognitive and functional decline in geriatric populations. An accurate, easy-to-apply, and inexpensive hearing screening method is needed to detect hearing loss in community-dwelling elderly people, intervene early and reduce the negative consequences and burden of untreated hearing loss on individuals, families and society. However, available hearing screening tools do not adequately meet the need for large-scale geriatric hearing detection due to several barriers, including time, personnel training and equipment costs. This study aimed to propose an efficient method that could potentially satisfy this need. Methods: In total, 1793 participants (≥60 years) were recruited to undertake a standard audiometric air conduction pure tone test at 4 frequencies (0.5-4 kHz). Audiometric data from one community were used to train the decision tree model and generate a pure tone screening rule to classify people with or without moderate or more serious hearing impairment. Audiometric data from another community were used to validate the tree model. Results: In the decision tree analysis, 2 kHz and 0.5 kHz were found to be the most important frequencies for hearing severity classification. The tree model suggested a simple two-step screening procedure in which a 42 dB HL tone at 2 kHz is presented first, followed by a 47 dB HL tone at 0.5 kHz, depending on the individual's response to the first tone. This approach achieved an accuracy of 91.20% (91.92%), a sensitivity of 95.35% (93.50%) and a specificity of 86.85% (90.56%) in the training dataset (testing dataset). Conclusions: A simple two-step screening procedure using the two tones (2 kHz and 0.5 kHz) selected by the decision tree analysis can be applied to screen moderate-to-profound hearing loss in a community-based geriatric population in Shanghai. The decision tree analysis is useful in determining the optimal hearing screening criteria for local elderly populations. Implanting the pair of tones into a well-calibrated sound generator may create a simple, practical and time-efficient screening tool with high accuracy that is readily available at healthcare centers of all levels, thereby facilitating the initiation of extensive nationwide hearing screening in older adults.

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Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise: A Pilot Study

Cited by 62 publications

References 35 publications

Machine Learning Models for Predicting Hearing Prognosis in Unilateral Idiopathic Sudden Sensorineural Hearing Loss

Machine Learning Models for Predicting Hearing Prognosis in Unilateral Idiopathic Sudden Sensorineural Hearing Loss

The Hearing Impairment Ontology: A Tool for Unifying Hearing Impairment Knowledge to Enhance Collaborative Research

A parsimonious approach for screening moderate-to-profound hearing loss in a community-dwelling geriatric population based on a decision tree analysis

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