SandeepKanao/HL7-FHIR-Model-V1

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license: apache-2.0 language:

• en tags:
• Token Classification co2_eq_emissions: 0.0279399890043426 widget:
• text: ""MSH|^~&|SendingAPP|MYTEST|||20230621090000||ORU^R01|1|P|2.5.1||||||UNICODE PID|1||13579246^^^TEST||Taylor^Michael||19830520|M|||987 Pine St^^Anytown^NY^23456||555-456-7890 PV1|1||bc^^004 OBR|1||13579246|BCD^LEFT Breast Cancer Diagnosis^99MRC||20230621090000|||Taylor^Sarah||20230620090000|||N OBX|1|ST|FINDINGS^Findings^99MRC||Lab report shows asymmetric density in the right breast.|F|||R OBX|2|ST|IMPRESSION^Impression^99MRC||BIRADS category: 4 - Probably left side as issues.|F|||R OBX|3|ST|RECOMMENDATION^Recommendation^99MRC||Follow-up specialit visit in six months.|F|||R"" example_title: "example 1"
• text: "MSH|^~&|SendingAPP|MYTEST|||20230621090000||ORU^R01|1|P|2.5.1||||||UNICODE PID|1||13579246^^^TEST||Taylor^Michael||19830520|M|||987 Pine St^^Anytown^NY^23456||555-456-7890 PV1|1||bc^^004 OBR|1||13579246|BCD^LEFT Breast Cancer Diagnosis^99MRC||20230621090000|||Taylor^Sarah||20230620090000|||N OBX|1|ST|FINDINGS^Findings^99MRC||Lab report shows asymmetric density in the right breast.|F|||R OBX|2|ST|IMPRESSION^Impression^99MRC||BIRADS category: 4 - Probably left side as issues.|F|||R OBX|3|ST|RECOMMENDATION^Recommendation^99MRC||Follow-up specialit visit in six months.|F|||R"

About the Model

An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased

• Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942
• Carbon emission: 0.0279399890043426 Kg
• Training time: 30.16527 minutes
• GPU used : 1 x GeForce RTX 3060 Laptop GPU

Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18

Usage

The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")

pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")

Author