Noise hearing loss evaluation device

Abstract

The invention mainly provides a noise hearing loss assessment device, which comprises a screening module, a collection module, a calculation module, an analysis module and an assessment module, and is characterized in that the assessment device performs single-factor regression analysis on key information of a hearing loss group and a hearing non-loss group and accumulated noise exposure; obtaining difference factors causing hearing loss, and performing multi-factor regression analysis according to the difference factors causing hearing loss to obtain independent risk factors causing hearing loss; according to the independent risk factors causing hearing loss, the column diagram is constructed and used for evaluating hearing loss of a plurality of to-be-detected objects, the evaluation device has good sensitivity and specificity when being used for evaluating hearing loss risks caused by occupational noise exposure, early recognition of high-risk groups can be achieved, effective individual intervention measures can be taken in time, and the evaluation efficiency is improved. The occupational health of workers is protected.

Description

Technical Field

[0001] The present invention relates to the technical field of hearing loss, and in particular, to a device for evaluating noise-induced hearing loss. Background Art

[0002] With the acceleration of industrialization and urbanization, noise pollution has become increasingly serious, especially in noisy working environments where hearing loss is more severe. Since 2015, occupational noise-induced deafness has become the second most common occupational disease after pneumoconiosis. In 2020, the detection rate of hearing loss among workers exposed to noise in industrial enterprises in China was as high as 26.88%. According to a report by the World Health Organization, 16% of adult hearing loss cases globally are caused by occupational noise exposure. Occupational noise exposure leading to hearing loss is a serious occupational health problem.

[0003] Currently, the main risk assessment models for occupational noise exposure-induced hearing loss internationally and in China are the "Acoustics - estimation of noise-induced hearing loss" [ISO1999:2013(E)] issued by the International Organization for Standardization. This model first calculates the age-related hearing threshold level H at different frequencies, then combines the noise exposure level and exposure time to calculate the permanent threshold shift N caused by noise, and finally calculates the hearing threshold level H' related to age and noise according to the formula H' = H + N - H×N / 120 to obtain the risk assessment result; while the hearing level is jointly affected by multiple factors such as gender, age, noise exposure level, noise characteristics, exposure time, wearing of personal protective equipment, daily ear use habits, smoking, drinking, blood pressure, blood sugar, exposure to ototoxic chemicals, educational level, physical exercise, sleep status, etc. The existing risk assessment model for noise-induced hearing loss (ISO1999:2013(E)) involves too few factors and only considers the evaluation of hearing loss by gender, age, noise exposure level (dB) and exposure time (years), while there are many factors leading to hearing loss, so the accuracy of the model for evaluating the risk of noise-induced hearing loss is reduced.

[0004] The above problems need to be solved urgently. Summary of the Invention

[0005] The present invention discloses a device for evaluating noise-induced hearing loss, aiming to solve the technical problems existing in the prior art.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a device for evaluating noise-induced hearing loss, including:

[0008] A screening module, configured to screen multiple objects to be detected that meet the standards;

[0009] A collection module for collecting the key information of the multiple objects to be detected;

[0010] A calculation module for calculating the cumulative noise exposure of the multiple objects to be detected based on the collected key information;

[0011] An analysis module for performing univariate regression analysis on the key information and cumulative noise exposure of the multiple objects to be detected to obtain the differential factors causing hearing loss; performing multivariate regression analysis based on the differential factors causing hearing loss to obtain the independent risk factors causing hearing loss; constructing a binary logistic evaluation model based on the independent risk factors causing hearing loss;

[0012] An evaluation module for constructing a nomogram based on the binary logistic evaluation model to evaluate the hearing loss of the multiple objects to be detected.

[0013] In some preferred embodiments, the key information of the multiple objects to be detected includes: the basic situation of the object to be detected, daily living habits, medical history, and work-related situations.

[0014] In some preferred embodiments, the work-related situations of the multiple objects to be detected include the noise exposure years, the number of noise exposure positions, and the noise exposure position codes of the work positions engaged in.

[0015] In some preferred embodiments, the cumulative noise exposure is calculated by formula (1) according to the work-related situations of the multiple objects to be detected:

[0016]

[0017] In the formula: CNE is the cumulative noise exposure;

[0018] T ref is the reference time;

[0019] n is the number of noise exposure positions;

[0020] i is the noise exposure position code;

[0021] T i is the noise exposure years;

[0022] L Aeq,8h is the 8-hour equivalent continuous A-weighted sound level.

[0023] In some preferred embodiments, a pure tone audiometry threshold test is performed on multiple objects to be detected that meet the standards, and the multiple objects to be detected are divided into a hearing loss group and a non-hearing loss group according to the results of the pure tone audiometry threshold test.

[0024] In some preferred embodiments, a univariate regression analysis is performed based on the key information of the hearing loss group and the non - hearing loss group and the cumulative noise exposure amount to obtain the differential factors leading to hearing loss.

[0025] In some preferred embodiments, a multivariate regression analysis is performed based on the differential factors leading to hearing loss to obtain the independent risk factors for hearing loss.

[0026] In some preferred embodiments, a binary logistic evaluation model is constructed based on the independent risk factors, and based on the binary logistic evaluation model, a nomogram is constructed using the rms package of R software V.4.4.1.

[0027] In some preferred embodiments, the equation of the binary logistic evaluation model is Logit(P)= - 1.349×undergraduate degree - 2.630×postgraduate degree and above - 2.340×using hearing protection supplies + 0.117×cumulative noise exposure amount - 7.463.

[0028] The technical solution adopted by the present invention can achieve the following beneficial effects:

[0029] The noise - induced hearing loss assessment device provided by the present invention includes: a screening module for screening a plurality of objects to be detected that meet the standards; a collection module for collecting the key information of the plurality of objects to be detected; a calculation module for calculating the cumulative noise exposure amount of the plurality of objects to be detected according to the collected key information; an analysis module for performing a univariate regression analysis on the key information of the plurality of objects to be detected and the cumulative noise exposure amount to obtain the differential factors leading to hearing loss, and performing a multivariate regression analysis on the differential factors leading to hearing loss to obtain the independent risk factors for hearing loss; constructing a binary logistic evaluation model according to the independent risk factors for hearing loss; an evaluation module for constructing a nomogram based on the binary logistic evaluation model to evaluate the hearing loss of the plurality of objects to be detected. By performing a regression analysis on the key information of the hearing loss group and the non - hearing loss group, the influencing factors leading to hearing loss can be comprehensively determined, which is used for the risk assessment of occupational noise - induced hearing loss, has good sensitivity and specificity, helps to realize the early identification of high - risk groups, and timely take effective individualized intervention measures to achieve the early prevention and intervention of noise - induced hearing loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. These drawings form a part of the present invention, and the illustrative embodiments of the present invention and their explanations explain the present invention without unduly limiting the present invention. In the drawings:

[0031] Figure 1 It is a schematic flowchart of a noise-induced hearing loss assessment device provided by an exemplary embodiment of the present invention;

[0032] Figure 2 It is a nomogram of a noise-induced hearing loss assessment model provided by an exemplary embodiment of the present invention;

[0033] Figure 3 It is an ROC curve graph of a noise-induced hearing loss assessment model provided by an exemplary embodiment of the present invention;

[0034] Figure 4 It is a calibration curve graph of a noise-induced hearing loss assessment model provided by an exemplary embodiment of the present invention. Detailed implementation manners

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. In the description of the present invention, it should be noted that the term "or" is generally used in the sense of including "and / or", unless otherwise clearly specified in the content.

[0036] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or a magnetic connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations. In addition, in the description of the present invention, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, or more, unless otherwise clearly and specifically defined.

[0037] Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0038] To solve the problems existing in the prior art, an embodiment of the present invention provides a noise-induced hearing loss assessment device.

[0039] Figure 1 It is a schematic flowchart of a noise-induced hearing loss assessment device provided by an exemplary embodiment of the present invention. As Figure 1As shown in the figure, the noise-induced hearing loss assessment device includes:

[0040] A screening module 101 for screening multiple objects to be detected that meet the criteria.

[0041] Specifically, through standard rules, multiple objects to be detected are selected. Among them, the standard rules include objects engaged in noise work for 1 year or more, and exclude objects with sudden deafness, congenital or familial deafness, traumatic deafness, ototoxic drug use, infectious disease-induced deafness, eardrum perforation, middle ear or outer ear malformation and surgery, Meniere's disease, acoustic neuroma, auditory neuropathy, conductive hearing loss, pseudohypacusis, and exaggerated deafness among those engaged in noise work for 1 year or more.

[0042] A collection module 102 for collecting key information of multiple objects to be detected;

[0043] Preferably, information such as the basic situation, daily living habits, disease history, and work-related situation of the objects to be detected is collected.

[0044] A calculation module 103 for calculating the cumulative noise exposure based on the key information of multiple objects to be detected;

[0045] An analysis module 104: used to perform univariate regression analysis on the key information and cumulative noise exposure of multiple objects to be detected to obtain the differential factors causing hearing loss, and perform multivariate regression analysis based on the differential factors causing hearing loss to obtain the independent risk factors causing hearing loss; construct a binary logistic evaluation model based on the independent risk factors causing hearing loss;

[0046] An evaluation module 105: used to construct a nomogram based on the binary logistic regression model to evaluate the hearing loss of multiple objects to be detected.

[0047] In some preferred embodiments, the key information of multiple objects to be detected includes: the basic situation, daily living habits, disease history, and work-related situation of the objects to be detected.

[0048] Preferably, the basic situation of the object to be detected involves name, gender, age, and educational level;

[0049] Preferably, the daily living habits of the object to be detected involve the frequency of using headphones daily, the exposure to noise environment in daily life, smoking status, drinking status, physical exercise status, and sleep status.

[0050] Preferably, the disease history of the object to be detected involves the hearing-related disease history, hearing-related disease family history, blood pressure, and blood sugar conditions.

[0051] Preferably, the work-related conditions of the object to be detected involve the noise-exposed unit, the noise-exposed position, the noise-exposed working years, the wearing situation of hearing protection supplies (wearing type and wearing time), and the exposure situation of ototoxic chemical substances.

[0052] In some preferred embodiments, the cumulative noise exposure CNE is calculated by formula (1) according to the work-related conditions of multiple objects to be detected:

[0053]

[0054] In formula (1): CNE is the cumulative noise exposure;

[0055] T ref is the reference time; the value is 1;

[0056] n is the number of noise-exposed positions; the unit is piece;

[0057] i is the noise-exposed position code;

[0058] T i is the noise-exposed working years; the unit is year;

[0059] L Aeq,8h is the 8-hour equivalent continuous A-weighted sound level.

[0060] Among them, the parameters n, i, Ti, and LAeq,8h are obtained from on-site investigations of the workplace and noise exposure level detections. Using the fixed-point sampling principle, detection points for noise exposure data are set, and sampling equipment is used to collect various types of exposure noise data, and LAeq,8h is calculated. The unit of LAeq,8h is dB(A).

[0061] Preferably, for example, if the number of noise-exposed positions of the object to be detected is 1, then the noise-exposed position code is 1; for another example, if the number of noise-exposed positions of the object to be detected is 2, then the noise-exposed position codes are 1 and 2; if the number of noise-exposed positions of the object to be detected is 3, then the noise-exposed position codes are 1, 2, and 3.

[0062] In some preferred embodiments, pure-tone audiometry threshold tests are performed on multiple objects to be detected that meet the standards, and the multiple objects to be detected are divided into a hearing loss group and a non-hearing loss group according to the pure-tone audiometry threshold test results.

[0063] In the embodiments of the present invention, by dividing the hearing loss group and the non-hearing loss group, it can be determined whether the influence of a certain factor in the hearing loss group is significant and whether it is ineffective in the non-hearing loss group.

[0064] Specifically, the hearing threshold is the minimum sound intensity that an object can hear at a specific frequency. It is an important indicator for measuring hearing ability, usually expressed in decibels (dB), and the comprehensive value of the specified frequency hearing threshold level is used to evaluate the hearing loss caused by noise exposure.

[0065] Optionally, in this embodiment, the discrimination criterion between the hearing loss group and the non-hearing loss group adopts the preconditions for diagnosing occupational noise-induced deafness stipulated in Chapter 4 of GBZ 49-2014, evaluates the average hearing threshold level of both ears at high frequencies (3000 Hz, 4000 Hz, 6000 Hz), and the boundary is 40 dB, which is used as the early warning value for the risk of noise occupational disease hazards.

[0066] Optionally, in this embodiment, the discrimination criterion between the hearing loss group and the non-hearing loss group can also be based on the "high-frequency standard hearing threshold shift" stipulated in the "Hearing Protection Specification for Industrial Enterprise Workers", evaluates the average hearing threshold level of any ear at high frequencies (3000 Hz, 4000 Hz, and 6000 Hz), and the boundary is 10 dB, which is used as the management value for the risk of noise occupational disease hazards.

[0067] Optionally, in this embodiment, the discrimination criterion between the hearing loss group and the non-hearing loss group can also be based on the diagnostic grading of occupational noise-induced deafness stipulated in Chapter 4 of GBZ 49-2014, evaluates the weighted hearing threshold values of the better ear at speech frequencies (500 Hz, 1000 Hz, and 2000 Hz) and high frequency 4000 Hz, that is, 1 / 3×[HL500 Hz + HL1000 Hz + HL2000 Hz]×0.9 + HL4000Hz×0.1, and the boundary is 25 dB, which is used as the warning value for the risk of noise occupational disease hazards.

[0068] Specifically, in the implementation process, in combination with the purpose of the risk assessment of noise occupational disease hazards, the frequencies and boundaries in the above discrimination criteria between the hearing loss group and the non-hearing loss group can be selected according to needs.

[0069] In some preferred embodiments, a univariate regression analysis is performed based on the key information of the hearing loss group and the non-hearing loss group and the cumulative noise exposure amount to obtain the differential factors causing hearing loss. Among them, the differential factors include the age, education level, drinking situation, blood pressure, use of protective equipment, noise level, length of service exposed to noise, and cumulative noise exposure amount of the object to be detected.

[0070] Preferably, in this embodiment, the discrimination criterion between the hearing loss group and the non-hearing loss group adopts the preconditions for diagnosing occupational noise-induced deafness stipulated in Chapter 4 of GBZ 49-2014, evaluates the average hearing threshold level of both ears at high frequencies (3000 Hz, 4000 Hz, 6000 Hz), and the boundary is 40 dB, which is used as the early warning value for the risk of noise occupational disease hazards.

[0071] Preferably, the key information of multiple objects to be detected includes: the basic situation of the object to be detected, daily living habits, medical history, work-related situations. According to the basic situation of the object to be detected (name, gender, age, educational level), daily living habits (frequency of using earphones in daily life, exposure to noise environment in daily life, smoking situation, drinking situation, physical exercise situation and sleep status), medical history (hearing-related medical history, family history of hearing-related diseases, blood pressure, blood sugar situation), work-related situations (noise-exposed unit, noise-exposed position, noise-exposed working years, wearing situation of hearing protection supplies (type and wearing time), exposure to ototoxic chemicals) and cumulative noise exposure amount and other relevant data, using Rstudio (version V.4.4.1) statistical analysis software, age, noise-exposed working years and cumulative noise exposure amount are used as continuous variables for analysis, and the remaining factors are used as categorical variables for analysis. With the hearing level as the dependent variable, the basic situation, daily living habits, medical history, work-related situations and CNE are used as independent variables for univariate logistic regression analysis. Table 1 is the univariate logistic regression analysis table of the detection objects. As shown in Table 1, the P-value (P-value) under each influencing factor is obtained through statistical analysis. The P-value is a parameter used to determine the result of a hypothesis test, and it can also be compared with the rejection region of the distribution according to different distributions. If the P-value is very small, it means that the probability of the occurrence of the original hypothesis situation is very small. And if it appears, according to the small probability principle, we have reason to reject the original hypothesis. The smaller the P-value, the more sufficient the reason for us to reject the original hypothesis, and the smaller the P-value, the more significant the result. As can be seen from Table 1, the P-values of age, educational level, drinking situation, blood pressure, use situation of protection supplies, noise level, noise-exposed working years and cumulative noise exposure amount are all less than 0.1, and the differences are significant, which are the differential factors of hearing loss.

[0072] Table 1 Univariate logistic regression analysis table of detection objects

[0073]

[0074]

[0075] In some preferred embodiments, multivariate regression analysis is performed according to the differential factors causing hearing loss to obtain independent risk factors for hearing loss. Among them, the independent risk factors include the educational level of the object to be detected, the use situation of protection supplies and the cumulative noise exposure amount.

[0076] Preferably, eight variables of differential factors leading to hearing loss (age, education level, alcohol consumption, blood pressure, use of protective equipment, noise level, length of service exposed to noise, and cumulative noise exposure) were included in the multivariate logistic regression analysis. The stepwise backward regression method was used for the multivariate regression analysis. The stepwise backward regression method includes two processes: introducing variables and removing variables. Both introducing variables and removing variables need to be subjected to an F-test before proceeding. The principle for introducing variables is that the one with the largest partial regression sum of squares among the variables not yet introduced is selected and subjected to an F-significance test. If it is significant, it is introduced; otherwise, the process is terminated. The principle for removing variables is that the one with the smallest partial regression sum of squares among the introduced independent variables is selected and removed if it is not significant after the F-test. Through the above stepwise backward regression method for regression analysis, as shown in Table 2, the P values of the education level, use of protective equipment, and cumulative noise exposure of the test subjects are < 0.05, which are independent risk factors for occupational noise exposure-induced hearing loss.

[0077] Table 2 Multivariate logistic regression analysis table of test subjects

[0078]

[0079] In some preferred embodiments, a binary logistic evaluation model was constructed based on the independent risk factors for hearing loss (education level, use of protective equipment, and cumulative noise exposure). The equation of the evaluation model is Logit(P) = -1.349 × undergraduate - 2.630 × postgraduate and above - 2.340 × use of hearing protection equipment + 0.117 × cumulative noise exposure - 7.463.

[0080] Based on the parameters of the constructed binary logistic evaluation model and the independent risk factors for hearing loss (education level, use of protective equipment, and cumulative noise exposure), a nomogram was constructed using the rms package of the Rstudio (version V.4.4.1) statistical analysis software. The nomogram can visualize the model. Figure 2 It is the nomogram of the noise-induced hearing loss evaluation model provided by an exemplary embodiment of the present invention; in the figure, for hearing protection equipment, 1 represents the use of protective equipment, and 0 represents the non-use of protective equipment; for education level, 2 represents postgraduate and above; 1 represents undergraduate, and 0 represents below undergraduate; as Figure 2 shown, the ordinate in the nomogram represents the risk relationship between occupational noise exposure-induced hearing loss and education level, hearing protection equipment, and cumulative noise exposure (CNE) respectively; the abscissa represents the corresponding "Points" value for each risk factor vertically; among them, "Total Points" is the sum of the "Points" corresponding to all risk factors, and "Pr()" is the risk prediction value of occupational noise exposure-induced hearing loss.

[0081] Preferably, for example, the cumulative noise exposure (CNE) is 88, and the corresponding Points value is 68; the hearing protection device is 1, indicating the use of protective equipment, and the corresponding Points value is 8; the education level is 1, indicating a bachelor's degree, and the corresponding Points value is 33. Then the total Points value is the sum of the above Points values, that is, 107, and the corresponding Pr() is 0.2. Then the probability of noise-induced hearing risk is 20%, indicating a relatively low risk. For another example, the cumulative noise exposure (CNE) is 88, and the corresponding Points value is 68; the hearing protection device is 0, indicating no use of protective equipment, and the corresponding Points value is 70; the education level is 0, indicating a degree below bachelor's, and the corresponding Points value is 71. Then the total Points value is the sum of the above Points values, that is, 209, and the corresponding Pr() is 0.93. Then the probability of noise-induced hearing risk is 93%, indicating a relatively high risk.

[0082] To further verify the accuracy of the binary logistic evaluation model, the pROC package of the Rstudio (version V.4.4.1) statistical analysis software was used to draw the ROC curve of the evaluation model. Figure 3 is the ROC curve graph of the noise hearing loss evaluation model provided by an exemplary embodiment of the present invention; as Figure 3 shown. The area under the ROC curve AUC = 0.825. The area under the ROC curve AUC is between 0.7 and 1, indicating high test accuracy. It can be seen that the evaluation model has high accuracy in distinguishing occupational noise exposure-induced hearing loss and non-hearing loss.

[0083] To further verify the specificity of the binary logistic evaluation model, the rms package of the Rstudio (version V.4.4.1) statistical analysis software was used to obtain the calibration curve of the prediction model. Figure 4 is the calibration curve graph of the noise hearing loss evaluation model provided by an exemplary embodiment of the present invention; as Figure 4 shown, the calibration curve shows that the predicted probability is basically consistent with the actual probability. After the Hosmer-Lemeshow test, the P value is 0.114, indicating that there is no significant difference in the goodness-of-fit of the evaluation model, that is, the consistency between the predicted probability and the actual observed probability of the model is good.

[0084] It can be seen that the risk assessment model of the present invention has comprehensive and complete influencing factors for the risk assessment of occupational noise exposure-induced hearing loss, and has good sensitivity and specificity. The nomogram based on this model is applicable to industrial enterprise managers to conduct hearing loss risk assessment on workers exposed to occupational noise, which helps to achieve early identification of high-risk groups, take effective individualized intervention measures in a timely manner, can conduct early prevention and intervention on noise-induced hearing loss, protect the occupational health of workers, and also provides a convenient and effective tool for enterprise managers to assess the risk of occupational noise exposure-induced hearing loss.

[0085] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention, and all of them belong to the protection scope of the present invention.

Claims

1. A device for evaluating noise-induced hearing loss, characterized in that, Including: A screening module for screening multiple objects to be detected that meet the criteria; A collection module for collecting key information of the multiple objects to be detected; A calculation module for calculating the cumulative noise exposure amount according to the key information of the multiple objects to be detected; An analysis module for performing univariate regression analysis on the key information and cumulative noise exposure amount of the multiple objects to be detected to obtain the differential factors causing hearing loss; performing multivariate regression analysis according to the differential factors causing hearing loss to obtain the independent risk factors causing hearing loss; constructing a binary logistic evaluation model according to the independent risk factors causing hearing loss; An evaluation module for constructing a nomogram based on the binary logistic evaluation model to evaluate the hearing loss of the multiple objects to be detected.

2. The noise-induced hearing loss assessment device according to claim 1, characterized in that, The key information of the multiple objects to be detected includes: the basic situation of the object to be detected, daily living habits, disease history, and work-related situations.

3. The noise-induced hearing loss assessment device according to claim 2, wherein The work-related situations of the multiple objects to be detected include the noise exposure working years, the number of noise exposure positions, and the noise exposure position codes of the work positions engaged in.

4. The noise-induced hearing loss assessment device according to claim 3, wherein The cumulative noise exposure amount is calculated by formula (1) according to the work-related situations of the multiple objects to be detected: In the formula: CNE is the cumulative noise exposure amount; T ref is the reference time; n is the number of noise exposure positions; i is the noise exposure position code; T i is the noise exposure length; L Aeq,8h is the equivalent continuous A-weighted sound level for 8 h.

5. The noise-induced hearing loss assessment device according to claim 1, wherein Perform pure tone audiometry threshold tests on multiple objects to be detected that meet the criteria, and divide the multiple objects to be detected into a hearing loss group and a non-hearing loss group according to the pure tone audiometry threshold test results.

6. The noise-induced hearing loss assessment device according to claim 5, characterized in that Perform univariate regression analysis on the key information and cumulative noise exposure amount of the hearing loss group and the non-hearing loss group to obtain the differential factors causing hearing loss.

7. The noise-induced hearing loss assessment device according to claim 6, wherein Perform multivariate regression analysis according to the differential factors causing hearing loss to obtain the independent risk factors causing hearing loss.

8. The noise-induced hearing loss assessment device according to claim 7, wherein Construct a binary logistic evaluation model according to the independent risk factors. Based on the binary logistic evaluation model, use the rms package of R software V.4.4.1 to construct a nomogram.

9. The noise-induced hearing loss assessment device according to claim 8, wherein The equation of the binary logistic evaluation model is Logit(P)=-1.349×bachelor's degree - 2.630×graduate degree or above - 2.340×using hearing protection equipment + 0.117×cumulative noise exposure amount - 7.463.

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