Early prediction of circulatory failure in the intensive care unit using machine learning

SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch… - Nature medicine, 2020 - nature.com
Intensive-care clinicians are presented with large quantities of measurements from multiple
monitoring systems. The limited ability of humans to process complex information hinders …

Machine learning for biomedical time series classification: from shapelets to deep learning

C Bock, M Moor, CR Jutzeler, K Borgwardt - Artificial Neural Networks, 2021 - Springer
With the biomedical field generating large quantities of time series data, there has been a
growing interest in developing and refining machine learning methods that allow its mining …

Patient similarity analysis with longitudinal health data

A Allam, M Dittberner, A Sintsova, D Brodbeck… - arXiv preprint arXiv …, 2020 - arxiv.org
Healthcare professionals have long envisioned using the enormous processing powers of
computers to discover new facts and medical knowledge locked inside electronic health …

Cross-domain missingness-aware time-series adaptation with similarity distillation in medical applications

B Yang, M Ye, Q Tan, PC Yuen - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Medical time series of laboratory tests has been collected in electronic health records
(EHRs) in many countries. Machine-learning algorithms have been proposed to analyze the …

To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach

M Richter-Laskowska, P Trybek… - PLOS Computational …, 2022 - journals.plos.org
The large conductance voltage-and Ca2+-activated K+ channels from the inner
mitochondrial membrane (mitoBK) are modulated by a number of factors. Among them …

A wasserstein subsequence kernel for time series

C Bock, M Togninalli, E Ghisu… - … Conference on Data …, 2019 - ieeexplore.ieee.org
Kernel methods are a powerful approach for learning on structured data. However, as we
show in this paper, simple but common instances of the popular R-convolution kernel …

Approximate network motif mining via graph learning

C Oliver, D Chen, V Mallet, P Philippopoulos… - arXiv preprint arXiv …, 2022 - arxiv.org
Frequent and structurally related subgraphs, also known as network motifs, are valuable
features of many graph datasets. However, the high computational complexity of identifying …

Machine learning for early prediction of circulatory failure in the intensive care unit

SL Hyland, M Faltys, M Hüser, X Lyu… - arXiv preprint arXiv …, 2019 - arxiv.org
Intensive care clinicians are presented with large quantities of patient information and
measurements from a multitude of monitoring systems. The limited ability of humans to …

Network-guided search for genetic heterogeneity between gene pairs

AC Gumpinger, B Rieck, DG Grimm… - …, 2021 - academic.oup.com
Motivation Correlating genetic loci with a disease phenotype is a common approach to
improve our understanding of the genetics underlying complex diseases. Standard analyses …

Enhancing statistical power in temporal biomarker discovery through representative shapelet mining

T Gumbsch, C Bock, M Moor, B Rieck… - …, 2020 - academic.oup.com
Motivation Temporal biomarker discovery in longitudinal data is based on detecting
reoccurring trajectories, the so-called shapelets. The search for shapelets requires …