61 citations · 218 across the 11 of their papers we have counts for
12 papers
Med-Flamingo: a Multimodal Medical Few-shot Learner
Michael Moor, Qian Huang, Shirley Wu +6
Medicine, by its nature, is a multifaceted domain that requires the synthesis of information across various modalities. Medical generative vision-language models (VLMs) make a firs…
Almanac: Retrieval-Augmented Language Models for Clinical Medicine
Cyril Zakka, Akash Chaurasia, Rohan Shad +11
Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-a…
Zero-shot causal learning
Hamed Nilforoshan, Michael Moor, Yusuf Roohani +5
Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online mark…
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Michael Moor, Nicolas Bennet, Drago Plecko +5
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 pathoge…
Topological Graph Neural Networks
Max Horn, Edward De Brouwer, Michael Moor +3
Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present…
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Zhiliang Wu, Yinchong Yang, Yunpu Ma +4
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electron…
Path Imputation Strategies for Signature Models of Irregular Time Series
Michael Moor, Max Horn, Christian Bock +2
The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series. However…
Set Functions for Time Series
Max Horn, Michael Moor, Christian Bock +2
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…
Topological Autoencoders
Michael Moor, Max Horn, Bastian Rieck +1
We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders. Using persistent homology, a technique from topologi…
Machine learning for early prediction of circulatory failure in the intensive care unit
Stephanie L. Hyland, Martin Faltys, Matthias Hüser +12
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 proce…
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping
Michael Moor, Max Horn, Bastian Rieck +2
Sepsis is a life-threatening host response to infection associated with high mortality, morbidity, and health costs. Its management is highly time-sensitive since each hour of dela…
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
Bastian Rieck, Matteo Togninalli, Christian Bock +4
While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data. Measures for characterizing and…