DATEXIS/sproto

S-Proto: Sparse Prototypical Networks for Long-Tail Clinical Diagnosis Prediction

Published at ECML PKDD 2024 (CORE A)
Boosting Long-Tail Data Classification with Sparse Prototypical Networks

Alexei Figueroa*, Jens-Michalis Papaioannou*, et al.
DATEXIS, Berliner Hochschule für Technik, Feinstein Institutes, TU Munich, Leibniz University Hannover
(* equal contribution)

This repository provides S-Proto, a sparse and interpretable prototypical network for extreme multi-label diagnosis prediction from clinical text. The model is designed to address the long-tail distribution of clinical diagnoses while preserving faithful, prototype-based explanations.

Interactive Demo

You can explore the model's predictions and interpretability features through our interactive web demo: https://s-proto.demo.datexis.com/

S-Proto was introduced in the paper:

Boosting Long-Tail Data Classification with Sparse Prototypical Networks European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024, CORE A)
Alexei Figueroa*, Jens-Michalis Papaioannou*, et al.
DATEXIS, Berliner Hochschule für Technik, Feinstein Institutes, TU Munich, Leibniz University Hannover
(* equal contribution)

Overview

Clinical outcome prediction from Electronic Health Records is characterized by extreme label imbalance. A small number of diagnoses account for most patients, while the majority of diagnoses appear rarely. Standard transformer classifiers tend to perform well on frequent diagnoses but degrade sharply in the long tail.

S-Proto addresses this problem by extending prototypical networks with:

• Multiple prototypes per diagnosis
• Sparse winner-takes-all activation
• Prototype-level interpretability
• Efficient training despite increased representational capacity

The model achieves state-of-the-art performance on MIMIC-IV diagnosis prediction, with particularly strong gains in PR-AUC for rare diagnoses, and transfers successfully to unseen clinical datasets.

Model Architecture

S-Proto builds on PubMedBERT as the text encoder and introduces a sparse prototypical layer on top.

For each diagnosis label, the model learns multiple sub-networks, each consisting of:

• A label-specific attention vector
• A prototype vector representing a prototypical patient

Given an input clinical note:

1. The note is encoded using PubMedBERT
2. Token embeddings are projected into a latent space
3. Each diagnosis activates multiple candidate sub-networks
4. A winner-takes-all mechanism selects the single most relevant sub-network per diagnosis
5. Only the winning prototype contributes to the prediction and receives gradient updates

This allows S-Proto to model heterogeneous disease phenotypes while remaining sparse and efficient.

Intended Use

This model is intended for:

• Clinical diagnosis prediction from admission notes
• Research on long-tail learning in healthcare NLP
• Interpretable clinical decision support systems
• Analysis of disease phenotypes via learned prototypes

This model is not intended for direct clinical deployment without external validation, auditing, and regulatory approval.

Inference Example

from transformers import AutoTokenizer, AutoModel
import torch

tokenizer = AutoTokenizer.from_pretrained(
    "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext"
)
model = AutoModel.from_pretrained(
    "DATEXIS/sproto",
    trust_remote_code=True
)
model.eval()

text_input = [
    "CHIEF COMPLAINT: Right Carotid Artery Stenosis. "
    "PRESENT ILLNESS: Ms. ___ is a ___ year old woman with hyperlipidemia, "
    "cirrhosis with esophageal varices, alcoholism, COPD, left eye blindness, "
    "and right carotid stenosis status post right carotid endarterectomy."
]

inputs = tokenizer(
    text_input,
    padding=True,
    truncation=True,
    max_length=512,
    return_tensors="pt"
)

tokens = [tokenizer.convert_ids_to_tokens(ids) for ids in inputs["input_ids"]]

with torch.no_grad():
    output = model(
        input_ids=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        token_type_ids=inputs.get("token_type_ids"),
        tokens=tokens
    )

logits = output["logits"]
max_indices = output["max_indices"]
metadata = output["metadata"]

print("Inference successful")
print("Logits shape:", logits.shape)
print("Max indices:", max_indices)
print("Metadata:", metadata)

Outputs

The model returns a dictionary with the following entries:

• logits
  Prediction scores per diagnosis label.

• max_indices
  Index of the winning prototype sub-network per diagnosis, corresponding to the selected prototype.

• metadata
  Additional information useful for analysis and interpretability.

Explainability

S-Proto provides built-in faithful explanations through its prototypical structure:

• Attention vectors highlight clinically relevant tokens
• Prototype distances reflect similarity to prototypical patients
• Multiple prototypes per diagnosis capture disease subtypes and cohorts
• Faithfulness metrics remain comparable to ProtoPatient despite higher capacity

Qualitative evaluation with medical professionals confirms that learned prototypes often correspond to clinically meaningful phenotypes.

Training

First, clone the repository:

git clone https://github.com/DATEXIS/sproto.git
cd sproto

Set up the environment using Poetry:

poetry install

Activate the virtual environment:

poetry env activate

Once the environment is active, you can start training by running the train command with the desired arguments.

Example:

train \
  --batch_size 3 \
  --pretrained_model microsoft/biomednlp-pubmedbert-base-uncased-abstract-fulltext \
  --pretrained_model_path path_to_pretrained_model.ckpt \
  --model_type MULTI_PROTO \
  --train_file training_data.csv \
  --val_file validation_data.csv \
  --test_file test_data.csv \
  --save_dir ../experiments/ \
  --gpus 1 \
  --check_val_every_n_epoch 2 \
  --num_warmup_steps 0 \
  --num_training_steps 50 \
  --max_length 512 \
  --lr_features 0.000005 \
  --lr_prototypes 0.001 \
  --lr_others 0.001 \
  --num_val_samples None \
  --use_attention True \
  --reduce_hidden_size 256 \
  --all_labels_path all_labels.pcl \
  --seed 42 \
  --label_column labels \
  --metric_opt auroc_macro \
  --train_files [] \
  --val_files [] \
  --only_test True \
  --model_name 5p \
  --store_metadata False \
  --num_prototypes_per_class 5

Citation

@inproceedings{figueroa2024sproto,
  title={Boosting Long-Tail Data Classification with Sparse Prototypical Networks},
  author={Figueroa, Alexei and Papaioannou, Jens-Michalis and Fallon, Conor and Bekiaridou, Alexandra and Bressem, Keno and Zanos, Stavros and Gers, Felix and Nejdl, Wolfgang and Löser, Alexander},
  booktitle={Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)},
  year={2024}
}

License

This model and its associated code are released under the Apache License 2.0.

The model was trained on the MIMIC-IV dataset, which is subject to restricted access. No training data is included or redistributed with this repository. The data were accessed under a data use agreement. No patient-identifiable information is shared.

Use of this model must comply with all applicable data governance and ethical guidelines.

Limitations

• Extremely rare diagnoses remain challenging
• Clinical dataset biases may be reflected in predictions
• Winner-takes-all selection is fixed and not learned dynamically
• Not validated for real-world clinical deployment

Ethical Considerations

• The model processes sensitive clinical text
• Predictions should always be reviewed by qualified professionals
• Outputs should not be used as sole evidence for clinical decisions
• Care must be taken to avoid reinforcing existing healthcare biases