# This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text

arXiv: https://arxiv.org/abs/2210.08500 · HTML: https://mlsanity.com/paper/2210.08500/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [A Diagnostic Study of Explainability Techniques for Text Classification](https://mlsanity.com/paper/2009.13295/) (2020)
- [Interpretable and Steerable Sequence Learning via Prototypes](https://mlsanity.com/paper/1907.09728/) (2019)
- [Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/2112.07869/) (2021)
- [Investigating the influence of noise and distractors on the interpretation of neural networks](https://mlsanity.com/paper/1611.07270/) (2016)
- [Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection](https://mlsanity.com/paper/1910.11621/) (2019)
- [Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration](https://mlsanity.com/paper/2102.04110/) (2021)
- [Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation](https://mlsanity.com/paper/2010.15728/) (2020)
- [Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment](https://mlsanity.com/paper/1709.09587/) (2017)
- [ProtoTEx: Explaining Model Decisions with Prototype Tensors](https://mlsanity.com/paper/2204.05426/) (2022)
- [What Do You See in this Patient? Behavioral Testing of Clinical NLP Models](https://mlsanity.com/paper/2111.15512/) (2021)
- [An Analysis of Attention over Clinical Notes for Predictive Tasks](https://mlsanity.com/paper/1904.03244/) (2019)

## Built on by (most cited first, 6 of 6)

- [Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models](https://mlsanity.com/paper/2311.01732/) (2023)
- [Robust Text Classification: Analyzing Prototype-Based Networks](https://mlsanity.com/paper/2311.06647/) (2023)
- [Regulation of Language Models With Interpretability Will Likely Result In A Performance Trade-Off](https://mlsanity.com/paper/2412.12169/) (2024)
- [CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models](https://mlsanity.com/paper/2509.26136/) (2025)
- [Evaluation and LLM-Guided Learning of ICD Coding Rationales](https://mlsanity.com/paper/2508.16777/) (2025)
- [Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs](https://mlsanity.com/paper/2509.09699/) (2025)

## Abstract (arXiv)

The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention with both of these abilities. ProtoPatient makes predictions based on parts of the text that are similar to prototypical patients - providing justifications that doctors understand. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines. Quantitative and qualitative evaluations with medical doctors further demonstrate that the model provides valuable explanations for clinical decision support.