# Temporal Phenotyping using Deep Predictive Clustering of Disease Progression

arXiv: https://arxiv.org/abs/2006.08600 · HTML: https://mlsanity.com/paper/2006.08600/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 26 of 26)

- [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021)
- [Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression](https://mlsanity.com/paper/2312.05933/) (2023)
- [T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression](https://mlsanity.com/paper/2302.12619/) (2023)
- [Fast and Multi-aspect Mining of Complex Time-stamped Event Streams](https://mlsanity.com/paper/2303.03789/) (2023)
- [TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine](https://mlsanity.com/paper/2301.12260/) (2023)
- [Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes](https://mlsanity.com/paper/2111.06152/) (2021)
- [SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series](https://mlsanity.com/paper/2205.15875/) (2022)
- [Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling](https://mlsanity.com/paper/2207.11846/) (2022)
- [Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology](https://mlsanity.com/paper/2305.16656/) (2023)
- [DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation](https://mlsanity.com/paper/2503.22257/) (2025)
- [Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks](https://mlsanity.com/paper/2112.07239/) (2021)
- [Interpreting deep embeddings for disease progression clustering](https://mlsanity.com/paper/2307.06060/) (2023)
- [Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation](https://mlsanity.com/paper/2403.08838/) (2024)
- [Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams](https://mlsanity.com/paper/2601.04741/) (2026)
- [Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data](https://mlsanity.com/paper/2102.11872/) (2021)
- [Temporal Clustering with External Memory Network for Disease Progression Modeling](https://mlsanity.com/paper/2109.14147/) (2021)
- [Phenotyping with Positive Unlabelled Learning for Genome-Wide Association Studies](https://mlsanity.com/paper/2202.07451/) (2022)
- [LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records](https://mlsanity.com/paper/2305.11407/) (2023)
- [Pain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction](https://mlsanity.com/paper/2310.06075/) (2023)
- [Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint](https://mlsanity.com/paper/2402.03388/) (2024)
- [Data-driven subgrouping of patient trajectories with chronic diseases: Evidence from low back pain](https://mlsanity.com/paper/2404.10580/) (2024)
- [Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes](https://mlsanity.com/paper/2405.03327/) (2024)
- [Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis](https://mlsanity.com/paper/2407.11427/) (2024)
- [Subjective Behaviors and Preferences in LLM: Language of Browsing](https://mlsanity.com/paper/2508.15474/) (2025)
- [AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data](https://mlsanity.com/paper/2510.02558/) (2025)
- [Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture](https://mlsanity.com/paper/2606.28623/) (2026)

## Abstract (arXiv)

Due to the wider availability of modern electronic health records, patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial for patient phenotyping, anticipating patients' prognoses by identifying "similar" patients, and designing treatment guidelines that are tailored to homogeneous patient subgroups. In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e.g., adverse events, the onset of comorbidities). To encourage each cluster to have homogeneous future outcomes, the clustering is carried out by learning discrete representations that best describe the future outcome distribution based on novel loss functions. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks and identifies meaningful clusters that can be translated into actionable information for clinical decision-making.