File size: 1,531 Bytes
fcb23f0 555e705 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | # PD Cognition BioClinicalBERT Fine Tuned spaCy Model
This model is a spaCy pipeline for span categorization trained on clinical text related to Parkinson disease cognition. It uses BioClinicalBERT as the transformer backbone and predicts labeled spans stored under the key sc.
## Model Details
Base model emilyalsentzer Bio ClinicalBERT
Framework spaCy with spacy transformers
Task span categorization
Language English
## Labels
The label set is defined in labels.json included in the model directory.
## Input:
Raw clinical text string
## Output:
Predicted spans in doc.spans["sc"] with start end label and score
## Usage
```python
import spacy
nlp = spacy.load("path_to_model_best")
doc = nlp("Patient shows cognitive decline and memory impairment")
for span in doc.spans["sc"]:
print(span.text, span.label_, span.score)
```
## Files
model best spaCy pipeline
config cfg training configuration
meta json pipeline metadata
labels json list of span labels
## Notes
This is a spaCy model and should be loaded with spacy.load not transformers
Performance depends on span alignment and threshold tuning
Intended for research use on clinical text
## Citation
```bibtex
@article{khanna2024cognitive,
title={Toward Automated Cognitive Assessment in Parkinson’s Disease Using Pretrained Language Models},
author={Khanna, Varada and Bhatt, Nilay and Shin, Ikgyu and Rosso, Mattia and Tinaz, Sule and Ren, Yang and Xu, Hua and Keloth, Vipina K},
journal={arXiv preprint arXiv:2511.08806},
year={2025}
}
``` |