@article{MaierWiljesHartungetal.2022,
  author    = {Maier, Corinna Sabrina and Wiljes, Jana de and Hartung, Niklas and Kloft, Charlotte and Huisinga, Wilhelm},
  title     = {A continued learning approach for model-informed precision dosing},
  series = {CPT: pharmacometrics \& systems pharmacology},
  volume    = {11},
  journal   = {CPT: pharmacometrics \& systems pharmacology},
  number    = {2},
  publisher = {London},
  address   = {Nature Publ. Group},
  issn      = {2163-8306},
  doi       = {10.1002/psp4.12745},
  pages     = {185 -- 198},
  year      = {2022},
  abstract  = {Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, because only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.},
  language  = {en}
}
@article{RuchiDubinkinaWiljes2021,
  author    = {Ruchi, Sangeetika and Dubinkina, Svetlana and Wiljes, Jana de},
  title     = {Fast hybrid tempered ensemble transform filter formulation for Bayesian elliptical problems via Sinkhorn approximation},
  series = {Nonlinear processes in geophysics / European Geosciences Union ; American Geophysical Union},
  volume    = {28},
  journal   = {Nonlinear processes in geophysics / European Geosciences Union ; American Geophysical Union},
  number    = {1},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1023-5809},
  doi       = {10.5194/npg-28-23-2021},
  pages     = {23 -- 41},
  year      = {2021},
  abstract  = {Identification of unknown parameters on the basis of partial and noisy data is a challenging task, in particular in high dimensional and non-linear settings. Gaussian approximations to the problem, such as ensemble Kalman inversion, tend to be robust and computationally cheap and often produce astonishingly accurate estimations despite the simplifying underlying assumptions. Yet there is a lot of room for improvement, specifically regarding a correct approximation of a non-Gaussian posterior distribution. The tempered ensemble transform particle filter is an adaptive Sequential Monte Carlo (SMC) method, whereby resampling is based on optimal transport mapping. Unlike ensemble Kalman inversion, it does not require any assumptions regarding the posterior distribution and hence has shown to provide promising results for non-linear non-Gaussian inverse problems. However, the improved accuracy comes with the price of much higher computational complexity, and the method is not as robust as ensemble Kalman inversion in high dimensional problems. In this work, we add an entropy-inspired regularisation factor to the underlying optimal transport problem that allows the high computational cost to be considerably reduced via Sinkhorn iterations. Further, the robustness of the method is increased via an ensemble Kalman inversion proposal step before each update of the samples, which is also referred to as a hybrid approach. The promising performance of the introduced method is numerically verified by testing it on a steady-state single-phase Darcy flow model with two different permeability configurations. The results are compared to the output of ensemble Kalman inversion, and Markov chain Monte Carlo methods results are computed as a benchmark.},
  language  = {en}
}
@misc{WiljesTong2020,
  author    = {Wiljes, Jana de and Tong, Xin T.},
  title     = {Analysis of a localised nonlinear ensemble Kalman Bucy filter with complete and accurate observations},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  volume    = {33},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {9},
  publisher = {IOP Publ.},
  address   = {Bristol},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-54041},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-540417},
  pages     = {4752 -- 4782},
  year      = {2020},
  abstract  = {Concurrent observation technologies have made high-precision real-time data available in large quantities. Data assimilation (DA) is concerned with how to combine this data with physical models to produce accurate predictions. For spatial-temporal models, the ensemble Kalman filter with proper localisation techniques is considered to be a state-of-the-art DA methodology. This article proposes and investigates a localised ensemble Kalman Bucy filter for nonlinear models with short-range interactions. We derive dimension-independent and component-wise error bounds and show the long time path-wise error only has logarithmic dependence on the time range. The theoretical results are verified through some simple numerical tests.},
  language  = {en}
}
@article{WiljesTong2020,
  author    = {Wiljes, Jana de and Tong, Xin T.},
  title     = {Analysis of a localised nonlinear ensemble Kalman Bucy filter with complete and accurate observations},
  series = {Nonlinearity},
  volume    = {33},
  journal   = {Nonlinearity},
  number    = {9},
  publisher = {IOP Publ.},
  address   = {Bristol},
  issn      = {0951-7715},
  doi       = {10.1088/1361-6544/ab8d14},
  pages     = {4752 -- 4782},
  year      = {2020},
  abstract  = {Concurrent observation technologies have made high-precision real-time data available in large quantities. Data assimilation (DA) is concerned with how to combine this data with physical models to produce accurate predictions. For spatial-temporal models, the ensemble Kalman filter with proper localisation techniques is considered to be a state-of-the-art DA methodology. This article proposes and investigates a localised ensemble Kalman Bucy filter for nonlinear models with short-range interactions. We derive dimension-independent and component-wise error bounds and show the long time path-wise error only has logarithmic dependence on the time range. The theoretical results are verified through some simple numerical tests.},
  language  = {en}
}