This morning Eleos Health, a company that uses AI-backed voice technology to gather insights into behavioral health, scored $6 million in seed funding. The funding news coincides with the company's announcement that Dr. David Shulkin, former Secretary of the U.S. Department of Veterans Affairs, has joined the company's board. The Israeli company created a tool that uses voice AI capabilities to help mental health professionals analyze their patients. Clinicians can run the technology in the background of a mental health session. The system is then able to analyze the session based on "hundreds of data parameters" and in turn give clinicians more insights and information about the patient, and in turn help personalize care.

Smartphones are quickly evolving from pure information delivery tools into devices with advanced software capable of diagnosing and evaluating a number of health issues. Not only are they being used to expand the reach of mobile eye exams, but with the help of some new research and innovative artificial intelligence tools, they're stepping into the domain of behavioral health. Emotions analytics company Beyond Verbal has launched its Beyond mHealth Research platform in the hopes that it will eventually be able to identify physiological markers in the voice that point to different health-related issues. The project has been getting attention from many in the healthcare and tech communities, including Mark Zuckerberg. According to VentureBeat, the Facebook head has praised the platform for its interesting potential.

Named-Entity Recognizers (NERs) are an important part of information extraction systems in annotation tasks. Although substantial progress has been made in recognizing domain-independent named entities (e.g. location, organization and person), there is a need to recognize named entities for domain-specific applications in order to extract relevant concepts. Due to the growing need for smart health applications in order to address some of the latest worldwide epidemics of behavioral issues (e.g. over eating, lack of exercise, alcohol and drug consumption), we focused on the domain of behavior change, especially {\em lifestyle change}. To the best of our knowledge, there is no named-entity recognizer designed for the lifestyle change domain to enable applications to recognize relevant concepts. We describe the design of an ontology for behavioral health based on which we developed a NER augmented with lexical resources. Our NER automatically tags words and phrases in sentences with relevant (lifestyle) domain-specific tags (e.g. [un/]healthy food, potentially-risky/healthy activity, drug, tobacco and alcoholic beverage). We discuss the evaluation that we conducted with with manually collected test data. In addition, we discuss how our ontology enables systems to make further information acquisition for the recognized named entities by using semantic reasoners.