News Release

Artificial intelligence identifies the shortest path to human happiness

Deep Longevity, in collaboration with Nancy Etcoff of Harvard Medical School, publishes an article showcasing a deep learning approach to mental health. The article serves as the scientific background for a free self-help application, FuturSelf

Peer-Reviewed Publication

Deep Longevity Ltd

SOM-based recommendation engine for mental health

video: FuturSelf is a free online mental health service that offers guidance based on a psychological profile assessment by AI. The core of FuturSelf is represented by a self-organizing map that classifies respondents and identifies the most suitable ways to improve one's well-being view more 

Credit: Fedor Galkin

Today, Deep Longevity, in co-authorship with Nancy Etcoff, PhD, an authority on happiness and beauty published an article in Aging-US describing a machine learning approach to human psychology: “Optimizing future well-being with artificial intelligence: Self-organizing maps (SOMs) for the identification of islands of emotional stability.”

The authors used data from the Midlife in the US study to create two digital models of human psychology.

The first model is an ensemble of deep neural networks that use information from a psychological survey to predict the chronological age of the respondents and their psychological well-being in 10 years. This model demonstrates the aging-related trajectories of the human mind. It also shows that the ability to build meaningful relationships increases with age, as do mental autonomy and environmental mastery. It simultaneously indicates that the focus on personal growth steadily declines, and the feeling of having a purpose in life only drops after 40–50 years. These findings contribute to the discussion of socioemotional selectivity and hedonic adaptation in the context of adult personality development.

The second model is a self-organizing map developed as the backbone of a recommendation engine for mental health applications. This unsupervised learning technique divides all respondents into clusters based on their risk of developing depression and identifies the shortest path toward a cluster of mental stability for any individual. Alex Zhavoronkov, the chief longevity officer of Deep Longevity, elaborates, “Existing mental health applications offer generic advice that applies to everyone yet fits no one. We have built a system that is scientifically sound and offers superior personalization.”

To demonstrate this system’s potential, Deep Longevity has released a web service FuturSelf, a free online application that lets users take the psychological test described in the original publication. At the end of the assessment, users receive a report with insights aimed at improving their long-term mental well-being and can enroll in a guidance program that provides them with a steady flow of AI-chosen recommendations. Data obtained on FuturSelf will be used to further develop Deep Longevity’s digital approach to mental health.

A leading biogerontology expert, professor Vadim Gladyshev from Harvard Medical School, comments on the potential of FuturSelf:

 “This study offers an interesting perspective on psychological age, future well-being, and risk of depression, and demonstrates a novel application of machine learning approaches to the issues of psychological health. It also broadens how we view aging and transitions through life stages and emotional states.”

The authors plan to continue studying human psychology in the context of aging and long-term well-being. They are working on a follow-up study on the effect of happiness on physiological measures of aging.

About Deep Longevity

The Company is wholly owned by Endurance Longevity (SEHK:0575.HK), whose shares are quoted on the Hong Kong Stock Exchange, develops explainable artificial intelligence systems to track the rate of aging at molecular, cellular, tissue, organ, system, physiological, and psychological levels. It is also developing systems for the emerging field of longevity medicine, which enables physicians to make better decisions about interventions that may slow down or reverse the aging processes. Deep Longevity developed the Longevity as a Service (LaaS)© solution to integrate multiple deep biomarkers of aging dubbed “deep aging clocks” to provide a universal multifactorial measure of human biological age.

Originally incubated by Insilico Medicine, Deep Longevity began its independent journey in 2020 after securing a round of funding from the most credible venture capitalists specializing in biotechnology, longevity, and artificial intelligence: ETP Ventures; the Human Longevity and Performance Impact Venture Fund; BOLD Capital Partners; Longevity Vision Fund; LongeVC; Michael Antonov, the co-founder of Oculus; and other expert AI and biotechnology investors. Deep Longevity established a research partnership with Human Longevity, Inc., one of the most prominent longevity organizations to provide a range of aging clocks to a network of advanced physicians and researchers.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.