Literature DB >> 30900518

Common Audiological Functional Parameters (CAFPAs): statistical and compact representation of rehabilitative audiological classification based on expert knowledge.

Mareike Buhl1,2, Anna Warzybok1,2, Marc René Schädler1,2, Thomas Lenarz2,3, Omid Majdani2,3,4, Birger Kollmeier1,2,5,6.   

Abstract

OBJECTIVE: As a step towards objectifying audiological rehabilitation and providing comparability between different test batteries and clinics, the Common Audiological Functional Parameters (CAFPAs) were introduced as a common and abstract representation of audiological knowledge obtained from diagnostic tests.
DESIGN: Relationships between CAFPAs as an intermediate representation between diagnostic tests and audiological findings, diagnoses and treatment recommendations (summarised as "diagnostic cases") were established by means of an expert survey. Expert knowledge was collected for 14 given categories covering different diagnostic cases. For each case, the experts were asked to indicate expected ranges of diagnostic test outcomes, as well as traffic light-encoded CAFPAs. STUDY SAMPLE: Eleven German experts in the field of audiological rehabilitation from Hanover and Oldenburg participated in the survey.
RESULTS: Audiological findings or treatment recommendations could be distinguished by a statistical model derived from the experts' answers for CAFPAs as well as audiological tests.
CONCLUSIONS: The CAFPAs serve as an abstract, comprehensive representation of audiological knowledge. If more detailed information on certain functional aspects of the auditory system is required, the CAFPAs indicate which information is missing. The statistical graphical representations for CAFPAs and audiological tests are suitable for audiological teaching material; they are universally applicable for real clinical databases.

Entities:  

Keywords:  CAFPAs; Medical audiology; audiological education; clinical decision support system; tele-audiology/tele-health

Mesh:

Year:  2019        PMID: 30900518     DOI: 10.1080/14992027.2018.1554912

Source DB:  PubMed          Journal:  Int J Audiol        ISSN: 1499-2027            Impact factor:   2.117


  4 in total

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

Authors:  Hamid Mousavi; Mareike Buhl; Enrico Guiraud; Jakob Drefs; Jörg Lücke
Journal:  Entropy (Basel)       Date:  2021-04-29       Impact factor: 2.524

2.  Interpretable Clinical Decision Support System for Audiology Based on Predicted Common Audiological Functional Parameters (CAFPAs).

Authors:  Mareike Buhl
Journal:  Diagnostics (Basel)       Date:  2022-02-11

3.  Expert validation of prediction models for a clinical decision-support system in audiology.

Authors:  Mareike Buhl; Gülce Akin; Samira Saak; Ulrich Eysholdt; Andreas Radeloff; Birger Kollmeier; Andrea Hildebrandt
Journal:  Front Neurol       Date:  2022-08-23       Impact factor: 4.086

4.  A flexible data-driven audiological patient stratification method for deriving auditory profiles.

Authors:  Samira Saak; David Huelsmeier; Birger Kollmeier; Mareike Buhl
Journal:  Front Neurol       Date:  2022-09-15       Impact factor: 4.086

  4 in total

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