Predicting and Explaining Hearing Aid Usage Using Encoder-Decoder with Attention Mechanism and SHAP

AI can help predict which hearing aid users will actually use their devices, which matters because many patients abandon hearing aids despite having them fitted. The study demonstrates that machine learning models can forecast future usage patterns, potentially allowing clinicians to identify at-risk patients early and intervene before they stop using their devices.

Explainability is crucial for medical AI adoption. The researchers paired their prediction model with SHAP (a technique that explains which factors most influenced each prediction), showing that AI systems can be made transparent enough for doctors to understand why the model made a particular prediction—not just what it predicts. This transparency helps build clinician trust in AI recommendations.

Multiple factors beyond the device itself affect whether patients use hearing aids. The framework identifies that personal, behavioral, and environmental factors all contribute to usage patterns. This suggests that improving hearing aid outcomes requires understanding the whole patient context, not just optimizing the technology alone.

This approach could reduce both human suffering and healthcare costs. By predicting non-compliance and enabling targeted interventions, the framework addresses a real problem: many hearing loss patients experience reduced quality of life and social isolation because they don't use their hearing aids, while healthcare systems bear unnecessary financial burdens from unused devices.