# AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units

arXiv: https://arxiv.org/abs/2102.04702 · HTML: https://mlsanity.com/paper/2102.04702/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 60 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Structured Inference Networks for Nonlinear State Space Models](https://mlsanity.com/paper/1609.09869/) (2016)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [Estimating Average Treatment Effects via Orthogonal Regularization](https://mlsanity.com/paper/2101.08490/) (2021)
- [Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes](https://mlsanity.com/paper/1610.08853/) (2016)
- [Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure Prediction](https://mlsanity.com/paper/1602.03686/) (2016)
- [Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets](https://mlsanity.com/paper/1710.08531/) (2017)
- [Modeling Irregularly Sampled Clinical Time Series](https://mlsanity.com/paper/1812.00531/) (2018)
- [Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification](https://mlsanity.com/paper/1811.10501/) (2018)
- [Patient Similarity Analysis with Longitudinal Health Data](https://mlsanity.com/paper/2005.06630/) (2020)
- [Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model](https://mlsanity.com/paper/1705.07674/) (2017)
- [Hierarchical Hidden Markov Jump Processes for Cancer Screening Modeling](https://mlsanity.com/paper/1910.05847/) (2019)
- [Estimating Risk-Adjusted Hospital Performance](https://mlsanity.com/paper/2011.05149/) (2020)

## Built on by (most cited first, 14 of 14)

- [Contrastive Learning for Unsupervised Domain Adaptation of Time Series](https://mlsanity.com/paper/2206.06243/) (2022)
- [Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects](https://mlsanity.com/paper/2202.12891/) (2022)
- [Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation](https://mlsanity.com/paper/2310.17463/) (2023)
- [Sequential Deconfounding for Causal Inference with Unobserved Confounders](https://mlsanity.com/paper/2104.09323/) (2021)
- [Generalizing Off-Policy Learning under Sample Selection Bias](https://mlsanity.com/paper/2112.01387/) (2021)
- [Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies](https://mlsanity.com/paper/2112.03013/) (2021)
- [IGC-Net for conditional average potential outcome estimation over time](https://mlsanity.com/paper/2405.21012/) (2024)
- [Stabilized Neural Prediction of Potential Outcomes in Continuous Time](https://mlsanity.com/paper/2410.03514/) (2024)
- [Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine](https://mlsanity.com/paper/2204.07124/) (2022)
- [Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction](https://mlsanity.com/paper/2207.06414/) (2022)
- [Estimating Treatment Effects in Continuous Time with Hidden Confounders](https://mlsanity.com/paper/2302.09446/) (2023)
- [Deep Belief Markov Models for POMDP Inference](https://mlsanity.com/paper/2503.13438/) (2025)
- [Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values](https://mlsanity.com/paper/2211.06045/) (2022)
- [Data-driven subgrouping of patient trajectories with chronic diseases: Evidence from low back pain](https://mlsanity.com/paper/2404.10580/) (2024)

## Abstract (arXiv)

Clinical practice in intensive care units (ICUs) requires early warnings when a patient's condition is about to deteriorate so that preventive measures can be undertaken. To this end, prediction algorithms have been developed that estimate the risk of mortality in ICUs. In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs. Specifically, we develop an attentive deep Markov model called AttDMM. To the best of our knowledge, AttDMM is the first ICU prediction model that jointly learns both long-term disease dynamics (via attention) and different disease states in health trajectory (via a latent variable model). Our evaluations were based on an established baseline dataset (MIMIC-III) with 53,423 ICU stays. The results confirm that compared to state-of-the-art baselines, our AttDMM was superior: AttDMM achieved an area under the receiver operating characteristic curve (AUROC) of 0.876, which yielded an improvement over the state-of-the-art method by 2.2%. In addition, the risk score from the AttDMM provided warnings several hours earlier. Thereby, our model shows a path towards identifying patients at risk so that health practitioners can intervene early and save patient lives.