Explaining an increase in predicted risk for clinical alerts

Hardt, Michaela; Rajkomar, Alvin; Flores, Gerardo; Dai, Andrew M.; Howell, Michael D.; Corrado, Greg S.; Cui, Claire; Hardt, Moritz

doi:10.1145/3368555.3384460

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…The interpretability of the time series forecasting model is of great importance for many downstream tasks [88,38,44]. Through disentangling the latent variables of the generative model, not only the interpretability but also the reliability of the prediction can be further enhanced [64].…”

Section: Disentangling Latent Variables For Interpretationmentioning

confidence: 99%

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

Li¹,

Lu²,

Wang³

et al. 2023

Preprint

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Time series forecasting has been a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. In this work, we propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D 3 VAE. Specifically, a coupled diffusion probabilistic model is proposed to augment the time series data without increasing the aleatoric uncertainty and implement a more tractable inference process with BVAE. To ensure the generated series move toward the true target, we further propose to adapt and integrate the multiscale denoising score matching into the diffusion process for time series forecasting. In addition, to enhance the interpretability and stability of the prediction, we treat the latent variable in a multivariate manner and disentangle them on top of minimizing total correlation. Extensive experiments on synthetic and real-world data show that D 3 VAE outperforms competitive algorithms with remarkable margins. Our implementation is available at https://github.com/ PaddlePaddle/PaddleSpatial/tree/main/research/D3VAE.

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Section: Disentangling Latent Variables For Interpretationmentioning

confidence: 99%

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

Li¹,

Lu²,

Wang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…33 constructs synthetic datasets with feature importance labels for evaluation. 34 obtains feature importance labels from both manually constructed tasks and domain experts. 35 derives importance labels from tasks with graph-valued data with computable ground truths.…”

Section: Evaluation Of Feature Importance Interpretationmentioning

confidence: 99%

MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

Meng

Trinh

et al. 2021

Preprint

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The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of dataset representation bias as well as interpretability and prediction fairness of deep learning models for in-hospital mortality prediction. In terms of interpretability, we observe that (1) the best-performing interpretability method successfully identifies critical features for mortality prediction on various prediction models; (2) demographic features are important for prediction. In terms of fairness, we observe that (1) there exists disparate treatment in prescribing mechanical ventilation among patient groups across ethnicity, gender and age; (2) all of the studied mortality predictors are generally fair while the IMV-LSTM (Interpretable Multi-Variable Long Short-Term Memory) model provides the most accurate and unbiased predictions across all protected groups. We further draw concrete connections between interpretability methods and fairness metrics by showing how feature importance from interpretability methods can be beneficial in quantifying potential disparities in mortality predictors.

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“…Recent work [23,30,31] have identified some limitations in time series interpretability. We provide the first benchmark that systematically evaluates different saliency methods across multiple neural architectures in a multivariate time series setting, identifies common limitations, and proposes a solution to adapt existing methods to time series.…”

Section: Background and Related Workmentioning

confidence: 99%

Benchmarking Deep Learning Interpretability in Time Series Predictions

Ismail,

Gunady,

Bravo

et al. 2020

Preprint

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Saliency methods are used extensively to highlight the importance of input features in model predictions. These methods are mostly used in vision and language tasks, and their applications to time series data is relatively unexplored. In this paper, we set out to extensively compare the performance of various saliency-based interpretability methods across diverse neural architectures, including Recurrent Neural Network, Temporal Convolutional Networks, and Transformers in a new benchmark † of synthetic time series data. We propose and report multiple metrics to empirically evaluate the performance of saliency methods for detecting feature importance over time using both precision (i.e., whether identified features contain meaningful signals) and recall (i.e., the number of features with signal identified as important). Through several experiments, we show that (i) in general, network architectures and saliency methods fail to reliably and accurately identify feature importance over time in time series data, (ii) this failure is mainly due to the conflation of time and feature domains, and (iii) the quality of saliency maps can be improved substantially by using our proposed two-step temporal saliency rescaling (TSR) approach that first calculates the importance of each time step before calculating the importance of each feature at a time step.

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Explaining an increase in predicted risk for clinical alerts

Cited by 7 publications

References 38 publications

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

Benchmarking Deep Learning Interpretability in Time Series Predictions

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