Abstract: Early identification of psychiatric conditions such as anxiety and depression in children, adolescents, and young adults coupled with evidenced based treatment can significantly reduce acute and chronic morbidity and complications that occur later in life. An individual with a high risk of developing these serious psychiatric conditions can be identified with data and machine-learning methods before the conditions develop. A method for training and preparing a machine learning model for use in identifying a specific patient's future risk of developing a psychiatric condition (e.g.

Abstract: A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.

Abstract: A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.