Patient name extraction using MedSpacy - spacy

Question

I was looking for some guidence on NER using medspacy. Aware of disease extraction using MedSpacy but the aim is to extract patient name from medical report using medspacy.
Text supposed to be :
patient:Jeromy, David (DOB)
Date range 2020 to 2022. Visited Dr Brian. Suffered from ...
This type of dataset is there, want to extract patient name from all the pages of medical reports using MedSpacy. I know target rules can be helpful but any clarified guidence will be appreciated.
Thanks & regards

Answer 1

If you find that the default SpaCy NER model is not sufficient, as it will not pick up names such as "Byrn, John", I have a couple of suggestions:
Train a custom NER component using SpaCy's Prodigy annotation tool, which you can use to easily label some examples of names. This is a rather simple task, so you can likely train a model with less than 100 diverse examples. Note: Prodigy is a paid tool, so see my other suggestions if you do not have access/are not willing to pay.
Train a custom NER component without Prodigy. Similar to the above approach, but slightly more involved. This Medium article provides a beginner-friendly introduction to doing so, and you can also refer to SpaCy's own documentation. You can provide SpaCy with some examples of texts and the entities you want extracted, like so:
TRAIN_DATA = [
('Patient: Byrn, John', {
'entities': [(9, 19, 'PERSON')]
}),
('John Byrn received 10mg of advil', {
'entities': [(0, 10, 'PERSON')]
})
]
Build rules based on existing SpaCy components. You can leverage existing SpaCy pipeline components (you don't necessarily need MedSpaCy for this), such as POS tagging and Dependency Parsing. For example, you can look for proper nouns in your documents to identify names. Check out the docs on POS tagging here.
Try other pretrained NER models. There may be other models that are better suited to your task. Check out other models on SpaCy Universe, or even better, on HuggingFaceHub, which contains some of the best models out there for every use case. Added bonus of HF Hub is that you can try out the models on each model model page, and assess the performance on some examples before you decide.
Hope this helps!