Scaling up Prediction of Psychosis by Natural Language Processing

Mental health professionals currently diagnose and treat mental disorders, such as schizophrenia, mainly by analyzing the language and speech of their patients, a method that maybe improved with the usage of artificial intelligence. This study aims to use machine learning to distinguish between the speech of patients who suffer from mental disorders which cause psychosis from that of healthy individuals to improve early detection of schizophrenia. We analyzed forty interview transcripts from patients who have been diagnosed with first episode psychosis. Word embeddings and convolutional neural network were utilized for the classification of patients from healthy individuals. The preliminary test results achieved a prediction rate of 99%, which indicated that our speech classifier was able to discriminate speech in patients from healthy individuals' daily conversations. This suggested that machine learning models can learn and train upon features of natural languages to predict whether or not an individual is beginning to show the first signs of early psychosis based on their speech. This line of inquiry will contribute to the improved identification of individuals at risk for psychiatric symptoms and lead to the development of targeted therapies.