⚡ Quick Summary
A recent study developed a machine learning model to predict occupational noise-induced hearing loss (ONIHL) using routine blood and biochemical indicators from 3,297 noise-exposed workers in Shenzhen, China. The model achieved an impressive AUC of 0.942 in validation, showcasing its potential for early diagnosis and intervention.
🔍 Key Details
- 📊 Dataset: 3,297 noise-exposed workers, including 160 ONIHL cases
- 🧩 Features used: Routine blood and biochemical indicators
- ⚙️ Technology: Machine learning algorithms, primarily XGBoost
- 🏆 Performance: AUC 0.942, Sensitivity 0.875, Specificity 0.936 on validation data
🔑 Key Takeaways
- 🔍 Machine learning can effectively predict ONIHL using routine health indicators.
- 📈 The XGBoost model demonstrated exceptional performance with an AUC of 0.942.
- 🧪 Key indicators for risk stratification include serum albumin, platelet distribution width, and lymphocyte percentage.
- 🌟 The model was refined to include only 16 critical features while maintaining strong performance.
- 📊 Validation results showed an AUC of 0.990 on the test dataset.
- 💡 Early diagnosis of ONIHL can lead to timely interventions, improving worker health outcomes.
- 🌍 Study conducted in Shenzhen, China, highlighting the importance of occupational health research.
- 🆔 PMID: 40295130.
📚 Background
Occupational noise-induced hearing loss (ONIHL) is a significant public health concern, particularly in industries with high noise exposure. Traditional methods for diagnosing ONIHL often rely on multiple pure-tone audiometry assessments, which can be time-consuming and resource-intensive. This study aims to leverage machine learning to create a more efficient and accessible risk prediction model using readily available blood and biochemical indicators.
🗒️ Study
The study analyzed data from 3,297 workers exposed to noise in Shenzhen, China, with a focus on identifying cases of ONIHL based on established diagnostic criteria. The dataset was divided into training (D1) and validation (D2) sets, allowing for robust model development and testing. Various machine learning algorithms, including XGBoost, were employed to construct predictive models, with a focus on refining the model to include the most representative variables.
📈 Results
The XGBoost model exhibited remarkable performance, achieving an AUC of 0.942 in the validation dataset, with a sensitivity of 0.875 and specificity of 0.936. On a separate test dataset, the model reached an AUC of 0.990. After feature selection, the model was streamlined to include only 16 critical features, maintaining strong performance metrics, including an AUC of 0.872 and balanced accuracy of 0.798.
🌍 Impact and Implications
The findings from this study have significant implications for occupational health practices. By utilizing routine blood and biochemical indicators, the developed machine learning model can facilitate early diagnosis of ONIHL, allowing for timely interventions that can improve the quality of life for workers. This innovative approach could serve as a model for similar predictive health assessments in other occupational settings, ultimately enhancing workplace safety and health outcomes.
🔮 Conclusion
This study highlights the transformative potential of machine learning in predicting occupational health risks such as ONIHL. By integrating routine health indicators into predictive models, we can achieve more accurate and timely diagnoses, paving the way for improved interventions and worker health. Continued research in this area is essential to further refine these models and expand their applicability across various occupational health challenges.
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Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study.
Abstract
OBJECTIVES: Occupational noise-induced hearing loss (ONIHL) represents a prevalent occupational health condition, traditionally necessitating multiple pure-tone audiometry assessments. We have developed and validated a machine learning model leveraging routine haematological and biochemical parameters, thereby offering novel insights into the risk prediction of ONIHL.
DESIGN, SETTING AND PARTICIPANTS: This study analysed data from 3297 noise-exposed workers in Shenzhen, including 160 ONIHL cases, with the data set divided into D1 (2868 samples, 107 ONIHL cases) and D2 (429 samples, 53 ONIHL cases). The inclusion criteria were formulated based on the GBZ49-2014 Diagnosis of Occupational Noise-Induced Hearing Loss. Model training was performed using D1, and model validation was conducted using D2. Routine blood and biochemical indicators were extracted from the case data, and a range of machine learning algorithms including extreme gradient boosting (XGBoost) were employed to construct predictive models. The model underwent refinement to identify the most representative variables, and decision curve analysis was conducted to evaluate the net benefit of the model across various threshold levels.
PRIMARY OUTCOME MEASURES: Model creation data set and validation data sets: ONIHL.
RESULTS: The prediction model, developed using XGBoost, demonstrated exceptional performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.942, a sensitivity of 0.875 and a specificity of 0.936 on the validation data set. On the test data set, the model achieved an AUC of 0.990. After implementing feature selection, the model was refined to include only 16 features, while maintaining strong performance on a newly acquired independent data set, with an AUC of 0.872, a balanced accuracy of 0.798, a sensitivity of 0.755 and a specificity of 0.840. The analysis of feature importance revealed that serum albumin (ALB), platelet distribution width (PDW), coefficient of variation in red cell distribution width (RDW-CV), serum creatinine (Scr) and lymphocyte percentage (LYMPHP) are critical factors for risk stratification in patients with ONIHL.
CONCLUSION: The analysis of feature importance identified ALB, PDW, RDW-CV, Scr and LYMPHP as pivotal factors for risk stratification in patients with ONIHL. The machine learning model, using XGBoost, effectively distinguishes patients with ONIHLamong individuals exposed to noise, thereby facilitating early diagnosis and intervention.
Author: [‘Li C’, ‘Shi L’, ‘Chen L’, ‘Lin D’, ‘Yang X’, ‘Li P’, ‘Zhang W’, ‘Feng W’, ‘Guo Y’, ‘Zhou L’, ‘Zhang N’, ‘Wang D’]
Journal: BMJ Open
Citation: Li C, et al. Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study. Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study. 2025; 15:e097249. doi: 10.1136/bmjopen-2024-097249