A novel machine learning architecture to improve classification of intermediate cases in health: workflow and case study for public health

This novel architecture is proving to be a very promising tool for improving accuracy of the models when classifying patients in intermediate classes, regardless of the algorithm used. Accuracy-improvement for likert-type scale measures offers an opportunity for rapidly identifying "risk-profiles" during emergencies and beyond. This applies equally to patients and healthcare providers, with potential for improving quality of care and strengthening patient-centered healthcare systems that prioritize healthcare providers' wellbeing.