
The purpose of this project is to build a dataset and model to enable an AI powered diagnostic tool that assesses a child's auditory skills and recommends resources and therapies that can bring them to the next stage. The primary user base of this tool is intended to be the parents of a child with hearing loss however it is the hope of the creators of this tool that speech and language pathologists (SLPs) and other early intervention and pediatric practitioners can find use.

The model uses a natural language processing (NLP) model for text-classification and converts free text inputted by the parent of a child with hearing loss into 1 of 4 clinical categories: DETECTION, DISCRIMINATION, IDENTIFICATION, CLASSIFICATION.

Based on the classification of the child against a given skill a recommendation is made for therapies that can be used to improve the child's competency against a given skill. The value of this approach is that each child is challenged to build upon existing skills while not being given any task too difficult that will result in discouragement.
{"language": ["en"], "license": "mit", "tags": ["medical"], "datasets": ["aarnow/auditory-skills-test2"]}
transformers safetensors distilbert text-classification medical en dataset:aarnow/auditory-skills-test2 license:mit autotrain_compatible endpoints_compatible has_space region:us