Using Machine Learning Algorithms To Build Prediction Models For Well-being: A Data-driven Approach Using Genetic, Environmental, And Psychosocial Predictors - Info and Reading Options

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Overall, life-time prevalence rates of mental health problems are around 30-50% in many countries (Andrade et al., 2000; Kessler et al., 2007), imposing a heavy burden on individuals, families, and communities, accompanied with high health costs related to screening, prevention, and treatment (GBD 2019 Mental Disorders Collaborators, 2022). Previous studies have built prediction models to be able to increase detection and prevention success, and to increase knowledge on possible risk factors of mental illness (Dwyer et al., 2018; Macalli et al., 2021; Tate et al., 2022; H. Yang et al., 2010). Mental health, however, includes both mental illness and well-being. That is, well-being is not simply the absence of mental illness (Keyes, 2002). To assure that our society remains resilient it is therefore also important to develop optimal risk prediction models for well-being, to be able to predict who will thrive and understand why this is the case (Oparina et al., 2022). This information can be valuable for well-being interventions. Previous research on mental health issues have provided us with possible risk factors, that are also relevant for well-being. First, mental health in adulthood has its developmental origins in childhood and adolescence, as indicated by associations with childhood psychopathology, making the availability of longitudinal data crucial (Lahey et al., 2014; Rutter et al., 2006). Second, mental health traits (e.g., depression, life satisfaction, positive affect) are partly driven by thousands of genetic variants with many small but relevant effects, many of which are shared across disorders (Baselmans, van de Weijer, et al., 2019; Kim et al., 2022; Meng et al., 2022; Thorp et al., 2021). Third, many environmental exposures are associated with mental health, examples including socio-economic status, childhood maltreatment, substance use, urbanicity and life events (Uher & Zwicker, 2017). Just as is seen for genetic effects, environmental effects for mental health and well-being overlap. Finally, environmental factors interact with genetic effects on mental health (Assary et al., 2018; Dunn et al., 2016; Uher & Zwicker, 2017). Together, a complex picture of mental health development emerges. Optimal prediction thus likely requires a broad inclusion of possible and protective risk factors, which may lead to the identification of the most relevant factors associated with mental health. This in turn could lead to individualized prediction models for individuals’ future mental health status (Bzdok et al., 2021). Given the multitude of factors associated with mental health, accurate prediction requires appropriate methods that can deal with high complexity. The rise of big data has led to the development of machine learning methods that enable the inclusion of large numbers of variables, while accounting for their potential interactions, consistent with the consensus that mental health results from complex interactions between developmental, social, psychological, genetic, and environmental factors. Recent developments in digitalization and record linkage have further made it increasingly possible to expand our environmental scope by including more objective environmental exposures (e.g., air pollution, green spaces) in mental health models (van de Weijer et al., 2021). Previous machine learning studies on responses to anti-depressants (Taliaz et al., 2021), rehospitalization after depressive episodes (Cearns et al., 2019), and resilience after cancer diagnoses (Kourou et al., 2021) have indeed shown that models including different data modalities outperform models including a single set of predictors. In line with the multi-factorial nature of well-being, a recent study further found that expanding the set of predictive features increased the performance of the models for well-being considerably (Oparina et al., 2022). Recent developments have thus paved the way for more accurate predictions for mental health related traits (Dwyer et al., 2018). However, many studies are conducted using clinical samples, i.e., when treatment is already sought, limiting their external validity and practical usefulness, especially for prevention. In addition, most studies focused on mental illness, rather than on mental health and well-being (Macalli et al., 2021; Tate et al., 2022). At the same time, machine learning prediction studies in population samples largely failed to take an integrative approach meaning that either cross-sectional data were used, or environmental exposures and/or genetic data were limited or missing (Dwyer et al., 2018; Macalli et al., 2021; Oparina et al., 2022; Tate et al., 2022; H. Yang et al., 2010). This may explain why predictive accuracies have not reached the standards needed for clinical use yet (Runeson et al., 2017). In the current project, we will overcome these caveats by building prediction models for well-being with extensive longitudinal data on environmental and psychosocial factors, and genetic data. More specifically, by using an extensive set of predictors and utilizing novel machine learning methods that enable the combined use of multiple prediction models (stacked ensemble model; see below), we aim to build a highly generalizable, comprehensive prediction model for well-being, which can inform future models for clinical prediction and decision-making, hereby preparing society for future mental health challenges.

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