Using NLP for Diagnosis of Mental Health Disorders

Natural language processing (NLP) tools have shown promise for automatically diagnosing mental-health disorders by analyzing written texts and speech transcriptions.

In this session, we will survey some of the latest developments in the field, with specific examples of how computational approaches can detect symptoms.

We will also share experimental results and discuss their potential for automatically detecting schizophrenia in patients using only speech patterns.

About the speaker

Kfir Bar

Chief Scientist at Basis Tech

Kfir Bar is the Chief Scientist at Basis Technology. He has spent many years working in a wide range of natural language processing (NLP) disciplines, including statistical machine translation, named entity recognition, and digital-humanity applications. Kfir is a big fan of combining linguistic knowledge with sophisticated AI algorithms for extracting the most important information from a piece of text.

Before Basis, Kfir worked for Intuview as CTO, supporting national security and counter-terrorism missions by deducing authorship, sentiment, intent, and other contextual information.

In 2013, he co-founded Comprendi, which transforms big data into actionable marketing insights. Comprendi served some large scale advertisers in different verticals, with great success. In 2016 Kfir’s team won the 2016 Twitter #Promote challenge, for a $250K prize in cash. Kfir is a lecturer at Bar Ilan University, COLMAN, and IDC (all are universities in Israel), where he teaches courses in computer science, digital humanities, machine learning, and natural language processing.

He holds a Ph.D. in computer science from Tel Aviv University for a thesis on Semantics and Machine Translation, titled “Deriving Paraphrases for Highly Inflected Languages, with a Focus on Machine Translation.