# Patient representation learning and interpretable evaluation using clinical notes

arXiv: https://arxiv.org/abs/1807.01395 · HTML: https://mlsanity.com/paper/1807.01395/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 15 of 15)

- [Towards Faithful Model Explanation in NLP: A Survey](https://mlsanity.com/paper/2209.11326/) (2022)
- [Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration](https://mlsanity.com/paper/2102.04110/) (2021)
- [Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals](https://mlsanity.com/paper/2310.00603/) (2023)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?](https://mlsanity.com/paper/2403.11894/) (2024)
- [GraphNarrator: Generating Textual Explanations for Graph Neural Networks](https://mlsanity.com/paper/2410.15268/) (2024)
- [Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data](https://mlsanity.com/paper/2103.00482/) (2021)
- [Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records](https://mlsanity.com/paper/2306.03179/) (2023)
- [Enriching Unsupervised User Embedding via Medical Concepts](https://mlsanity.com/paper/2203.10627/) (2022)
- [Assessing mortality prediction through different representation models based on concepts extracted from clinical notes](https://mlsanity.com/paper/2207.10872/) (2022)
- [Comparing representations of long clinical texts for the task of patient note-identification](https://mlsanity.com/paper/2503.24006/) (2025)
- [A Study into patient similarity through representation learning from medical records](https://mlsanity.com/paper/2104.14229/) (2021)
- [Applying unsupervised keyphrase methods on concepts extracted from discharge sheets](https://mlsanity.com/paper/2303.08928/) (2023)
- [ReToP: Learning to Rewrite Electronic Health Records for Clinical Prediction](https://mlsanity.com/paper/2601.19286/) (2026)
- [Enhance the after-discharge mortality rate prediction via learning from the medical notes](https://mlsanity.com/paper/2605.03560/) (2026)

## Abstract (arXiv)

We have three contributions in this work: 1. We explore the utility of a stacked denoising autoencoder and a paragraph vector model to learn task-independent dense patient representations directly from clinical notes. To analyze if these representations are transferable across tasks, we evaluate them in multiple supervised setups to predict patient mortality, primary diagnostic and procedural category, and gender. We compare their performance with sparse representations obtained from a bag-of-words model. We observe that the learned generalized representations significantly outperform the sparse representations when we have few positive instances to learn from, and there is an absence of strong lexical features. 2. We compare the model performance of the feature set constructed from a bag of words to that obtained from medical concepts. In the latter case, concepts represent problems, treatments, and tests. We find that concept identification does not improve the classification performance. 3. We propose novel techniques to facilitate model interpretability. To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model. Further, we calculate feature sensitivity across two networks to identify the most significant input features for different classification tasks when we use these pretrained representations as the supervised input. We successfully extract the most influential features for the pipeline using this technique.