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import gradio as gr
import pandas as pd
import json
from collections import defaultdict
# Create tokenizer for biomed model
from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all") # https://huggingface.co/d4data/biomedical-ner-all?text=asthma
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
# Matplotlib for entity graph
import matplotlib.pyplot as plt
plt.switch_backend("Agg")
# Load examples from JSON
import os
# Load terminology datasets:
basedir = os.path.dirname(__file__)
#dataLOINC = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')
#dataPanels = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')
#dataSNOMED = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')
#dataOMS = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')
#dataICD10 = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv')
dataLOINC = pd.read_csv(f'LoincTableCore.csv')
dataPanels = pd.read_csv(f'PanelsAndForms-ACW1208Labeled.csv')
dataSNOMED = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')
dataOMS = pd.read_csv(f'SnomedOMS.csv')
dataICD10 = pd.read_csv(f'ICD10Diagnosis.csv')
dir_path = os.path.dirname(os.path.realpath(__file__))
EXAMPLES = {}
#with open(dir_path + "\\" + "examples.json", "r") as f:
with open("examples.json", "r") as f:
example_json = json.load(f)
EXAMPLES = {x["text"]: x["label"] for x in example_json}
def MatchLOINC(name):
#basedir = os.path.dirname(__file__)
pd.set_option("display.max_rows", None)
#data = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')
data = dataLOINC
swith=data.loc[data['COMPONENT'].str.contains(name, case=False, na=False)]
return swith
def MatchLOINCPanelsandForms(name):
#basedir = os.path.dirname(__file__)
#data = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')
data = dataPanels
# Assessment Name:
#swith=data.loc[data['ParentName'].str.contains(name, case=False, na=False)]
# Assessment Question:
swith=data.loc[data['LoincName'].str.contains(name, case=False, na=False)]
return swith
def MatchSNOMED(name):
#basedir = os.path.dirname(__file__)
#data = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')
data = dataSNOMED
swith=data.loc[data['term'].str.contains(name, case=False, na=False)]
return swith
def MatchOMS(name):
#basedir = os.path.dirname(__file__)
#data = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')
data = dataOMS
swith=data.loc[data['SNOMED CT'].str.contains(name, case=False, na=False)]
return swith
def MatchICD10(name):
#basedir = os.path.dirname(__file__)
#data = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv')
data = dataICD10
swith=data.loc[data['Description'].str.contains(name, case=False, na=False)]
return swith
def SaveResult(text, outputfileName):
#try:
basedir = os.path.dirname(__file__)
savePath = outputfileName
print("Saving: " + text + " to " + savePath)
from os.path import exists
file_exists = exists(savePath)
if file_exists:
with open(outputfileName, "a") as f: #append
#for line in text:
f.write(str(text.replace("\n"," ")))
f.write('\n')
else:
with open(outputfileName, "w") as f: #write
#for line in text:
f.write(str(text.replace("\n"," ")))
f.write('\n')
#except ValueError as err:
# raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
return
def loadFile(filename):
try:
basedir = os.path.dirname(__file__)
loadPath = basedir + "\\" + filename
print("Loading: " + loadPath)
from os.path import exists
file_exists = exists(loadPath)
if file_exists:
with open(loadPath, "r") as f: #read
contents = f.read()
print(contents)
return contents
except ValueError as err:
raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
return ""
def get_today_filename():
from datetime import datetime
date = datetime.now().strftime("%Y_%m_%d-%I.%M.%S.%p")
#print(f"filename_{date}") 'filename_2023_01_12-03-29-22_AM'
return f"MedNER_{date}.csv"
def get_base(filename):
basedir = os.path.dirname(__file__)
loadPath = basedir + "\\" + filename
#print("Loading: " + loadPath)
return loadPath
def group_by_entity(raw):
outputFile = get_base(get_today_filename())
out = defaultdict(int)
for ent in raw:
out[ent["entity_group"]] += 1
myEntityGroup = ent["entity_group"]
print("Found entity group type: " + myEntityGroup)
if (myEntityGroup in ['Sign_symptom', 'Detailed_description', 'History', 'Activity', 'Medication' ]):
eterm = ent["word"].replace('#','')
minlength = 3
if len(eterm) > minlength:
print("Found eterm: " + eterm)
eterm.replace("#","")
g1=MatchLOINC(eterm)
g2=MatchLOINCPanelsandForms(eterm)
g3=MatchSNOMED(eterm)
g4=MatchOMS(eterm)
g5=MatchICD10(eterm)
sAll = ""
print("Saving to output file " + outputFile)
# Create harmonisation output format of input to output code, name, Text
try: # 18 fields, output to labeled CSV dataset for results teaching on scored regret changes to action plan with data inputs
col = " 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19"
#LOINC
g11 = g1['LOINC_NUM'].to_string().replace(","," ").replace("\n"," ")
g12 = g1['COMPONENT'].to_string().replace(","," ").replace("\n"," ")
s1 = ("LOINC Terms of entity ," + myEntityGroup + ", with term ," + eterm + ", LOINC codes of ," + g11 + ", and LOINC questions of ," + g12 + ", Label,Value, Label,Value, Label,Value ")
if g11 != 'Series([] )': SaveResult(s1, outputFile)
#LOINC Panels
g21 = g2['Loinc'].to_string().replace(","," ").replace("\n"," ")
g22 = g2['LoincName'].to_string().replace(","," ").replace("\n"," ")
g23 = g2['ParentLoinc'].to_string().replace(","," ").replace("\n"," ")
g24 = g2['ParentName'].to_string().replace(","," ").replace("\n"," ")
s2 = ("LOINC Panels of entity ," + myEntityGroup + ", with term ," + eterm + ", LOINC codes of ," + g21 + ", and LOINC name of ," + g22 + ", and Parent codes of ," + g23 + ", with Parent names of ," + g24 + ", Label,Value ")
if g21 != 'Series([] )': SaveResult(s2, outputFile)
#SNOMED
g31 = g3['conceptId'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
g32 = g3['term'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
s3 = ("SNOMED Terms of entity ," + myEntityGroup + ", with term ," + eterm + ", SNOMED concepts of ," + g31 + ", and SNOMED terms of ," + g32 + ", Label,Value, Label,Value, Label,Value ")
if g31 != 'Series([] )': SaveResult(s3, outputFile)
#OMS
g41 = g4['Omaha Code'].to_string().replace(","," ").replace("\n"," ")
g42 = g4['SNOMED CT concept ID'].to_string().replace(","," ").replace("\n"," ")
g43 = g4['SNOMED CT'].to_string().replace(","," ").replace("\n"," ")
g44 = g4['PR'].to_string().replace(","," ").replace("\n"," ")
g45 = g4['S&S'].to_string().replace(","," ").replace("\n"," ")
s4 = ("OMS Terms of entity ," + myEntityGroup + ", with term ," + eterm + ", Omaha codes of ," + g41 + ", and SNOMED concepts of ," + g42 + ", and SNOMED codes of ," + g43 + ", and OMS problem of ," + g44 + ", and OMS Sign Symptom of ," + g45)
if g41 != 'Series([] )': SaveResult(s4, outputFile)
#ICD10
g51 = g5['Code'].to_string().replace(","," ").replace("\n"," ")
g52 = g5['Description'].to_string().replace(","," ").replace("\n"," ")
s5 = ("ICD10 matches of entity ," + myEntityGroup + ", with term ," + eterm + ", ICD10 codes of ," + g51 + ", and descriptions of ," + g52 + ", Label,Value, Label,Value, Label,Value ")
if g51 != 'Series([] )': SaveResult(s5, outputFile)
except ValueError as err:
raise ValueError("Error in group by entity \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
#print(sAll)
#return out;
#break;
# out["total"] = sum(out.values())
# return out
return outputFile
def plot_to_figure(grouped):
fig = plt.figure()
plt.bar(x=list(grouped.keys()), height=list(grouped.values()))
plt.margins(0.2)
plt.subplots_adjust(bottom=0.4)
plt.xticks(rotation=90)
return fig
def ner(text):
raw = pipe(text)
ner_content = {
"text": text,
"entities": [
{
"entity": x["entity_group"],
"word": x["word"],
"score": x["score"],
"start": x["start"],
"end": x["end"],
}
for x in raw
],
}
#grouped = group_by_entity(raw)
outputFile = group_by_entity(raw)
#figure = plot_to_figure(grouped)
label = EXAMPLES.get(text, "Unknown")
#meta = {
# "entity_counts": grouped,
# "entities": len(set(grouped.keys())),
# "counts": sum(grouped.values()),
# }
#return (ner_content, meta, label, figure)
outputDataframe = pd.read_csv(outputFile)
#outputFile = outputFile.replace(os.path.dirname(__file__) + "\\","") # Just filename for File download UI output element
#return (ner_content, meta, label, figure, outputDataframe, outputFile)
return (ner_content, outputDataframe, outputFile)
# New way = Gradio Blocks:
demo = gr.Blocks()
with demo:
gr.Markdown(
"""
# 🩺⚕️NLP Clinical Ontology Biomedical NER Model
"""
)
input = gr.Textbox(label="Note text", value="")
#output=[
# gr.HighlightedText(label="NER", combine_adjacent=True)
#]
with gr.Tab("Biomedical Entity Recognition"):
output=[
gr.HighlightedText(label="NER", combine_adjacent=True),
#gr.JSON(label="Entity Counts"),
#gr.Label(label="Rating"),
#gr.Plot(label="Bar"),
gr.Dataframe(label="Dataframe"),
gr.File(label="File"),
]
examples=list(EXAMPLES.keys())
gr.Examples(examples, inputs=input)
input.change(fn=ner, inputs=input, outputs=output)
with gr.Tab("Clinical Terminology Resolution"):
#output=[
# gr.Textbox(placeholder="CT Match Results", lines=10)
#]
with gr.Row(variant="compact"):
btnLOINC = gr.Button("LOINC")
btnPanels = gr.Button("Panels")
btnSNOMED = gr.Button("SNOMED")
btnOMS = gr.Button("OMS")
btnICD10 = gr.Button("ICD10")
#output=[
# gr.HighlightedText(label="NER", combine_adjacent=True),
# gr.File(label="File"), # add download link here
# gr.Dataframe(label="Dataframe", headers=["LOINC", "Panels", "SNOMED", "OMS", "ICD10"]), # add harmonised output for input corpus here as a dataframe to UI
# gr.Textbox(placeholder="CT Match Results", lines=10) # add matched text scratchpad here
#]
#textCT = gr.Textbox(placeholder="CT Match Results", lines=10)
#btnLOINC.click(loadFile, inputs=["LOINCTerms.txt"], outputs=output)
#btnPanels.click(loadFile, "LOINCPanelsandForms.txt", output)
#btnSNOMED.click(loadFile, "SNOMEDTerms.txt", output)
#btnOMS.click(loadFile, "OMSTerms.txt", output)
#btnICD10.click(loadFile, "ICD10Terms.txt", output)
examples=list(EXAMPLES.keys())
gr.Examples(examples, inputs=input)
input.change(fn=ner, inputs=input, outputs=output)
#with gr.Tab("Examples Page 1"):
# gr.Examples(["a", "b", "c"], inputs=input)
#with gr.Tab("Examples Page 2"):
# gr.Examples(["d", "e", "f"], inputs=input)
#with gr.Tab("Examples Page 2"):
# gr.Examples(["g", "h", "i"], inputs=input)
demo.launch(debug=True)
# Old Way - Interface Load
#interface = gr.Interface(
# ner,
# inputs=gr.Textbox(label="Note text", value=""),
# outputs=[
# gr.HighlightedText(label="NER", combine_adjacent=True),
# gr.JSON(label="Entity Counts"),
# gr.Label(label="Rating"),
# gr.Plot(label="Bar"),
# ],
# examples=list(EXAMPLES.keys()),
# allow_flagging="never",
#)
#interface.launch() |