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ENBIS-24 Leuven Conference

15–19 Sept 2024
Leuven, Belgium
Europe/Berlin timezone

Chair of the Local Organising Committee

Session

Stochastic modelling

16 Sept 2024, 13:30
Leuven, Belgium

Leuven, Belgium

Janseniusstraat 1, 3000 Leuven

Conveners

Stochastic modelling

  • Anne Driscoll (Virginia Tech)

Presentation materials

There are no materials yet.

  1. 74. Knowledge Transfer from Marketing to Healthcare - Using Customer Lifetime Value in Dental Practice
    Andrea Ahlemeyer-Stubbe (Ahlemeyer-Stubbe)
    16/09/2024, 13:30
    Stochastic Modelling

    Dental practices are a small business. Like any other business, they need cash flow management and financial planning to be viable, if not highly profitable. What a lot of practices may not realize is that they are sitting on a treasure trove of data to be used in more ways than plain accounting and financial forecasting. Here we focus on longitudinal data, such as the timing of each patient’s...

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  2. 76. Application of spatial point processes to the study of brain tumor localization
    Dr Sophie Mézières (INRIA BIGS - IECL)
    16/09/2024, 13:50
    Stochastic Modelling

    Gliomas are the most common form of primary brain tumors. Diffuse Low-Grade Gliomas (DLGG) are slow growing tumors, and often asymptomatic during a long period. They turn into a higher grade, leading to the patients’ death. Treatments are surgery, chemotherapy and radiotherapy, with the aim of controlling tumor evolution. Neuro-oncologists estimate the tumor size evolution by delineating tumor...

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  3. 129. Automated Paths Detection in Composite-Based Structural Equation Modeling
    Laura Trinchera (NEOMA Business School)
    16/09/2024, 14:10
    Stochastic Modelling

    Structural Equation Models (SEMs) are primarily employed as a confirmatory approach to validate research theories. SEMs operate on the premise that a theoretical model, defined by structural relationships among unobserved constructs, can be tested against empirical data by comparing the observed covariance matrix with the implied covariance matrix derived from the model parameters....

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