• Medientyp: E-Book; Hochschulschrift
  • Titel: Discovering latent structure in high-dimensional healtcare data : toward improved interpretability
  • Beteiligte: Becker, Ann-Kristin [Verfasser:in]; Stanke, Mario [Akademische:r Betreuer:in]; Kaderali, Lars [Akademische:r Betreuer:in]; Fröhlich, Holger [Akademische:r Betreuer:in]
  • Körperschaft: Universität Greifswald
  • Erschienen: Greifswald, August 2021
  • Umfang: 1 Online-Ressource (PDF-Datei: 153 Seiten, 10722 Kilobyte); Illustrationen (teilweise farbig), Diagramme (teilweise farbig)
  • Sprache: Englisch
  • Identifikator:
  • RVK-Notation: ST 278 : Mensch-Maschine-Kommunikation Software-Ergonomie
  • Schlagwörter: Datenstruktur > Maschinelles Lernen > Gesundheitswesen
  • Entstehung:
  • Hochschulschrift: Dissertation, Mathematisch-Naturwissenschaftliche Fakultät der Universität Greifswald, 2022
  • Anmerkungen: Literaturverzeichnis: Seite 115-123. - Literaturangaben
  • Beschreibung: Data Science, Bayes-Netz, Dimensionsreduktion, Fettleber, Cluster, Hierarchie, Interpretability, Interpretable Machine Learning, Latent Structure

    This cumulative thesis describes contributions to the field of interpretable machine learning in the healthcare domain. Three research articles are presented that lie at the intersection of biomedical and machine learning research. They illustrate how incorporating latent structure can provide a valuable compression of the information hidden in complex healthcare data. Methodologically, this thesis gives an overview of interpretable machine learning and the discovery of latent structure, including clusters, latent factors, graph structure, and hierarchical structure. Different workflows are developed and applied to two main types of complex healthcare data (cohort study data and time-resolved molecular data). The core result builds on Bayesian networks, a type of probabilistic graphical model. On the application side, we provide accurate predictive or discriminative models focusing on relevant medical conditions, related biomarkers, and their interactions.
  • Zugangsstatus: Freier Zugang