AI Generalisation Gap In Comorbid Sleep Disorder Staging

1. 1Identify the limitations of current AI models for sleep staging in clinical populations
2. 2Use techniques like Grad-CAM to interpret and analyze model performance
3. 3Develop new models like iSLEEPS that can better handle disrupted sleep patterns in clinical populations
4. 4Evaluate and refine models using diverse datasets to improve generalization

Data scientists and AI engineers working on healthcare projects can benefit from understanding this limitation to improve model performance in clinical settings. This knowledge can help them develop more accurate and reliable sleep staging models

💡 AI models for sleep staging may not generalize well to clinical populations with disrupted sleep, highlighting the need for more robust and diverse training data

🚨 AI models for sleep staging have a generalization gap in clinical populations #AIinHealthcare