This book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Personalized Psychiatry - Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health.

This innovative book focuses on potential, limitations, and recommendations for the digital mental health landscape. Authors synthesize existing literature on the validity of digital health technologies, including smartphones apps, sensors, chatbots and telepsychiatry for mental health disorders. They also note that collecting real-time biological information is usually better than just collect filled-in forms, and that will also mitigate problems related to recall bias in clinical appointments. Limitations such as confidentiality, engagement and retention rates are moreover discussed. Presented in fifteen chapters, the work addresses the following questions: may smartphones and sensors provide more accurate information about patients' symptoms between clinical appointments, which in turn avoid recall bias? Is there evidence that digital phenotyping could help in clinical decisions in mental health? Is there scientific evidence to support the use of mobile interventions in mental health? Digital Mental Health will help clinicians and researchers, especially psychiatrists and psychologists, to define measures and to determine how to test apps or usefulness, feasibility and efficacy in order to develop a consensus about reliability. These professionals will be armed with the latest evidence as well as prepared to a new age of mental health.