Common Audiological Functional Parameters (CAFPAs) for single patient cases: deriving statistical models from an expert-labelled data set

Buhl, Mareike; Warzybok, Anna; Schädler, Marc René; Majdani, Omid; Kollmeier, Birger

doi:10.1080/14992027.2020.1728401

Cited by 8 publications

(48 citation statements)

References 20 publications

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“…A detailed description of this database was published by Gieseler et al (22). In the expert survey by Buhl et al (23), a part of this database was labeled for the The CAFPAs CA1-CA4 refer to the hearing threshold at increasing frequencies. Hearing threshold refers to the minimum sound level that is required to hear a sound.…”

Section: Expert Surveymentioning

confidence: 99%

Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology

Saak

Hildebrandt

Kollmeier

et al. 2020

Front. Digit. Health

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The application of machine learning for the development of clinical decision-support systems in audiology provides the potential to improve the objectivity and precision of clinical experts' diagnostic decisions. However, for successful clinical application, such a tool needs to be accurate, as well as accepted and trusted by physicians. In the field of audiology, large amounts of patients' data are being measured, but these are distributed over local clinical databases and are heterogeneous with respect to the applied assessment tools. For the purpose of integrating across different databases, the Common Audiological Functional Parameters (CAFPAs) were recently established as abstract representations of the contained audiological information describing relevant functional aspects of the human auditory system. As an intermediate layer in a clinical decision-support system for audiology, the CAFPAs aim at maintaining interpretability to the potential users. Thus far, the CAFPAs were derived by experts from audiological measures. For designing a clinical decision-support system, in a next step the CAFPAs need to be automatically derived from available data of individual patients. Therefore, the present study aims at predicting the expert generated CAFPA labels using three different machine learning models, namely the lasso regression, elastic nets, and random forests. Furthermore, the importance of different audiological measures for the prediction of specific CAFPAs is examined and interpreted. The trained models are then used to predict CAFPAs for unlabeled data not seen by experts. Prediction of unlabeled cases is evaluated by means of model-based clustering methods. Results indicate an adequate prediction of the ten distinct CAFPAs. All models perform comparably and turn out to be suitable choices for the prediction of CAFPAs. They also generalize well to unlabeled data. Additionally, the extracted relevant features are plausible for the respective CAFPAs, facilitating interpretability of the predictions. Based on the trained models, a prototype of a clinical decision-support system in audiology can be implemented and extended towards clinical databases in the future.

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Section: Expert Surveymentioning

confidence: 99%

Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology

Saak

Hildebrandt

Kollmeier

et al. 2020

Front. Digit. Health

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“…Furthermore, for any given cause (any disease), the distribution of symptoms may be of interest for data analysis (e.g., for precise disease characterization). Indeed, many schemes for medical records taking establish a more continuous recording of symptoms [ 9 , 10 , 11 ]. If symptoms are binarized, as required for noisy-OR networks or as provided by data sets such as QMR-DT [ 12 ], then not all statistical information can be leveraged.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we aim at generalizations of standard noisy-OR models towards models with observable values that lie within an interval, as they occur, e.g., for medical data analysis. An example for medical data that we will use is provided in the form of Common Audiological Functional Parameters (CAFPAs) [ 9 , 23 ] describing certain audiological symptoms which can be used to infer different types of causes for hearing loss or hearing deficits. Another example that we will use is image data that are conventionally stored by assigning a pixel value in an interval (e.g.,

).…”

Section: Introductionmentioning

confidence: 99%

“…In Section 2 , we will motivate the maximum combination (together with other prerequisites of the model) using the example of diseases and symptoms modelling for medical data. Because of its actuality we specifically consider COVID-19 as an introductory example but will use medical data of hearing impairments [ 9 ] for numerical experiments in Section 5 as such data is directly at our perusal. The symptoms will be assumed to be continuous and to lie in a finite interval.…”

Section: Introductionmentioning

confidence: 99%

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Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data

Mousavi

Buhl

Guiraud

et al. 2021

Entropy

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Latent Variable Models (LVMs) are well established tools to accomplish a range of different data processing tasks. Applications exploit the ability of LVMs to identify latent data structure in order to improve data (e.g., through denoising) or to estimate the relation between latent causes and measurements in medical data. In the latter case, LVMs in the form of noisy-OR Bayes nets represent the standard approach to relate binary latents (which represent diseases) to binary observables (which represent symptoms). Bayes nets with binary representation for symptoms may be perceived as a coarse approximation, however. In practice, real disease symptoms can range from absent over mild and intermediate to very severe. Therefore, using diseases/symptoms relations as motivation, we here ask how standard noisy-OR Bayes nets can be generalized to incorporate continuous observables, e.g., variables that model symptom severity in an interval from healthy to pathological. This transition from binary to interval data poses a number of challenges including a transition from a Bernoulli to a Beta distribution to model symptom statistics. While noisy-OR-like approaches are constrained to model how causes determine the observables’ mean values, the use of Beta distributions additionally provides (and also requires) that the causes determine the observables’ variances. To meet the challenges emerging when generalizing from Bernoulli to Beta distributed observables, we investigate a novel LVM that uses a maximum non-linearity to model how the latents determine means and variances of the observables. Given the model and the goal of likelihood maximization, we then leverage recent theoretical results to derive an Expectation Maximization (EM) algorithm for the suggested LVM. We further show how variational EM can be used to efficiently scale the approach to large networks. Experimental results finally illustrate the efficacy of the proposed model using both synthetic and real data sets. Importantly, we show that the model produces reliable results in estimating causes using proofs of concepts and first tests based on real medical data and on images.

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“…A detailed description of this database was published by Gieseler et al (22). In the expert survey by Buhl et al (23), a part of this database was labeled for the…”

Section: Expert Surveymentioning

confidence: 99%

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Common Audiological Functional Parameters (CAFPAs) for single patient cases: deriving statistical models from an expert-labelled data set

Cited by 8 publications

References 20 publications

Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology

Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology

Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data

Table_1.DOCX

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