Machine Learning Algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer’s Disease Patients in the General Population, 2019, by Uspenskaya-Cadoz et al. Researchers found that language patterns such as writing short and simple phrases, repeating and misspelling words, and skipping punctuation were associated with future onset of Alzheimer's. The language pattern analysis was about 70% accurate in predicting who developed Alzheimer's disease. Tests that are easier for diagnosis and automate via natural language processing techniques (RMI brain scans are expensive). Our first tool:
- Listen or read to memorize poems: offering different levels of difficulty, genres, authors
- Voice recognition: the user has learned a poem, speaks with the microphone
- See misspelling - correspondence to the text and gives recommendations: go further to the next level, try some more practice, teach in small passages…
Log in or sign up for Devpost to join the conversation.