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BISE 66(3) - Juni 2024

https://dl.gi.de/handle/20.500.12116/45326
Auflistung nach:

Auflistung BISE 66(3) - Juni 2024 nach Schlagwort "Artificial intelligence"

1 - 3 von 3
  • Zeitschriftenartikel
    Digital Surveillance in Organizations
    (Business & Information Systems Engineering: Vol. 66, No. 3, 2024) Grisold, Thomas; Seidel, Stefan; Heck, Markus; Berente, Nicholas
  • Zeitschriftenartikel
    How are We Doing Today? Using Natural Speech Analysis to Assess Older Adults’ Subjective Well-Being
    (Business & Information Systems Engineering: Vol. 66, No. 3, 2024) Finze, Nikola; Jechle, Deinera; Faußer, Stefan; Gewald, Heiko
    The research presents the development and test of a machine learning (ML) model to assess the subjective well-being of older adults based solely on natural speech. The use of such technologies can have a positive impact on healthcare delivery: the proposed ML model is patient-centric and securely uses user-generated data to provide sustainable value not only in the healthcare context but also to address the global challenge of demographic change, especially with respect to healthy aging. The developed model unobtrusively analyzes the vocal characteristics of older adults by utilizing natural language processing but without using speech recognition capabilities and adhering to the highest privacy standards. It is based on theories of subjective well-being, acoustic phonetics, and prosodic theories. The ML models were trained with voice data from volunteer participants and calibrated through the World Health Organization Quality of Life Questionnaire (WHOQOL), a widely accepted tool for assessing the subjective well-being of human beings. Using WHOQOL scores as a proxy, the developed model provides accurate numerical estimates of individuals’ subjective well-being. Different models were tested and compared. The regression model proves beneficial for detecting unexpected shifts in subjective well-being, whereas the support vector regression model performed best and achieved a mean absolute error of 10.90 with a standard deviation of 2.17. The results enhance the understanding of the subconscious information conveyed through natural speech. This offers multiple applications in healthcare and aging, as well as new ways to collect, analyze, and interpret self-reported user data. Practitioners can use these insights to develop a wealth of innovative products and services to help seniors maintain their independence longer, and physicians can gain much greater insight into changes in their patients’ subjective well-being.
  • Zeitschriftenartikel
    Precision Digital Health
    (Business & Information Systems Engineering: Vol. 66, No. 3, 2024) Baird, Aaron; Xia, Yusen
    Accounting for individual and situational heterogeneity (i.e., precision) is now an important area of research and treatment in the field of medicine. This essay argues that precision should also be embraced within digital health artifacts, such as by designing digital health apps to tailor recommendations to individual user characteristics, needs, and situations, rather than only providing generic advice. The challenge, however, is that not much guidance is available for embracing precision when designing or researching digital health artifacts. The paper suggests that a shift toward precision in digital health will require embracing heterogeneous treatment effects (HTEs), which are variations in the effectiveness of treatment, such as variations in effects for individuals of different ages. Embracing precision via HTEs is not trivial, however, and will require new approaches to the research and design of digital health artifacts. Thus, this essay seeks to not only define precision digital health, but also to offer suggestions as to where and how machine learning, deep learning, and artificial intelligence can be used to enhance the precision of interventions provisioned via digital health artifacts (e.g., personalized advice from mental health wellbeing apps). The study emphasizes the value of applying emerging causal ML methods and generative AI features within digital health artifacts toward the goal of increasing the effectiveness of digitially provisioned interventions.