A Patient-Centered Text-Derived Neural Network Paradigm for Diagnosis of Schizophrenia

Abstract

A text-derived neural network for diagnosing Schizophrenia is illustrated in this paper. Schizophrenia is a continuous mental condition that affects the job performance, social relationship, and livelihood of individuals. Using DSM-V criterion for schizophrenia diagnosis, we collected data from medical records of 1205 patients in psychiatric hospitals (57% Schizophrenia and 43% Related Illnesses) and developed a neural network model. In order for the developed model to categorize the test data into classes, significant features from the acquired dataset were fed into it to identify indicators in the training data. The model diagnosed schizophrenia with 90% accuracy, 92% specificity, 84% precision and Area under the Receiver Operating Characteristic (ROC) curve of 0.97. These results are promising for schizophrenia diagnosis in the near future. The text-derived ANN developed is more accurate and faster computationally and can be used to generalize in the case of new data when compared to image-based classification.