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 individualsu0027 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. Source code and data of this work have been made public on https:github.comDrDongSiPsychosis_NLP

Predictive modelling of stigmatized behaviour in vaccination discussions on Facebook Facebook often serves as a platform for sharing healthrelated information and is a venue to express attitudes, thoughts, and frustrations within groups centered around healthcare themes. This information can be utilized for public health monitoring, with the aim of tackling stigmatized and stereotypical attitudes in relation to immunization or other health related issues expressed in social media. However, the effectiveness of those attempts will rest on our understanding of the concept of stigma and its correct modeling. In this study, we aim to expand the small pool of existing computational studies on the topic of stigma identification in a health care context. More specifically, we compare the following models using a dataset of 2,761 comments from Facebook: Convolutional Neural Network (CNN): Term FrequencyInverse Document Frequency (TFIDF) with Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Knearest neighbours (KNN), and Stochastic Gradient Descent (SGDC), Long shortterm memory networks (LSTM), Bidirectional long shortterm memory (BiLSTM), and fastText. Accuracy results as evaluated on an unbalanced data subset (with limited training samples) show that fastText gives the best performance, although BiLSTM and CNN achieve comparably good results on unbalanced data as well. CNN algorithm significantly outperforms other algorithms on balanced version of the dataset according to a paired sample ttest ( p  ).