# Clinical Intervention Prediction and Understanding using Deep Networks

arXiv: https://arxiv.org/abs/1705.08498 · HTML: https://mlsanity.com/paper/1705.08498/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 30 of 35)

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- [Data-Centric Foundation Models in Computational Healthcare: A Survey](https://mlsanity.com/paper/2401.02458/) (2024)
- [Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attention](https://mlsanity.com/paper/2305.02504/) (2023)
- [Semi-Markov Offline Reinforcement Learning for Healthcare](https://mlsanity.com/paper/2203.09365/) (2022)
- [MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset](https://mlsanity.com/paper/2102.06761/) (2021)
- [Interpretation of Time-Series Deep Models: A Survey](https://mlsanity.com/paper/2305.14582/) (2023)
- [Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations](https://mlsanity.com/paper/2201.08186/) (2022)
- [TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation](https://mlsanity.com/paper/2506.05035/) (2025)
- [Feature Importance Explanations for Temporal Black-Box Models](https://mlsanity.com/paper/2102.11934/) (2021)
- [Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing](https://mlsanity.com/paper/2108.12510/) (2021)
- [CAFO: Feature-Centric Explanation on Time Series Classification](https://mlsanity.com/paper/2406.01833/) (2024)
- [Explanation Space: A New Perspective into Time Series Interpretability](https://mlsanity.com/paper/2409.01354/) (2024)
- [Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records](https://mlsanity.com/paper/2306.03179/) (2023)
- [On the Consistency and Robustness of Saliency Explanations for Time Series Classification](https://mlsanity.com/paper/2309.01457/) (2023)
- [SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training](https://mlsanity.com/paper/2311.05143/) (2023)
- [Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises](https://mlsanity.com/paper/2506.12808/) (2025)
- [DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values](https://mlsanity.com/paper/2507.02342/) (2025)
- [OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction](https://mlsanity.com/paper/2604.16878/) (2026)
- [Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System](https://mlsanity.com/paper/2108.10818/) (2021)
- [Interpreting Time Series Transformer Models and Sensitivity Analysis of Population Age Groups to COVID-19 Infections](https://mlsanity.com/paper/2401.15119/) (2024)
- [Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions](https://mlsanity.com/paper/2506.19035/) (2025)
- [Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Results](https://mlsanity.com/paper/2603.02948/) (2026)
- [Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts](https://mlsanity.com/paper/2206.00679/) (2022)
- [CGS-Mask: Making Time Series Predictions Intuitive for All](https://mlsanity.com/paper/2312.09513/) (2023)
- [WinTSR: A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning Models](https://mlsanity.com/paper/2412.04532/) (2024)
- ["It Felt Like I Was Left in the Dark": Exploring Information Needs and Design Opportunities for Family Caregivers of Older Adult Patients in Critical Care Settings](https://mlsanity.com/paper/2502.05115/) (2025)

## Abstract (arXiv)

Real-time prediction of clinical interventions remains a challenge within intensive care units (ICUs). This task is complicated by data sources that are noisy, sparse, heterogeneous and outcomes that are imbalanced. In this paper, we integrate data from all available ICU sources (vitals, labs, notes, demographics) and focus on learning rich representations of this data to predict onset and weaning of multiple invasive interventions. In particular, we compare both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five intervention tasks: invasive ventilation, non-invasive ventilation, vasopressors, colloid boluses, and crystalloid boluses. Our predictions are done in a forward-facing manner to enable "real-time" performance, and predictions are made with a six hour gap time to support clinically actionable planning. We achieve state-of-the-art results on our predictive tasks using deep architectures. We explore the use of feature occlusion to interpret LSTM models, and compare this to the interpretability gained from examining inputs that maximally activate CNN outputs. We show that our models are able to significantly outperform baselines in intervention prediction, and provide insight into model learning, which is crucial for the adoption of such models in practice.