Inspired By Research. Centered Around People. Rooted In Technology.

Aiberry’s technology is based on two decades of research led by our Chief Scientist and co-founder, Dr. Newton Howard. A fateful personal experience led him down a path committed to improving mental healthcare through technology, and his work is the backbone of Aiberry’s solution.

A Personal Experience

A Commitment To Improving Mental Healthcare

“Through my personal and professional experience, I have seen firsthand that our brain and neural systems are capable of incredible powers that have yet to be discovered or fully understood.

My journey into the field of neurosciences and mental health began after years of active duty in the United States Army. While overseas on a special assignment, the vehicle I was traveling in ran over an IED. As a result of this blast, I developed a traumatic brain injury (TBI) and quickly saw firsthand how poor mental health treatment and care options were. It was at this moment that I decided to dedicate my professional efforts to brain science and mental health. I refocused my research on psychiatry, neuroscience, and artificial intelligence with the goal of gaining a deeper understanding of the human brain and novel treatments using technology.”

- Dr. Newton Howard

A Deeper Look At The Research

Since finding his calling, Dr. Howard has collaborated with top researchers across the world and pushed our understanding of cognitive neuroscience and computational systems to a new level. Throughout his career, he has published over 183 patents and written more than 300 articles in the field of AI. While at the Department of Mathematical Sciences at the University of Oxford in 2000, Dr. Howard proposed the Theory of Intention Awareness, which is one of the cornerstones for the science behind Aiberry’s solution.

Case 1

Real-time vocal features extraction for automated suicidal risk assessment in hotlines

Mental health disorders, particularly depression and suicide, have become a major concern for society. Public health interventions, such as suicide crisis hotlines, are often staffed by individuals who not mental health professionals and can overlook subtle signs. The human voice carries much more than simply the linguistic content, and the purpose of this research is to evaluate suicidal risk in hotline calls through speech analysis and give real-time feedback regarding the caller’s suicidal impulses.

C. Aguet, L. Falissard, F. Shurmann, N. Howard

Case 2

Towards a Differential Diagnostic of PTSD Using Cognitive Computing Methods

The aim of this research is to construct a predictive algorithm based on linguistic computation to predict Post Traumatic Stress Disorder (PTSD) and comorbid conditions. Measurement of the phenomena associated with diagnosing PTSD is difficult and largely subjective. Our approach analyses natural language using the Mind State Indicator algorithm (MSI), which may be able to diagnose PTSD and specify differential diagnosis from comorbid disorders.

Newton Howard, Louis Jehel, Romain Arnal (2014)

Case 3

Fusing audio, visual and textual clues for sentiment analysis from multimodal content

A novel methodology for multimodal sentiment analysis, which consists in harvesting sentiments from videos by leveraging audio, visual and textual modalities as sources of information. The approach uses both feature-and decision-level fusion methods to merge affective information extracted from multiple modalities.

Soujanya Poria, Erik Cambria, Newton Howard, Guang-Bin Huang, Amir Hussain (2015)

Case 4

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

An approach for developing a diagnosis system for mood disorders, such as depression and bipolar disorder, based on language analysis from speech and text. Our system is based on the Mood State Indicator algorithm (MSI) for real-time analysis of a patient’s mental state.

Soujanya Poria, Navonil Majumder, Rada Mihalcea, Eduard Hovy (2019)

Case 5

Approach Towards a Natural Language Analysis for Diagnosing Mood Disorders and Comorbid Conditions

Dr. Newton Howard (2013)

Aiberry’s Groundbreaking Clinical Study

Aiberry’s AI tools and technology are held to the same rigorous standards as research clinicians to ensure the efficacy of our platform. We are currently conducting an ongoing clinical study with leading healthcare providers and academic institutions to train and validate Aiberry’s Mental Health Screening Platform in patients between the ages of 13 and 79.