Search Results for author: Michael Moor

Found 16 papers, 10 papers with code

AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments

no code implementations13 May 2024 Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, Jeffrey Jopling, Michael Moor

Here, we present AgentClinic: a multimodal benchmark to evaluate LLMs in their ability to operate as agents in simulated clinical environments.

Decision Making Question Answering

Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting

no code implementations26 Oct 2023 Benjamin Yan, Ruochen Liu, David E. Kuo, Subathra Adithan, Eduardo Pontes Reis, Stephen Kwak, Vasantha Kumar Venugopal, Chloe P. O'Connell, Agustina Saenz, Pranav Rajpurkar, Michael Moor

First, we extract the content from an image; then, we verbalize the extracted content into a report that matches the style of a specific radiologist.

Med-Flamingo: a Multimodal Medical Few-shot Learner

1 code implementation27 Jul 2023 Michael Moor, Qian Huang, Shirley Wu, Michihiro Yasunaga, Cyril Zakka, Yash Dalmia, Eduardo Pontes Reis, Pranav Rajpurkar, Jure Leskovec

However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a significant limitation as in many medical applications data is scarce, necessitating models that are capable of learning from few examples in real-time.

Medical Visual Question Answering Question Answering +1

Almanac: Retrieval-Augmented Language Models for Clinical Medicine

no code implementations1 Mar 2023 Cyril Zakka, Akash Chaurasia, Rohan Shad, Alex R. Dalal, Jennifer L. Kim, Michael Moor, Kevin Alexander, Euan Ashley, Jack Boyd, Kathleen Boyd, Karen Hirsch, Curt Langlotz, Joanna Nelson, William Hiesinger

Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-answering.

Decision Making Dialogue Generation +4

Zero-shot causal learning

1 code implementation NeurIPS 2023 Hamed Nilforoshan, Michael Moor, Yusuf Roohani, Yining Chen, Anja Šurina, Michihiro Yasunaga, Sara Oblak, Jure Leskovec

There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it.

Marketing Meta-Learning

Topological Graph Neural Networks

1 code implementation ICLR 2022 Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten Borgwardt

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.

Graph Learning Node Classification

Challenging Euclidean Topological Autoencoders

1 code implementation NeurIPS Workshop TDA_and_Beyond 2020 Michael Moor, Max Horn, Karsten Borgwardt, Bastian Rieck

Topological autoencoders (TopoAE) have demonstrated their capabilities for performing dimensionality reduction while at the same time preserving topological information of the input space.

Dimensionality Reduction

Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score

1 code implementation2 Jul 2020 Zhiliang Wu, Yinchong Yang, Yunpu Ma, Yushan Liu, Rui Zhao, Michael Moor, Volker Tresp

Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups.

Path Imputation Strategies for Signature Models of Irregular Time Series

2 code implementations25 May 2020 Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, Bastian Rieck

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.

Imputation Irregular Time Series +2

Set Functions for Time Series

2 code implementations ICML 2020 Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten Borgwardt

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.

Time Series Time Series Analysis +1

Topological Autoencoders

2 code implementations ICML 2020 Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt

We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders.

Topological Data Analysis

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping

2 code implementations5 Feb 2019 Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten Borgwardt

This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner based on time series distances.

Dynamic Time Warping Management +3

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