Abstract 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm x 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods.

Authors Paola Malsot, Malte Londschien, Valentina Boeva, Gunnar Rätsch

Submitted Bioinformatics

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Abstract Intensive care departments generate vast multivariate time series data capturing the dynamic physiological states of critically ill patients. Despite advances in AI-driven clinical decision support, existing models remain limited. They are tailored to specific conditions or single institutions and require extensive adaptation for new settings. To make such generalization feasible, we introduce ICareFM, a novel foundation model for intensive care, trained on a harmonized dataset of unprecedented scale. The dataset contains 650,000 patient stays, accumulating more than 4,000 patient years of data, and over one billion measurements from hospitals in the US, several European countries, and China. ICareFM employs a novel self-supervised time-to-event objective that extracts robust patient representations from noisy, irregular, multivariate time series. As a result, ICareFM can generalize to new tasks and beyond its training distribution, a property we demonstrate through evaluations in a range of out-of-distribution scenarios, including transfer to unseen hospitals and zero-shot inference on previously unobserved tasks. ICareFM consistently outperforms conventional machine learning models and recent foundation model baselines, demonstrating strong generalization, improved data efficiency, and the ability to generate interpretable forecasts. These results establish ICareFM as a scalable and adaptable foundation model for critical care time series, enabling zero-shot clinical prediction and working towards the development of digital patient twins for precision medicine.

Authors Manuel Burger, Daphné Chopard, Gregor Lichtner, Malte Londschien, Fedor Sergeev, Moritz Fuchs, Hugo Yèche, Rita Kuznetsova, Martin Faltys, Eike Gerdes, Polina Leshetkina, Micha Christ, Moritz Schanz, Nora Göbel, Peter Bühlmann, Elias Grünewald, Felix Balzer, Gunnar Rätsch

Submitted medRxiv

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Abstract Notable progress has been made in generalist medical large language models across various healthcare areas. However, large-scale modeling of in-hospital time series data - such as vital signs, lab results, and treatments in critical care - remains underexplored. Existing datasets are relatively small, but combining them can enhance patient diversity and improve model robustness. To effectively utilize these combined datasets for large-scale modeling, it is essential to address the distribution shifts caused by varying treatment policies, necessitating the harmonization of treatment variables across the different datasets. This work aims to establish a foundation for training large-scale multi-variate time series models on critical care data and to provide a benchmark for machine learning models in transfer learning across hospitals to study and address distribution shift challenges. We introduce a harmonized dataset for sequence modeling and transfer learning research, representing the first large-scale collection to include core treatment variables. Future plans involve expanding this dataset to support further advancements in transfer learning and the development of scalable, generalizable models for critical healthcare applications.

Authors Manuel Burger, Fedor Sergeev, Malte Londschien, Daphné Chopard, Hugo Yèche, Eike Gerdes, Polina Leshetkina, Alexander Morgenroth, Zeynep Babür, Jasmina Bogojeska, Martin Faltys, Rita Kuznetsova, Gunnar Rätsch

Submitted Best Paper @ NeurIPS AIM-FM Workshop 2024

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Abstract We propose a novel multivariate nonparametric multiple change point detection method using classifiers. We construct a classifier log-likelihood ratio that uses class probability predictions to compare different change point configurations. We propose a computationally feasible search method that is particularly well suited for random forests, denoted by changeforest. However, the method can be paired with any classifier that yields class probability predictions, which we illustrate by also using a k-nearest neighbor classifier. We provide theoretical results motivating our choices. In a large simulation study, our proposed changeforest method achieves improved empirical performance compared to existing multivariate nonparametric change point detection methods. An efficient implementation of our method is made available for R, Python, and Rust users in the changeforest software package.

Authors Malte Londschien, Peter Bühlmann, and Solt Kovács

Submitted arXiv preprints

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Abstract We propose estimation methods for change points in high-dimensional covariance structures with an emphasis on challenging scenarios with missing values. We advocate three imputation like methods and investigate their implications on common losses used for change-point detection. We also discuss how model selection methods have to be adapted to the setting of incomplete data. The methods are compared in a simulation study and applied to a time series from an environmental monitoring system.

Authors Malte Londschien, Solt Kovács and Peter Bühlmann

Submitted Journal of Computational and Graphical Statistics

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