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Found 12 papers, 3 papers with code
Contrastive Corpus Attribution for Explaining Representations
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.
Algorithms to estimate Shapley value feature attributions
Based on the various feature removal approaches, we describe the multiple types of Shapley value feature attributions and methods to calculate each one.
Explaining a Series of Models by Propagating Shapley Values
Local feature attribution methods are increasingly used to explain complex machine learning models.
True to the Model or True to the Data?
Furthermore, we argue that the choice comes down to whether it is desirable to be true to the model or true to the data.
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals
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.
Explaining Models by Propagating Shapley Values of Local Components
In healthcare, making the best possible predictions with complex models (e. g., neural networks, ensembles/stacks of different models) can impact patient welfare.
Explainable AI for Trees: From Local Explanations to Global Understanding
3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction.
Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models
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).
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data
In this paper, we present feature learning via long short term memory (LSTM) networks and prediction via gradient boosting trees (XGB).
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning
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.
Checkpoint Ensembles: Ensemble Methods from a Single Training Process
We present the checkpoint ensembles method that can learn ensemble models on a single training process.
Probabilistic Model-Based Approach for Heart Beat Detection
One of the biggest flaws in the medical system is perhaps an unexpected one: the patient alarm system.
