Datasets:

cristian-untaru
/

symcat-medical-triage-dataset

@@ -1,239 +1,154 @@
----
-language:
-- en
-task_categories:
-- text-classification
-task_ids:
-- multi-class-classification
-pretty_name: SymCAT Medical Triage Dataset
-tags:
-- medical-triage
-- symptom-classification
-- healthcare
-- pre-triage
-- symcat-derived
-- natural-language-processing
-- academic-project
-size_categories:
-- n<1K
----
-# SymCAT Medical Triage Dataset
-This repository contains a SymCAT-derived dataset prepared for symptom-based medical pre-triage text classification.
-The dataset was created and processed as part of a bachelor's thesis project by **Cristian Untaru** at the **West University of Timișoara, Faculty of Informatics**.
-## Dataset Description
-The dataset contains natural-language symptom descriptions labeled into three triage-oriented categories:
-- `self_monitor`
-- `consult_gp`
-- `urgent`
-Each example is generated from SymCAT-derived condition and symptom information and converted into a text classification format suitable for fine-tuning transformer-based language models.
-The dataset is intended for academic and experimental work on medical pre-triage classification. It is not intended to be used as a standalone clinical dataset or as a source for real-world medical decision-making.
-## Intended Use
-This dataset can be used for:
-- fine-tuning text classification models for symptom-based pre-triage;
-- comparing transformer-based models such as DistilBERT, BioBERT, and RoBERTa;
-- academic experiments in natural language processing for healthcare;
-- prototyping medical pre-triage assistant systems.
-The dataset was used to fine-tune transformer-based triage classification models in the broader pre-triage assistant project.
-## Labels
-| Label | Meaning |
-|---|---|
-| `self_monitor` | The symptoms may be monitored by the patient, assuming no worsening or additional warning signs. |
-| `consult_gp` | The patient should consider consulting a general practitioner or a non-emergency medical professional. |
-| `urgent` | The symptoms may require urgent medical attention or emergency evaluation. |
-The label mapping is provided in `label_map.json`.
-## Dataset Splits
-The dataset is provided using stratified train, validation, and test splits.
-| Split | File | Number of examples |
-|---|---|---:|
-| Train | `train.csv` | 490 |
-| Validation | `validation.csv` | 105 |
-| Test | `test.csv` | 106 |
-| Full dataset | `full.csv` | 701 |
-The split ratio is approximately 70% train, 15% validation, and 15% test.
-## Class Distribution
-The class distribution in the full dataset is:
-| Class | Number of examples |
-|---|---:|
-| `self_monitor` | 220 |
-| `consult_gp` | 266 |
-| `urgent` | 215 |
-Additional dataset statistics:
-| Statistic | Value |
-|---|---:|
-| Total examples | 701 |
-| Duplicate texts | 0 |
-| Imbalance ratio | 1.237 |
-| SymCAT-derived conditions before final subset selection | 801 |
-| Condition-level overrides applied before final subset selection | 75 |
-## Data Fields
-The CSV files include the following fields:
-| Field | Description |
-|---|---|
-| `condition_name` | Name of the medical condition associated with the generated example. |
-| `condition_slug` | Normalized condition identifier. |
-| `symptom_names` | List of symptom names associated with the condition. |
-| `symptom_slugs` | Normalized symptom identifiers. |
-| `symptom_probabilities` | Symptom probabilities extracted from the SymCAT-derived source. |
-| `text` | Natural-language input text used for classification. |
-| `num_symptoms` | Number of symptoms included in the generated text. |
-| `max_symptom_probability` | Maximum symptom probability for the example. |
-| `mean_symptom_probability` | Mean symptom probability for the example. |
-| `urgent_score` | Rule-based urgency score used during weak labeling. |
-| `gp_score` | Rule-based general-practitioner consultation score used during weak labeling. |
-| `self_score` | Rule-based self-monitoring score used during weak labeling. |
-| `label` | Final triage label. |
-| `override_applied` | Whether a condition-level override was applied during labeling. |
-## Labeling Methodology
-SymCAT does not provide direct triage labels. Therefore, the triage labels in this dataset were derived using a weak-supervision approach based on symptom-level risk cues and condition-level overrides.
-The labeling process assigns each example to one of three triage categories:
-1. `urgent`
-2. `consult_gp`
-3. `self_monitor`
-The general logic is:
-- symptoms or conditions associated with emergency warning signs are assigned to `urgent`;
-- symptoms that suggest the need for non-emergency medical evaluation are assigned to `consult_gp`;
-- lower-risk symptom combinations are assigned to `self_monitor`.
-Condition-level overrides were applied in selected cases where a condition required a stronger triage label than the symptom-level rules alone would suggest.
-This means that the labels are suitable for academic experimentation and model comparison, but they should not be interpreted as clinically validated triage decisions.
-## Files
-| File | Description |
-|---|---|
-| `train.csv` | Training split used for fine-tuning. |
-| `validation.csv` | Validation split used during model selection and early stopping. |
-| `test.csv` | Held-out test split used for final evaluation. |
-| `full.csv` | Complete processed dataset before splitting into train, validation, and test files. |
-| `label_map.json` | Mapping between labels and numeric class IDs. |
-| `dataset_stats.json` | Dataset-level statistics generated during preprocessing. |
-| `.gitattributes` | Git LFS configuration automatically used by Hugging Face/Git. |
-| `README.md` | Dataset Card documentation. |
-## Relationship to MedQuAD
-This dataset is separate from MedQuAD.
-The SymCAT-derived dataset in this repository was used for fine-tuning triage classification models. MedQuAD was processed separately in the broader project as a retrieval corpus for contextual medical question-answer information.
-The processed MedQuAD retrieval dataset is available separately as:
-[`cristian-untaru/medquad-retrieval-pretriage`](https://huggingface.co/datasets/cristian-untaru/medquad-retrieval-pretriage)
-Therefore:
-- SymCAT-derived dataset: used for training/fine-tuning triage classifiers;
-- MedQuAD retrieval dataset: used for contextual retrieval of medical Q&A information;
-- the triage classifier is not fine-tuned on MedQuAD.
-This distinction is important because the SymCAT-derived dataset provides supervised triage labels, while MedQuAD provides medical question-answer context that can be retrieved and displayed alongside the classifier prediction.
-## How to Load the Dataset
-The dataset can be loaded with the Hugging Face `datasets` library:
-```python
-from datasets import load_dataset
-dataset = load_dataset("cristian-untaru/symcat-medical-triage-dataset")
-print(dataset)
-print(dataset["train"][0])
-```
-Expected splits:
-```text
-train
-validation
-test
-```
-The repository also includes `full.csv`, which contains the complete processed dataset before splitting.
-## Example Record
-A typical record contains a generated symptom-based text and its corresponding triage label.
-Example structure:
-```text
-text: Condition: Example condition. Patient reports: symptom 1, symptom 2, symptom 3.
-label: consult_gp
-```
-The exact fields available in the CSV files are described in the **Data Fields** section.
-## Related Dataset and Model Repositories
-Related dataset repositories:
-- [`cristian-untaru/symcat-medical-triage-dataset`](https://huggingface.co/datasets/cristian-untaru/symcat-medical-triage-dataset)
-- [`cristian-untaru/medquad-retrieval-pretriage`](https://huggingface.co/datasets/cristian-untaru/medquad-retrieval-pretriage)
-Related model repositories:
-- [`cristian-untaru/distilbert-medical-triage`](https://huggingface.co/cristian-untaru/distilbert-medical-triage)
-Additional BioBERT and RoBERTa model repositories may be added separately after training and publication.
-## Limitations
-This dataset has several important limitations:
-- It is a small academic dataset.
-- The labels were produced using weak supervision and rule-based triage logic.
-- It should not be treated as a clinically validated triage dataset.
-- It does not replace professional medical judgment.
-- It does not include patient history, age, vital signs, physical examination findings, comorbidities, medication history, or laboratory results.
-- Some generated examples may be simplified and may not reflect the full complexity of real patient descriptions.
-- The dataset is intended for academic experimentation and prototype development, not real-world clinical deployment.
-- The dataset is derived from SymCAT-based condition and symptom information, and users should verify any source-specific usage requirements before reuse.
-## Medical Disclaimer
-This dataset is intended only for academic, research, and prototype development purposes.
-It must not be used as the sole basis for medical diagnosis, treatment, triage, or emergency decision-making. In real-world scenarios, medical triage should be performed by qualified healthcare professionals.
-In case of severe, worsening, or life-threatening symptoms, users should contact emergency medical services or a qualified healthcare professional.
-## Author
-**Cristian Untaru**
-Faculty of Informatics
-West University of Timișoara

+---
+language:
+- en
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+pretty_name: SymCAT Medical Triage Dataset
+tags:
+- medical-triage
+- symptom-classification
+- healthcare
+- pre-triage
+- symcat-derived
+- natural-language-processing
+- academic-project
+size_categories:
+- n<1K
+---
+# SymCAT Medical Triage Dataset
+## Dataset Description
+This repository contains a SymCAT-derived dataset prepared for symptom-based medical pre-triage text classification in the SortMed academic project.
+The dataset contains English symptom descriptions labeled into three triage-oriented categories:
+- `self_monitor`
+- `consult_gp`
+- `urgent`
+It is intended for academic experimentation with transformer-based text classifiers and parameter-efficient fine-tuning methods. It is not a clinically validated medical dataset.
+## Dataset Sources and Role
+The dataset was generated from SymCAT-derived condition and symptom information and converted into a supervised text-classification format.
+In the SortMed system, this dataset is used for training and evaluating the triage classification models. It is separate from the MedQuAD retrieval dataset, which is used only for retrieving related medical information.
+## Dataset Structure
+| Split | File | Examples |
+|---|---|---:|
+| Train | `train.csv` | 490 |
+| Validation | `validation.csv` | 105 |
+| Test | `test.csv` | 106 |
+| Full dataset | `full.csv` | 701 |
+## Label Mapping
+| Label | ID | Meaning |
+|---|---:|---|
+| `self_monitor` | 0 | Symptoms appear mild and may be monitored, assuming no worsening or additional warning signs. |
+| `consult_gp` | 1 | A general practitioner or non-emergency medical professional should be consulted. |
+| `urgent` | 2 | Symptoms may require urgent medical attention or emergency evaluation. |
+## Class Distribution
+| Class | Examples |
+|---|---:|
+| `self_monitor` | 220 |
+| `consult_gp` | 266 |
+| `urgent` | 215 |
+Additional statistics:
+| Statistic | Value |
+|---|---:|
+| Total examples | 701 |
+| Duplicate texts | 0 |
+| Imbalance ratio | 1.237 |
+| SymCAT-derived conditions before final subset selection | 801 |
+| Condition-level overrides applied before final subset selection | 75 |
+## Files and Columns
+| File | Description |
+|---|---|
+| `train.csv` | Training split. |
+| `validation.csv` | Validation split used for checkpoint selection and early stopping. |
+| `test.csv` | Held-out test split used for final evaluation. |
+| `full.csv` | Complete dataset. |
+| `label_map.json` | Mapping between labels and numeric IDs. |
+| `dataset_stats.json` | Dataset statistics used for documentation and reproducibility. |
+| `README.md` | Dataset card documentation. |
+Main columns include:
+| Column | Description |
+|---|---|
+| `text` | Natural-language input text used by the classifier. |
+| `label` | Triage class name. |
+| `label_numeric` | Numeric class ID. |
+| `condition_name` | Source condition name, if available. |
+| `symptom_names` | Symptoms associated with the generated example. |
+## Preprocessing
+The dataset was generated from condition and symptom information, then transformed into short English symptom descriptions suitable for text classification.
+Labels were assigned using deterministic rule-based triage logic designed for academic pre-triage experiments. The labels are weakly supervised and should not be interpreted as expert clinical annotations.
+## Intended Use
+This dataset can be used for:
+- training medical pre-triage text classifiers;
+- evaluating transformer-based classification models;
+- comparing full fine-tuning with PEFT methods such as LoRA, Bottleneck MLP Adapter, and Frozen Encoder;
+- academic experiments in NLP for healthcare.
+## Limitations
+- The labels are rule-based and not clinically validated.
+- The dataset is small.
+- It may not represent real patient language or demographic diversity.
+- It should not be used as a standalone clinical dataset.
+- It does not include patient history, vital signs, comorbidities, or physical examination findings.
+- It is designed for English text only.
+## Related Datasets
+- [`cristian-untaru/medquad-retrieval-pretriage`](https://huggingface.co/datasets/cristian-untaru/medquad-retrieval-pretriage)
+## Related Models
+### Full Fine-Tuned Models
+- [`cristian-untaru/distilbert-medical-triage`](https://huggingface.co/cristian-untaru/distilbert-medical-triage)
+- [`cristian-untaru/biobert-medical-triage`](https://huggingface.co/cristian-untaru/biobert-medical-triage)
+- [`cristian-untaru/roberta-medical-triage`](https://huggingface.co/cristian-untaru/roberta-medical-triage)
+- [`cristian-untaru/biomedbert-medical-triage`](https://huggingface.co/cristian-untaru/biomedbert-medical-triage)
+### LoRA Models
+- [`cristian-untaru/lora-distilbert-medical-triage`](https://huggingface.co/cristian-untaru/lora-distilbert-medical-triage)
+- [`cristian-untaru/lora-biobert-medical-triage`](https://huggingface.co/cristian-untaru/lora-biobert-medical-triage)
+- [`cristian-untaru/lora-roberta-medical-triage`](https://huggingface.co/cristian-untaru/lora-roberta-medical-triage)
+- [`cristian-untaru/lora-biomedbert-medical-triage`](https://huggingface.co/cristian-untaru/lora-biomedbert-medical-triage)
+### Bottleneck MLP Adapter Models
+- [`cristian-untaru/bottleneck-mlp-distilbert-medical-triage`](https://huggingface.co/cristian-untaru/bottleneck-mlp-distilbert-medical-triage)
+- [`cristian-untaru/bottleneck-mlp-biobert-medical-triage`](https://huggingface.co/cristian-untaru/bottleneck-mlp-biobert-medical-triage)
+- [`cristian-untaru/bottleneck-mlp-roberta-medical-triage`](https://huggingface.co/cristian-untaru/bottleneck-mlp-roberta-medical-triage)
+- [`cristian-untaru/bottleneck-mlp-biomedbert-medical-triage`](https://huggingface.co/cristian-untaru/bottleneck-mlp-biomedbert-medical-triage)
+### Frozen Encoder Models
+- [`cristian-untaru/frozen-encoder-distilbert-medical-triage`](https://huggingface.co/cristian-untaru/frozen-encoder-distilbert-medical-triage)
+- [`cristian-untaru/frozen-encoder-biobert-medical-triage`](https://huggingface.co/cristian-untaru/frozen-encoder-biobert-medical-triage)
+- [`cristian-untaru/frozen-encoder-roberta-medical-triage`](https://huggingface.co/cristian-untaru/frozen-encoder-roberta-medical-triage)
+- [`cristian-untaru/frozen-encoder-biomedbert-medical-triage`](https://huggingface.co/cristian-untaru/frozen-encoder-biomedbert-medical-triage)