Publications

This is my list of publications and preprints. The ‘dagger’ symbol, i.e. \( {}^\dagger \), indicates equal contribution. The ‘double dagger’ symbol, i.e. \( {}^\ddagger \), is listed for authors that jointly directed said work.

ICML 2020
Abstract We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders. Using persistent homology, a technique from topological data analysis, we calculate topological signatures of both the input and latent space to derive a topological loss term. Under weak theoretical assumptions, we construct this loss in a differentiable manner, such that the encoding learns to retain multi-scale connectivity information. We show that our approach is theoretically well-founded and that it exhibits favourable latent representations on a synthetic manifold as well as on real-world image data sets, while preserving low reconstruction errors.
MLHC 2019
Abstract Sepsis is a life-threatening host response to infection that is associated with high mortality, morbidity, and health costs. Its management is highly time-sensitive because each hour of delayed treatment increases mortality due to irreversible organ damage. Meanwhile, despite decades of clinical research, robust biomarkers for sepsis are missing. Therefore, detecting sepsis early by utilizing the affluence of high-resolution intensive care records has become a challenging machine learning problem. Recent advances in deep learning and data mining promise to deliver a powerful set of tools to efficiently address this task. This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner that is based on time series distances. Our deep learning model employs a temporal convolutional network that is embedded in a multi-task Gaussian Process adapter framework, making it directly applicable to irregularly-spaced time seriesdata. In contrast, our lazy learner is an ensemble approach that employs dynamic time warping. We frame the timely detection of sepsis as a supervised time series classification task. Consequently, we derive the most recent sepsis definition in an hourly resolution to provide the first fully accessible early sepsis detection environment. Seven hours before sepsis onset, our methods improve area under the precision–recall curve from 0.25 to 0.35 and 0.40, respectively, over the state of the art. This demonstrates that they are well-suited for detecting sepsis in the crucial earlier stages when management is most effective.
arXiv 2021
Abstract Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathogen is identified, organ damage may have already progressed to irreversible stages. Effective sepsis management is therefore highly time-sensitive. By systematically analysing trends in the plethora of clinical data available in the intensive care unit (ICU), an early prediction of sepsis could lead to earlier pathogen identification, resistance testing, and effective antibiotic and supportive treatment, and thereby become a life-saving measure. Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU. Our analysis represents the largest multi-national, multi-centre in-ICU study for sepsis prediction using ML to date. Our dataset contains 156,309 unique ICU admissions, which represent a refined and harmonised subset of five large ICU databases originating from three countries. Using the international consensus definition Sepsis-3, we derived hourly-resolved sepsis label annotations, amounting to 26,734 (17.1%) septic stays. We compared our approach, a deep self-attention model, to several clinical baselines as well as ML baselines and performed an extensive internal and external validation within and across databases. On average, our model was able to predict sepsis with an AUROC of 0.847±0.050 (internal out-of sample validation) and 0.761±0.052 (external validation). For a harmonised prevalence of 17%, at 80% recall our model detects septic patients with 39% precision 3.7 hours in advance.
Front. Med. 2021
Abstract Background: Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data Sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. Study Eligibility Criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded. Study Appraisal and Synthesis Methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed. Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from “poor” (satisfying ≤ 40% of the quality criteria) to “very good” (satisfying ≥ 90% of the quality criteria). The majority of the studies (n = 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n = 2, 9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only two studies provided publicly accessible source code and data sources fostering reproducibility. Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU. Conclusions and Key Findings: A growing number of studies employs machine learning to optimize the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses. Systematic Review Registration Number: CRD42020200133.

All Publications

2022

Prediction of Recovery from Multiple Organ Dysfunction Syndrome in Pediatric Sepsis Patients
Bowen Fan, Juliane Klatt, Michael Moor, Latasha A. Daniels, Swiss Pediatric Sepsis Study, Lazaro N. Sanchez-Pinto, Philipp K. A. Agyeman, Luregn J. Schlapbach and Karsten Borgwardt. Intelligent Systems for Molecular Biology (ISMB), 2022.

Topological Graph Neural Networks
Max Horn\(^\dagger\), Edward De Brouwer\(^\dagger\), Michael Moor, Yves Moreau, Bastian Rieck\(^\ddagger\), and Karsten Borgwardt\(^\ddagger\).
International Conference on Learning Representations (ICLR), 2022.

2021

Predicting Sepsis in Multi-Site, Multi-National Intensive Care Cohorts Using Deep Learning
Michael Moor\(^\dagger\), Nicolas Bennet\(^\dagger\), Drago Plecko\(^\dagger\), Max Horn\(^\dagger\), Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, and Karsten Borgwardt.
Preprint arxiv:2107.05230, 2021.

Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review
Michael Moor\(^\dagger\), Bastian Rieck\(^\dagger\), Max Horn, Catherine R Jutzeler\(^\ddagger\), and Karsten Borgwardt\(^\ddagger\).
Frontiers in Medicine, Volume 8, 2021.

A Survey of Topological Machine Learning Methods
Felix Hensel, Michael Moor, and Bastian Rieck.
Frontiers in Artificial Intelligence, Volume 4, 2021.

Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
Jannis Born\(^\dagger\), Nina Wiedemann\(^\dagger\), Manuel Cossio, Charlotte Buhre, Gabriel Brändle, Konstantin Leidermann, Avinash Aujayeb, Michael Moor, Bastian Rieck, and Karsten Borgwardt.
Applied Sciences, Volume 11, No. 2, 2021.

2020

Challenging Euclidean Topological Autoencoders
Michael Moor, Max Horn, Karsten Borgwardt, and Bastian Rieck.
Accepted for presentation at the NeurIPS 2020 TDA and Beyond Workshop. (Poster).

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning
Christian Bock\(^\dagger\), Michael Moor\(^\dagger\), Catherine R Jutzeler, and Karsten Borgwardt.
In Artificial Neural Networks, pp. 33-71. Humana, New York, NY.

Enhancing Statistical Power in Temporal Biomarker Discovery through Representative Shapelet Mining
Thomas Gumbsch, Christian Bock, Michael Moor, Bastian Rieck, and Karsten Borgwardt.
Bioinformatics, Volume 36, Issue Supplement_2, pp. i840–i848, December 2020.

Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Zhiliang Wu, Yinchong Yang, Yunpu Ma, Yushan Liu, Rui Zhao, Michael Moor, and Volker Tresp.
International Conference on Health Informatics ICHI 2020.

Path Imputation Strategies for Signature Models
Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, and Bastian Rieck.
Workshop on the Art of Learning with Missing Values (Artemiss) at ICML 2020. Slides.

Topological Autoencoders
Michael Moor\(^\dagger\), Max Horn\(^\dagger\), Bastian Rieck\(^\ddagger\), Karsten Borgwardt\(^\ddagger\).
International Conference on Machine Learning (ICML), PMLR 119:7045-7054, 2020. Recorded Talk.

Set Functions for Time Series
Max Horn, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt.
International Conference on Machine Learning (ICML), PMLR 119:4353-4363, 2020.
Recorded Talk.

Path Imputation Strategies for Signature Models of Irregular Time Series
Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, and Bastian Rieck.
Preprint, arXiv:2005.12359, 2020.

Early prediction of circulatory failure in the intensive care unit using machine learning
Stephanie L. Hyland\(^\dagger\), Martin Faltys\(^\dagger\), Matthias Hüser\(^\dagger\), Xinrui Lyu\(^\dagger\), Thomas Gumbsch\(^\dagger\), Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt\(^\ddagger\), Gunnar Rätsch\(^\ddagger\), and Tobias M. Merz\(^\ddagger\).
Nature Medicine, Volume 26, Issue 3, pp. 364–373, March 2020.

2019

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping
Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten Borgwardt.
Machine Learning for Healthcare Conference (MLHC), Volume 106 of Proceedings of Machine Learning Research, pp. 2–26, August 2019. (Poster).

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
Bastian Rieck\(^\dagger\), Christian Bock\(^\dagger\), Matteo Tognialli\(^\dagger\), Michael Moor, Max Horn, Thomas Gumbsch, Karsten Borgwardt.
International Conference on Learning Representations (ICLR), 2019. (Poster).

Quantification of Liver, Subcutaneous, and Visceral Adipose Tissues by MRI Before and After Bariatric Surgery Anne Christin Meyer-Gerspach, Ralph Peterli, Michael Moor, Philipp Madörin, Andreas Schötzau, Diana Nabers, Stefan Borgwardt, Christoph Beglinger, Oliver Bieri, Bettina Wölnerhanssen.
Obesity surgery, Volume 29, Issue 9, pp. 2795-2805. Mai 2019.

2018

Association mapping in biomedical time series via statistically significant shapelet mining
Christian Bock, Thomas Gumbsch, Michael Moor, Bastian Rieck, Damian Roqueiro, Karsten Borgwardt.
Bioinformatics, Volume 34, Issue 13, pp. i438-i446, July 2018.