Publications

2018
Jin L, Doshi-Velez F, Miller T, Schuler W, Schwartz L. Depth-bounding is effective: Improvements and Evaluation of Unsupervised PCFG Induction. Conference on Empirical Methods in Natural Language Processing (EMNLP) . 2018. Paper
Doshi-Velez F, Kim B. Considerations for Evaluation and Generalization in Interpretable Machine Learning. In: Escalante H, Escalera S, Guyon I, Baró X, Güçlütürk Y, Güçlü U, van Gerven MAJ Explainable and Interpretable Models in Computer Vision and Machine Learning. 1st ed. Springer International Publishing ; 2018. Chapter
Glueck M, Naeini MP, Doshi-Velez F, Chevalier F, Khan A, Wigdor D, Brudno M. PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models. IEEE Transactions on Visualization and Computer Graphics. 2018;24 (1) :371-381. Paper
2017
Ghassemi M, Wu M, Hughes MC, Szolovits P, Doshi-Velez F. Predicting intervention onset in the ICU with switching state space models. American Medical Informatics Association (AMIA),. 2017. Paper
Glueck M, Naeini MP, Doshi-Velez F, Chevalier F, Khan A, Wigdor D, Brudno M. PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models. Conference on Visual Analytics Science and Technology (VAST),. 2017. Paper
Ross AS, Lage I, Doshi-Velez F. The Neural LASSO: Local Linear Sparsity for Interpretable Explanations. Neural Information Processing Systems (NIPS) Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. 2017. Paper
Wu M, Hughes M, Parbhoo S, Zazzi M, Roth V, Doshi-Velez F. Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. Neural Information Processing Systems (NIPS) Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. 2017. Paper
Parbhoo S, Roth V, Doshi-Velez F. Counterfactual Reasoning with Dynamic Switching Models for HIV Therapy Selection. Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Healthcare. 2017. Paper
Hughes MC, Hope G, Weiner L, McCoy TH, Perlis RH, Sudderth EB, Doshi-Velez F. Prediction-Constrained Topic Models for Antidepressant Recommendation. Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Healthcare. 2017. Paper
Ghosh S, Doshi-Velez F. Model Selection in Bayesian Neural Networks via Horseshoe Priors. Neural Information Processing Systems (NIPS) Workshop on Bayesian Deep Learning. 2017. Paper
Singh R, Ling J, Doshi-Velez F. Structured Variational Autoencoders for the Beta-Bernoulli Process. Neural Information Processing Systems (NIPS) Workshop on Advances in Approximate Bayesian Inference. 2017. Paper
Tan S, Doshi-Velez F, Quiroz J, Glassman E. Clustering LaTeX Solutions to Machine Learning Assignments for Rapid Assessment. Advancing Education with Data Knowledge Discovery and Data Mining (KDD) Workshop. 2017. Paper
Depeweg S, Hernandez-Lobato JM, Doshi-Velez F, Udluft S. Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables. International Conference on Machine Learning (ICML) Workshop. 2017. Paper
Parbhoo S, Bogojeska J, Zazzi M, Roth V, Doshi-Velez F. Combining Kernel and Model Based Learning for HIV Therapy Selection, in AMIA Summits on Translational Science Proceedings . Vol 2017. ; 2017 :239. Paper
Fan A, Doshi-Velez F, Miratrix L. Prior Matters: Simple and General Methods for Evaluating and Improving Topic Quality in Topic Modeling. Text as Data. 2017. Paper
Killian T, Daulton S, Konidaris G, Doshi-Velez F. Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. Neural Information Processing Systems (NIPS). 2017. Paper
Wang T, Rudin C, Doshi-Velez F, Liu Y, Klampfl E, MacNeille P. A Bayesian Framework for Learning Rule Sets for Interpretable Classification. Journal of Machine Learning. 2017;18 (70) :1-37. Paper
Ross AS, Hughes MC, Doshi-Velez F. Right for the Right Reasons: Training Differentiable Models by Constraining their Explananations. International Joint Conference on Artificial Intelligence (IJCAI). 2017. Paper
Doshi-Velez F, Williamson S. Restricted Indian Buffet Processes. Statistics and Computing. 2017;27 (5) :1205-1223. Paper
Wu M, Ghassemi M, Fend M, Celi LA, Szolovits P, Doshi-Velez F. Understanding Vasopressor Intervention and Weaning: Risk Prediction in a Public Heterogeneous Clinical Time Series Database. Journal of the American Medical Informatics Association. 2017;24 (3) :488-495.Abstract

 

Background The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations.

Objective We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes.

Materials and Methods We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning.

Results The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean.

Conclusion Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series.

 

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