Spaces:
Running
Running
File size: 2,756 Bytes
ba29f94 fe6e7b2 ba29f94 fe6e7b2 ba29f94 fe6e7b2 ba29f94 fe6e7b2 ba29f94 fe6e7b2 ba29f94 fe6e7b2 ba29f94 fe6e7b2 ba29f94 fe6e7b2 ba29f94 fe6e7b2 ba29f94 fe6e7b2 |
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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
import easyocr
import requests
from transformers import AutoModelForTokenClassification, AutoTokenizer
from transformers import pipeline
# Initialize EasyOCR for text extraction from medical report image
reader = easyocr.Reader(['en'])
# Function to extract text from image
def extract_text_from_image(image_path):
result = reader.readtext(image_path)
text = ' '.join([item[1] for item in result])
return text
# Load Med7 model for medical NER (Named Entity Recognition)
model_name = "jeff1evesque/med7"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
# Initialize the NER pipeline with Med7 model
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
# Function to extract medical entities (test results) from text
def extract_medical_values(text):
entities = nlp(text)
medical_data = {}
for entity in entities:
if entity['entity_group'] == 'LAB_RESULT': # Change based on Med7's NER labels
medical_data[entity['word']] = entity['score']
return medical_data
# Function to check the values with LabTestAPI or similar API
def validate_medical_value(test_name, test_value):
# Example of API call (replace with actual API)
api_url = "https://api.labtestapi.com/get_reference_range"
params = {
"test_name": test_name,
"value": test_value
}
response = requests.get(api_url, params=params)
if response.status_code == 200:
result = response.json()
return result['normal_range']
else:
return None
# Function to analyze medical report
def analyze_report(image_path):
# Step 1: Extract text from the medical report image
text = extract_text_from_image(image_path)
# Step 2: Extract medical values using Med7
medical_values = extract_medical_values(text)
analysis_results = []
for test_name, test_value in medical_values.items():
# Step 3: Validate test value against normal range
normal_range = validate_medical_value(test_name, test_value)
if normal_range:
if test_value < normal_range[0]:
analysis_results.append(f"{test_name} is low. Consider consulting a doctor.")
elif test_value > normal_range[1]:
analysis_results.append(f"{test_name} is high. Consult a doctor.")
else:
analysis_results.append(f"{test_name} is within the normal range.")
else:
analysis_results.append(f"Could not validate {test_name}.")
return analysis_results
# Example Usage
image_path = "path_to_medical_report_image.png"
analysis = analyze_report(image_path)
for result in analysis:
print(result)
|