Max Horn

Last Name

Horn

First Name

Max

ORCID

0000-0002-8269-9948

Show all metadata

Search Results

Publications1 - 10 of 18

Set Functions for Time Series
Horn, Max; Moor, Michael; Bock, Christian; et al. (2020)
Proceedings of Machine Learning Research ~ Proceedings of the 37th International Conference on Machine Learning
Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications. This paper proposes a novel approach for classifying irregularly-sampled time series with unaligned measurements, focusing on high scalability and data efficiency. Our method SeFT (Set Functions for Time Series) is based on recent advances in differentiable set function learning, extremely parallelizable with a beneficial memory footprint, thus scaling well to large datasets of long time series and online monitoring scenarios. Furthermore, our approach permits quantifying per-observation contributions to the classification outcome. We extensively compare our method with existing algorithms on multiple healthcare time series datasets and demonstrate that it performs competitively whilst significantly reducing runtime.
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Moor, Michael; Bennet, Nicolas; Plecko, Drago; et al. (2021)
arXiv
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.
Predicting sepsis using deep learning across international sites: a retrospective development and validation study
Moor, Michael; Bennett, Nicolas; Plečko, Drago; et al. (2023)
eClinicalMedicine
Background: When sepsis is detected, organ damage may have progressed to irreversible stages, leading to poor prognosis. The use of machine learning for predicting sepsis early has shown promise, however international validations are missing. Methods: This was a retrospective, observational, multi-centre cohort study. We developed and externally validated a deep learning system for the prediction of sepsis in the intensive care unit (ICU). Our analysis represents the first international, multi-centre in-ICU cohort study for sepsis prediction using deep learning to our knowledge. Our dataset contains 136,478 unique ICU admissions, representing a refined and harmonised subset of four large ICU databases comprising data collected from ICUs in the US, the Netherlands, and Switzerland between 2001 and 2016. Using the international consensus definition Sepsis-3, we derived hourly-resolved sepsis annotations, amounting to 25,694 (18.8%) patient stays with sepsis. We compared our approach to clinical baselines as well as machine learning baselines and performed an extensive internal and external statistical validation within and across databases, reporting area under the receiver-operating-characteristic curve (AUC). Findings: Averaged over sites, our model was able to predict sepsis with an AUC of 0.846 (95% confidence interval [CI], 0.841–0.852) on a held-out validation cohort internal to each site, and an AUC of 0.761 (95% CI, 0.746–0.770) when validating externally across sites. Given access to a small fine-tuning set (10% per site), the transfer to target sites was improved to an AUC of 0.807 (95% CI, 0.801–0.813). Our model raised 1.4 false alerts per true alert and detected 80% of the septic patients 3.7 h (95% CI, 3.0–4.3) prior to the onset of sepsis, opening a vital window for intervention. Interpretation: By monitoring clinical and laboratory measurements in a retrospective simulation of a real-time prediction scenario, a deep learning system for the detection of sepsis generalised to previously unseen ICU cohorts, internationally.
Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
Moor, Michael; Rieck, Bastian Alexander; Horn, Max; et al. (2021)
Frontiers in Medicine
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.
Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra
Weis, Caroline; Horn, Max; Rieck, Bastian Alexander; et al. (2020)
Bioinformatics
Motivation Microbial species identification based on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a standard tool in clinical microbiology. The resulting MALDI-TOF mass spectra also harbour the potential to deliver prediction results for other phenotypes, such as antibiotic resistance. However, the development of machine learning algorithms specifically tailored to MALDI-TOF MS-based phenotype prediction is still in its infancy. Moreover, current spectral pre-processing typically involves a parameter-heavy chain of operations without analyzing their influence on the prediction results. In addition, classification algorithms lack quantification of uncertainty, which is indispensable for predictions potentially influencing patient treatment. Results We present a novel prediction method for antimicrobial resistance based on MALDI-TOF mass spectra. First, we compare the complex conventional pre-processing to a new approach that exploits topological information and requires only a single parameter, namely the number of peaks of a spectrum to keep. Second, we introduce PIKE, the peak information kernel, a similarity measure specifically tailored to MALDI-TOF mass spectra which, combined with a Gaussian process classifier, provides well-calibrated uncertainty estimates about predictions. We demonstrate the utility of our approach by predicting antibiotic resistance of three clinically highly relevant bacterial species. Our method consistently outperforms competitor approaches, while demonstrating improved performance and security by rejecting out-of-distribution samples, such as bacterial species that are not represented in the training data. Ultimately, our method could contribute to an earlier and precise antimicrobial treatment in clinical patient care.
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
O’Bray, Leslie; Horn, Max; Rieck, Bastian Alexander; et al. (2021)
arXiv
Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a principled way to evaluate and compare them. In this paper, we enumerate the desirable criteria for comparison metrics, discuss the development of such metrics, and provide a comparison of their respective expressive power. We perform a systematic evaluation of the main metrics in use today, highlighting some of the challenges and pitfalls researchers inadvertently can run into. We then describe a collection of suitable metrics, give recommendations as to their practical suitability, and analyse their behaviour on synthetically generated perturbed graphs as well as on recently proposed graph generative models.
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
O’Bray, Leslie; Horn, Max; Rieck, Bastian Alexander; et al. (2022)
The Tenth International Conference on Learning Representations (ICLR 2022)
Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a principled way to evaluate and compare them. In this paper, we enumerate the desirable criteria for such a comparison metric and provide an overview of the status quo of graph generative model comparison in use today, which predominantly relies on the maximum mean discrepancy (MMD). We perform a systematic evaluation of MMD in the context of graph generative model comparison, highlighting some of the challenges and pitfalls researchers inadvertently may encounter. After conducting a thorough analysis of the behaviour of MMD on synthetically-generated perturbed graphs as well as on recently-proposed graph generative models, we are able to provide a suitable procedure to mitigate these challenges and pitfalls. We aggregate our findings into a list of practical recommendations for researchers to use when evaluating graph generative models.
Representation Learning for Dimensionality Reduction, Irregularly-Sampled Sequences and Graphs
Horn, Max (2023)
Machine learning has the potential to revolutionize the fields of biology and healthcare by providing new tools to help scientists and clinicians do research and decide what would be the right treatment for patients. However, while recent approaches in representation learning give the impression of being universal black-box solutions to all problems, research has shown that this is not generally true. Even though models can perform well in a black-box fashion, they often suffer from low generalization and are sensitive to distribution shifts. This highlights the need for developing approaches that are informed by their downstream application and tailored to incorporate symmetries of the problem into the model architecture. These inductive biases are essential for performance on new data and for models to remain robust even when the data distribution changes. Nevertheless, constructing good models is only half of the solution. To be sure that models would translate well into clinical applications they also need to be evaluated appropriately with this goal in mind. In this thesis, I address the above points while taking a detailed look at structured data types present at the intersection of biology, medicine, and machine learning. In terms of algorithmic contributions, I first present a new non-linear dimensionality reduction algorithm that aims to preserve multi-scale relations. The cost reduction of genome sequencing and the ability to sequence individual cells has led to exponentially increasing high-dimensional data in the life sciences. Such data cannot be intuitively understood, making dimensionality reduction approaches, which can capture the nested relationships present in biology, essential. Second, I develop methods for clinical applications where irregularly-sampled data are present. Conventional machine learning models either require the conversion of such data into fixed-size representations or the imputation of missing values prior to their application. I present two approaches tailored for irregularly-sampled data that do not require such preprocessing steps. The first is a new kernel for peaks derived from MALDI-TOF spectra, whereas the second is a deep learning model that can be applied to irregularly-sampled time series by phrasing them as sets of observations. Third, I present an extension to graph neural networks that allow the models to account for global information instead of requiring nodes to only exchange information with their neighbors. Graphs are an important data structure for pharmacology as they are often used to represent small molecules. In order to address the appropriate evaluation of such models, I present a detailed study of medical time series models with a focus on their capability to transfer to other datasets in the context of a sepsis early prediction task. Further, I show that the conventional approach for the evaluation of graph generative models is highly sensitive to the selection of hyperparameters which can lead to biased performance estimates. Summarizing, my thesis addresses many problems at the intersection of machine learning, healthcare, and biology. It demonstrates how models can be improved by including more (domain-specific) knowledge and where to pay attention when evaluating said models.
Pathologies in Priors and Inference for Bayesian Transformers
Cinquin, Tristan; Immer, Alexander; Horn, Max; et al. (2022)
Fourth Symposium on Advances in Approximate Bayesian Inference (AABI 2022)
In recent years, the transformer has established itself as a workhorse in many applications ranging from natural language processing to reinforcement learning. Similarly, Bayesian deep learning has become the gold-standard for uncertainty estimation in safety-critical applications, where robustness and calibration are crucial. Surprisingly, no successful attempts to improve transformer models in terms of predictive uncertainty using Bayesian inference exist. In this work, we study this curiously underpopulated area of Bayesian transformers. We find that weight-space inference in transformers does not work well, regardless of the approximate posterior. We also find that the prior is at least partially at fault, but that it is very hard to find well-specified weight priors for these models. We hypothesize that these problems stem from the complexity of obtaining a meaningful mapping from weight-space to function-space distributions in the transformer. Therefore, moving closer to function-space, we propose a novel method based on the implicit reparameterization of the Dirichlet distribution to apply variational inference directly to the attention weights. We find that this proposed method performs competitively with our baselines.
Bridging the Gap to Real-World Object-Centric Learning
Seitzer, Maximilian; Horn, Max; Zadaianchuk, Andrii; et al. (2023)
The Eleventh International Conference on Learning Representations (ICLR 2023)
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover objects. In this work, we overcome this limitation by showing that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing object-centric learning models on simulated data and is the first unsupervised object-centric model that scales to real world-datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually simple and shows competitive performance compared to more involved pipelines from the computer vision literature.

Publications1 - 10 of 18

Max Horn

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results