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Artificial Intelligence in Behavioral and Mental Health Care

By: , Posted on: November 10, 2015

AI brainAs Artificial Intelligence (AI) becomes increasingly capable and sophisticated, so does the potential for misuse of the technology. In the field of medicine, forward-thinking practitioners, researchers, and policymakers are recognizing (and rapidly embracing) AI — both for its tangible current benefits and for the revolutionary capabilities these technologies promise for the not-so-distant future.) Leaders in AI are also recognizing the need for developing ethical best practices when applying these technologies in healthcare.

In a “big data” medical environment, intelligent machines are crucial components. Why? Because AI excels in the repetitive collection and analysis of complex data — the core capabilities of advanced medical practice. And as medical data grows (both in quantity and quality) healthcare professionals simply have to rely on intelligent machines in order to unlock the secrets of these valuable data assets.

Elsevier’s Artificial Intelligence in Behavioral and Mental Health Care (D. D. Luxton, Editor — October, 2015) is an eye-opening window on state-of-the-art medical AI. This recently released text is both a primer (providing a context on modern AI in medicine) and a description of advanced applications of artificial intelligence technology. The book examines exemplary AI solutions that span a variety of specific technical areas, including: Expert Systems, Machine Learning, Virtual Humans, Mobile Devices, Behavior Models, Public Health Surveillance/Predictive Analytics, and Robotics.

Among the book’s noteworthy examples is a chapter on “The Durkheim Project” — an opt-in public health surveillance solution providing researchers with unique risk-detection capabilities for suicide and extreme mental-health-related events. Through the novel use of machine learning on digital text (as well as large-cohort studies for data collection via social and mobile media) Durkheim’s AI technology accurately predicted suicide risk.

Specifically, the Durkheim Project illustrated next-generation mental health care capabilities in three main areas:

• Machine learning based classification: At 70% predictive accuracy, Durkheim’s medical risk classification accuracy was 19% better than that of an off-line human observer.

• Scalable reach: Using the latest in “big data” technology, the Durkheim system supported a capability for thousands (and potentially hundreds of thousands) of patients to be monitored simultaneously.

• Intervention brokering: The Durkheim system was built to enable both patient/doctor and peer-to-peer interactions — facilitating a systemic approach for remediating suicide risk and facilitating collaboration on interventions correlated to risk levels.

In addition to the Durkheim Project case study, Artificial Intelligence in Behavioral and Mental Health Care showcases numerous other innovative AI case studies. Taken together, these writings illustrate a powerful set of solutions for detecting and treating mental health disorders.  Readers will find here a survey of effective use cases that constitute a next-generation set of capabilities to detect risk, to assist in diagnosis, and to enhance treatment efficacy. It is the hope of these authors that medical AI systems will be adopted, improved upon and combined — as AI continues to shift paradigms and improve outcomes in behavioral and mental health care.

artificial intelligence mental health care

Artificial Intelligence in Behavioral and Mental Health Care is available for purchase on the Elsevier Store. Use discount code “STC215” at checkout to save up to 30% on this and all other books and ebooks!

About The Authors:

David D. Luxton is the editor and primary author of “Artificial Intelligence in Behavioral and Mental Health Care”. Dr. Luxton’s research involves medical informatics and innovation in the area of technology enabled intervention. He is currently a Research Health Scientist at the Naval Health Research Center in San Diego, CA and an affiliate Associate Professor at the University of Washington School of Medicine in Seattle. Luxton previously served as a Research Psychologist and Program Manager with the U.S. Army. An expert in behavioral health technology, he has served on numerous national workgroups and committees and is a highly sought after subject matter expert and consultant. (For more information on David D. Luxton, visit his LinkedIn profile here. His work also is available at http://www.davidluxton.com and http://www.luxtonlabs.com.

Chris Poulin is a contributing author. He also leads a research team (The Durkheim Project) whose work is described in Chapter 9: Public Health Surveillance: Predictive Analytics and Big Data. (A highly relevant topic in current events.)  Currently, Poulin is Principal Partner of Patterns and Predictions, a ‘big data’ predictive analytics group. He recently served as Director of The Durkheim Project, a non-profit big data collaboration with the U.S. Department of Veterans Affairs and Facebook, Inc.  Poulin also was Co-Director of the Dartmouth Metalearning Working Group at Dartmouth College, focusing on large-scale machine learning. He has lectured on Artificial Intelligence and Big Data at the U.S. Naval War College. (For more information on Chris Poulin, visit his LinkedIn profile here. Additional information on The Durkheim Project can be found at http://www.durkheimproject.org.

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Computing functionality is ubiquitous. Today this logic is built into almost any machine you can think of, from home electronics and appliances to motor vehicles, and it governs the infrastructures we depend on daily — telecommunication, public utilities, transportation. Maintaining it all and driving it forward are professionals and researchers in computer science, across disciplines including:

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