no code implementations • 24 Dec 2015 • Hugh Chen, Yusuf Erol, Eric Shen, Stuart Russell
One of the biggest flaws in the medical system is perhaps an unexpected one: the patient alarm system.
no code implementations • 9 Oct 2017 • Hugh Chen, Scott Lundberg, Su-In Lee
We present the checkpoint ensembles method that can learn ensemble models on a single training process.
no code implementations • 2 Dec 2017 • Gabriel Erion, Hugh Chen, Scott M. Lundberg, Su-In Lee
We also provide a simple way to visualize the reason why a patient's risk is low or high by assigning weight to the patient's past blood oxygen values.
no code implementations • 23 Jan 2018 • Hugh Chen, Scott Lundberg, Su-In Lee
In this paper, we present feature learning via long short term memory (LSTM) networks and prediction via gradient boosting trees (XGB).
no code implementations • ICLR 2019 • Hugh Chen, Scott Lundberg, Gabe Erion, Su-In Lee
Here, we present the PHASE (PHysiologicAl Signal Embeddings) framework, which consists of three components: i) learning neural network embeddings of physiological signals, ii) predicting outcomes based on the learned embedding, and iii) interpreting the prediction results by estimating feature attributions in the "stacked" models (i. e., feature embedding model followed by prediction model).
2 code implementations • 11 May 2019 • Scott M. Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, Su-In Lee
3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction.
no code implementations • 27 Nov 2019 • Hugh Chen, Scott Lundberg, Su-In Lee
In healthcare, making the best possible predictions with complex models (e. g., neural networks, ensembles/stacks of different models) can impact patient welfare.
no code implementations • 12 Feb 2020 • Hugh Chen, Scott Lundberg, Gabe Erion, Jerry H. Kim, Su-In Lee
Here, we present a transferable embedding method (i. e., a method to transform time series signals into input features for predictive machine learning models) named PHASE (PHysiologicAl Signal Embeddings) that enables us to more accurately forecast adverse surgical outcomes based on physiological signals.
no code implementations • 29 Jun 2020 • Hugh Chen, Joseph D. Janizek, Scott Lundberg, Su-In Lee
Furthermore, we argue that the choice comes down to whether it is desirable to be true to the model or true to the data.
no code implementations • 30 Apr 2021 • Hugh Chen, Scott M. Lundberg, Su-In Lee
Local feature attribution methods are increasingly used to explain complex machine learning models.
1 code implementation • 15 Jul 2022 • Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee
Based on the various feature removal approaches, we describe the multiple types of Shapley value feature attributions and methods to calculate each one.
1 code implementation • 30 Sep 2022 • Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee
To address this, we propose contrastive corpus similarity, a novel and semantically meaningful scalar explanation output based on a reference corpus and a contrasting foil set of samples.