# NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping

arXiv: https://arxiv.org/abs/2602.03562 · HTML: https://mlsanity.com/paper/2602.03562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models](https://mlsanity.com/paper/2005.00683/) (2020)
- [Deep k-Means: Jointly clustering with k-Means and learning representations](https://mlsanity.com/paper/1806.10069/) (2018)
- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [ExBEHRT: Extended Transformer for Electronic Health Records to Predict Disease Subtypes & Progressions](https://mlsanity.com/paper/2303.12364/) (2023)

## Abstract (arXiv)

Electronic Health Records (EHRs) provide high-dimensional temporal data essential for patient modeling; however, conventional algorithmic approaches often rely on data aggregation or imputation, which distorts temporal disease trajectories. Such computational limitations are particularly critical in sepsis, a heterogeneous syndrome where clustering-based stratification plays a key role in identifying clinically distinct phenotypes for precise treatment strategies. Furthermore, existing clustering processes seldom incorporate domain-driven constraints, often resulting in phenotypes that lack clear clinical distinction. We propose a novel clustering network, NPCNet, that comprises a text embedding generator, a clustering operator, and a target navigator. We first transform EHRs into pseudo texts by discretizing continuous clinical measurements, then integrate them with static variables to construct the embeddings. The target navigator then infuses clinical knowledge into the embeddings through auxiliary tasks, constraining clustering results to better align sepsis phenotypes with clinical significance. Finally, the clustering operator employs an iterative refinement mechanism to jointly optimize phenotype centroids and patient representations under domain-driven constraints. Extensive experiments on public datasets validate that NPCNet achieves superior performance on both internal clustering benchmarks and clinical validity metrics, offering a viable pathway for precision treatment strategies in the management of sepsis.