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The wide accessibility of the Internet and web-based programs enable an increased volume of online interventions for mental health treatment. In contrast to traditional face-to-face therapy, online treatment has the potential to overcome some of the barriers such as improved geographical accessibility, individual time planning, and reduced costs. The availability of clients’ treatment data fuels research to analyze the collected data to obtain a better understanding of the relationship among symptoms in mental disorders and derive outcome and symptom predictions. This research leads to predictive models that can be integrated into the online treatment process to assist clinicians and clients.
This dissertation discusses different aspects of the development of predictive modeling in online treatment: Categorization of predictive models, data analyses for predictive purposes, and model evaluation. Specifically, the categorization of predictive models and barriers against the uptake of mental health treatment are discussed in the first part of this dissertation. Data analysis and predictive modeling are emphasized in the second part by presenting methods for inference and prediction of mood as well as the prediction of treatment outcome and costs. Prediction of future and current mood can be beneficial in many aspects. Inference of users’ mood levels based on unobtrusive measures or diary data can provide crucial information for intervention scheduling. Prediction of future mood can be used to assess clients’ response to the treatment and expected treatment outcome. Prediction of the expected treatment costs and outcomes for different treatment types allows simultaneous optimization of these objectives and to increase the cost-effectiveness of the treatment. In the third part, a systematic predictive model evaluation incorporating simulation analyses is demonstrated and a method for model parameter estimation for computationally limited devices is presented.
This dissertation aims to overcome the current challenges of predictive model development and its use in online treatment. The development of predictive models for varies data collected in online treatment is demonstrated and how these models can be applied in practice. The derived results contribute to computer science and mental health research with client individual data analysis, the development ofpredictive models, and their statistical evaluation.
Over the last two decades, online advertising has become one of the most important dimension of corporate communications. Nowadays, companies promote their products and services through multiple online marketing channels, for example newsletters, display and video advertising, and most notably, search engine advertising. In this context, statistical models developed in research and practice can be used to measure the effectiveness of advertising activities. The results from these kinds of analyses can, for instance, be used to attribute marketing success (sales, registration, etc.) to individual advertising activities and, thus, to support budget planning for future advertising campaigns. In recent years, a new form of advertising on the Internet has emerged: real-time advertising. Among others, it allows companies to identify potential customers and target them with respect to their interests. In this way, real-time advertising can increase advertising effectiveness and it could, at the same time, improve user experience. With the emerge of this new form of advertising, statistical models have become even more important because they are now being increasingly used to predict online user behavior. The articles included in this dissertation analyze user-level clickstream data generated during multi-channel advertising campaigns (including TV advertising) and during real-time auctions. The goal of the analyses conducted here is to better understand advertising effects and to support decision-making in this context. Most of the analyses are based on Bayesian models. These models allow for a very flexible structure, which enables researchers to model, for instance, heterogeneity across different types of users or non-linear parameters such as users´ reaction times and exponential decay of advertising effects. In addition, these models allow for the inclusion of prior knowledge of parameter distributions, and, therefore, they are well suited for iterative analyses based on clickstream data. Bayesian models can be evaluated in different ways. Instead of only relying on statistical metrics, the articles included in this dissertation aim to estimate the economic value of these models based on their predictive performance. Although this measure can only approximate their true economic value, this approach can be used to compare and evaluate different models and to illustrate the impact of predictive analyses for companies in the context of big data. This dissertation contributes to both information systems research and marketing research and has many managerial implications. First, a process is developed to determine optimal sample sizes representing the best balance between computational costs and predictive accuracy in e-commerce in particular and big data contexts in general. In practice, this process can be used to reduce infrastructure and computational costs. Second, the articles included here describe models that can be used to measure the impact of television ads on users´ online shopping behavior. The models can provide insights concerning the effectiveness of individual television ads, the interactions between different advertising channels and the difference in user behavior of TV-induced customers and their non-TV-induced counterparts. Thereby, the models could support decision-making with respect to future advertising campaigns and targeting. Third, the articles describe several possibilities to extend and improve decision support systems currently used in e-commerce and marketing. These improvements enable practitioners to predict users´ interests for arbitrary products and services by using corresponding websites as dependent variables. This approach can be used to improve the effectiveness of real-time advertising campaigns, especially those intended to raise brand awareness among customers. In addition to these contributions, the articles describe possibilities for future research projects at the intersection of information systems and marketing. These kinds of projects could aim to develop methods to take advantage of new possibilities resulting from technological progress, to increase profits from advertising campaigns and selling ad inventories, to provide deeper insights concerning the effectiveness of multi-channel advertising campaigns, and to improve targeting of individual consumers by considering their interests and privacy concerns.
Mental health is an important factor in an individuals’ life - more than 300 million individuals suffered from depression in 2015. Online-based interventions have been developed for the treatment of various mental disorders. These types of interventions
have proven their efficacy and can lead to positive outcomes for suffering patients. During these interventions, a large amount of patient-specific data is gathered that can be utilized to increase treatment outcomes by informing decision-making processes of psychotherapists, experts in the field, and patients.
The articles included in this dissertation focus on the analysis of such data collected in digital psychological treatments by using machine learning approaches. This dissertation utilizes various machine learning methods such as Bayesian models, regularization techniques, or decision trees to predict different psychological factors, such as mood or self-esteem, dropout of patients, or treatment outcomes and costs. These models are evaluated using a variety of performance metrics, for example, receiver operating characteristics curve, root mean square error, or specialized performance metrics for Bayesian inference. These types of analyses can support decision- making for psychologists and patients, which can, in turn, lead to better recommendations and subsequently to increased outcomes for patients and simultaneously more insight about the interplay between psychological factors. The contribution of this interdisciplinary dissertation is manifold and can be classified at the intersection of Information Systems, health economics, and psychology. The analysis of user journey data has not yet been fully examined in the field of psychological research. A process for this endeavor is developed and a technical implementation is provided for the research community. The application of machine learning in this context is still in its infancy. Thus, another contribution is the exploration and application of machine learning techniques for the revelation of correlations between psychological factors or characteristics and treatment outcomes as well as their prediction. Additionally, economic factors are predicted to develop a process for treatment type recommendations. This approach can be utilized for finding the optimal treatment type for patients on an individual level considering predicted treatment outcomes and costs. By evaluating the predictive accuracy of multiple machine learning techniques based on various performance metrics, the importance of considering heterogeneity among patients’ behavior and affect is highlighted in some articles. Furthermore, the potential of machine learning-based decision support systems in clinical practice has been examined from a psychotherapists’ point of view.
