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Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
Zhengping Che, Sanjay Purushotham, Robinder Khemani, Yan Liu
TL;DR
EHR phenotyping needs robust prediction and interpretable features for clinical decision-making. The paper uses Gradient Boosting Trees to distill knowledge from deep models, reporting similar or better predictive performance and clinician-validated interpretable features. Its experiments are preliminary, with evaluation on a real-world dataset and larger-dataset validation planned.
Problem
Deep models perform strongly on computational phenotyping, but their learned features are difficult to interpret even though clinicians rely on interpretable models for decisions.
Source: §1 · pp. 1–2Method
Interpretable Mimic Learning trains Gradient Boosting Trees to learn interpretable features from deep models while mimicking their performance.
Source: §1 · p. 2Results
The mimic methods achieved similar or better performance than deep models on both MOR and VFD classification tasks.
Source: §4.4 · p. 8Takeaways & Limitations
The authors report interpretable features and decision rules learned by the mimic models that expert clinicians validated.
Source: §1 · p. 2The experimental results are preliminary on a real-world dataset; the authors plan evaluation on a larger healthcare dataset such as MIMIC-II.
Source: §5 · p. 10
Abstract
from arXiv · showhide
Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved in decision-making. In this paper, we introduce a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable phenotype features for making robust prediction while mimicking the performance of deep learning models. Our framework uses Gradient Boosting Trees to learn interpretable features from deep learning models such as Stacked Denoising Autoencoder and Long Short-Term Memory. Exhaustive experiments on a real-world clinical time-series dataset show that our method obtains similar or better performance than the deep learning models, and it provides interpretable phenotypes for clinical decision making.
1 Introduction
EHR phenotyping needs robust prediction and interpretable features because clinicians rely on interpretable models for decisions. The paper proposes distilling deep models into Gradient Boosting Trees and evaluates the approach across deep-network architectures.
1 Introduction
- Clinicians need interpretable models for informed decisions, while deep learning has achieved state-of-the-art performance on computational phenotype prediction.The paper frames the central challenge as matching deep-learning performance while discovering interpretable features; robust prediction is described as critical in healthcare research.Source: §1 · pp. 1–2
- The framework builds on mimic learning, in which a smaller model learns from a trained deep model’s soft-target class probabilities.Choosing a suitable simple model may help extract informative physiologic patterns and interpretable features.Source: §1 · p. 2
- Interpretable Mimic Learning uses Gradient Boosting Trees to learn interpretable features from deep models while mimicking their prediction performance.The authors chose GBTs for their interpretable decision rules and tree structures, and their ability to maintain performance of complex models.Source: §1 · p. 2
- Experiments cover Stacked Denoising Autoencoders and Long Short-Term Memory models to evaluate mimic performance across deep architectures.The paper also investigates whether neural-network-extracted features improve mimicking compared with soft labels.Source: §1 · p. 2
- The authors report that expert clinicians validated the interpretable features and decision rules learned by their mimic models.They also compare using neural-network-extracted features against soft labels for different deep networks.Source: §1 · p. 2
2 Related Work
Prior healthcare deep-learning work achieved strong predictive performance but made limited attempts to interpret learned features. Related work motivates interpretable models and mimic learning as routes toward combining predictive performance with interpretability.
2 Related Work
- Healthcare deep-learning studies reported state-of-the-art performance, but made limited attempts to interpret learned features.The paper says this limited interpretability prevents clinicians from understanding and applying these models.Source: §2 · pp. 2–3
- Decision trees are interpretable and used in healthcare, but can overfit and perform poorly with missing values common in healthcare datasets.Deep models offer strong performance, but their learned features are hardly interpretable.Source: §2 · p. 3
- Interpretability research in computer vision found that hidden-unit interpretations may mislead because deep-model behavior can be more complex than assumed.The cited work also studied visualizations and feature generalizability in convolutional networks.Source: §2 · p. 3
- Prior work showed shallow networks can learn the same function as deep networks, while later distillation approaches transferred knowledge into a single student model.The paper cites Bayesian distillation and real-world mimic-learning results as further precedents.Source: §2 · p. 3
3 Methods
The methods define EHR inputs, describe deep-network architectures and gradient boosting, then train GBTs to mimic deep networks through two alternative pipelines. The framework aims to combine interpretable features with deep-network classification performance.
3 Methods
- The methods section presents the notation, deep-learning models as original models, GBTs as interpretable mimics, and the overall framework pipeline.Source: §3 · p. 3
3.1 Notations
- Each EHR sample combines Q static variables with T time steps of P temporal variables and a binary label; flattening and concatenation give D = TP + Q input features.Source: §3.1 · p. 3
3.2 Feedforward Network and Stacked Denosing Autoencoder
- A stacked denoising autoencoder reconstructs its uncorrupted input after random input variables are hidden, then adds a logistic prediction layer to the encoder.Its encoder follows a feedforward structure, while the autoencoder minimizes squared reconstruction loss rather than cross-entropy prediction loss.Source: §3.2 · p. 4
3.3 Long Short-Term Memory
- The LSTM processes temporal inputs using input, forget, and output gates to update cell states and outputs, then flattens outputs across time for prediction.The method applies it to sequences of P temporal variables across T time steps.Source: §3.3 · p. 4
3.4 Gradient Boosting Trees
- Gradient boosting builds a prediction function in stages by fitting weak learners to the loss gradient; its tree learners use shrinkage to limit overfitting.Each weak learner is a simple classification or regression tree, and shrinkage multiplies the stage function by learning rate ν.Source: §3.4 · pp. 4–5
3.5 Interpretable Mimic Learning method
- GBTmimic trains Gradient Boosting Trees to mimic deep-network performance, with two pipelines comparing extracted-feature-derived soft targets against direct neural-network soft labels.Both pipelines train the final mimic from raw inputs; the framework seeks interpretable features while maintaining deep-network classification performance.Source: §3.5 · pp. 5–6
4 Experiments
The experiments compare Interpretable Mimic Learning with deep and conventional models, examine its learned features, and test how input feature sets affect performance. The reported mimic models match or exceed deep-model performance while yielding interpretable features and rules.
4.1 Dataset Descriptions
- VENT contains data from 398 intensive-care patients with acute hypoxemic respiratory failure, including 27 static and 21 daily temporal features from the first four ventilation days.The resulting input vector has 109 features; missingness is 13.43%, with some patient-variable pairs exceeding 30%.Source: §4.1 · p. 6
4.2 Experimental Design
- The experiments use mortality within 60 days and ventilator-free days as binary tasks, with AUC as the evaluation metric.VFD classifies survival with more than 14 ventilator days as the positive outcome; values at or below 14 are bad outcomes.Source: §4.2 · p. 7
4.3 Methods and Implementation Details
- The comparison includes conventional classifiers, DNN, SDA, and LSTM, plus GBT mimics trained on neural-network soft predictions and decision-tree mimics as a comparator.The authors also evaluate logistic regression on the highest-layer activations of the neural networks.Source: §4.3 · p. 7
4.4 Quantitative Results
- GBTmimic-LR-SDA and GBTmimic-LR-DNN had the best MOR and VFD performance, respectively; mimic methods improved on DNN and SDA and were comparable to LSTM methods.Table 2 reports the classification results.Source: §4.4 · p. 8, Table 2 · p. 8
- For MOR, prediction differences between temporal-only and all features were small; for VFD, adding static features was more critical, while static and day-0 temporal inputs degraded performance on both tasks.The authors report that static and day-0 temporal inputs performed worse than temporal-only inputs on all tested models.Source: §4.4 · p. 8, Table 3 · p. 9, Table 4 · p. 9
4.5 Interpretations
- Almost all top-ranked features were temporal; among static features, PIM2 and PRISM were the most useful variables.Table 5 reports top features and importance scores for GBT and GBTmimic models across cross-validation runs.Source: §4.5 · p. 9, Table 5 · p. 10
- On the same MOR cross-validation fold, original GBT and mimic trees shared some features and similar rules that healthcare experts could evaluate and explain.The authors suggest this can help experts understand the models and make better patient decisions.Source: §4.5 · p. 9
- DTmimic methods performed poorly compared with GBTmimic methods, despite the single decision tree being easier to interpret and visualize.The authors note that GBT has several internal tree estimators and can be harder to interpret than a single decision tree.Source: §4.5 · p. 10, Table 6 · p. 10
5 Discussions
The authors report preliminary results showing similar or better performance from their mimic methods on a real-world dataset. They identify richer interpretability and broader evaluation as future work.
5 Discussions
- Preliminary experiments on a real-world dataset show similar or even better performance from the mimic methods.The authors describe this as a promising direction for future healthcare machine-learning research.Source: §5 · p. 10
- Future work will seek more useful decision rules or tree-node features for better diagnosis interpretability.The authors also plan to apply the approaches to a larger healthcare dataset and extend them to other state-of-the-art models.Source: §5 · p. 10
- Figure 3 depicts important decision trees for the MOR task, with each leaf node's value representing a sample prediction score for its decision-rule path.Source: Figure 3 · p. 11
- The authors aim to explore application in difficult practical settings and help domain experts better understand the models.Source: §5 · p. 11