Extract medical marker name, values and units from analysed image? - python-3.x

Question

I am using Amazon Textract to analyse anonymous blood tests.
It consists of markers, their values, units, ref interval.
I want to extract them into a dictionary like this:
{"globulin": [2.8, gidL, [1.0, 4.0]], "cholesterol": [161, mg/dL, [120, 240]], .... }
Here is an example of such OCR produced text:
Name:
Date Perfermed
$/6/2010
DOBESevState:
Date Collected:
05/03/201004.00 PN
Date Lac Meat: 05/03/2010 10.45 A
Eraminer:
PTM
Date Received: $/7/2010 12:13.11A
Tukit No.
8028522035
Abeormal
Normal
Range
CARDLAC RISK
CHOLESTEROL
161.00
120.00 240.00 mg/dL
CHOLESTEROLHDL RATIO
2.39
1.250 5.00
HIGH DENSITY LIPOPROTEINCHDL)
67.30
35.00 75.00 me/dL
LOW DENSITY LIPOPROTEIN (LDL)
78.70
60.00 a 190.00 midI.
TRIGLYCERIDES
75.00
10.00 a 200.00 made
CHEMISTRIES
ALBUMIN
4.40
3.50 5.50 pidl
ALKALINE PHOSPHATASE
49.00
30.00 120.00 UAL
BLOOD UREA NITROGEN (BUN)
17.00
6.00 2500 meidL
CREATININE
0,85
060 1.50 matdL
FRUCTOSAMINE
182
1.20 1.79 mmoV/l
GAMMA GLUTAMYUTRANSFERASE
9.00
2.00 65.00 UIL
GLOBULIN
2.80
1.00 4.00 gidL.
GLUCOSE
61.00
70.00 125.00 me/dl.
HEMOGLOBIN AIC
5.10
3.00 6.00 %
SGOT (AST)
25.00
0.00 41.00 UM
SOPI (ALT)
22.00
0.00 45.00 IMI
TOTAL BILIRUBIN
0.52
0.10 1.20 mmeldi.
TOTAL PROTEIN
720
6.00 8.50 gidl.
1. This sample lab report shows both normal and abnormal results. as well as
acceptable reference ranges for each testing category.
Please advise what is the best way to extract this information, I have tried Amazon Comprehend medical - it does the job but not for all images.
Tried SpaCy: https://github.com/NLPatVCU/medaCy,
https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da

Answer 1

This might not be a good application of NLP as the text isn't any sort of natural language. Rather, they are structured data that can be extracted using rules. Writing rules is definitely one way to go about this.
You can first try to do a fuzzy match of the categories on the OCR results, namely "CARDIAC RISK" and "CHEMISTRIES" to partition the string into their respective categories.
If you are sure that each entry will take only 3 lines, you can simply partition them by newline and extract the data from there.
Once you have them split into entries
Here's some sample code I ran on the data you provided. It requires the fuzzyset package which you can get by running python3 -m pip install fuzzyset. Since some entries don't have units I modified your desired output format slightly and made units a list so it can easily be empty. It also stores random letters found in the third line.
from fuzzyset import FuzzySet
### Load data
with open("ocr_result.txt") as f:
data = f.read()
lines = data.split("\n")
### Create fuzzy set
CATEGORIES = ("CARDIAC RISK", "chemistries")
fs = FuzzySet(lines)
### Get the line ranges of each category
cat_ranges = [0] * (len(CATEGORIES) + 1)
for i, cat in enumerate(CATEGORIES):
match = fs.get(cat)[0]
match_idx = lines.index(match[1])
cat_ranges[i] = match_idx
last_idx = lines.index(fs.get("sample lab report")[0][1])
cat_ranges[-1] = last_idx
### Read lines in each category
def _to_float(s: str) -> float:
"""
Attempt to convert a string value to float
"""
try:
f = float(s)
except ValueError:
if "," in s:
s = s.replace(",", ".")
f = float(s)
else:
raise ValueError(f"Cannot convert {s} to float.")
return f
result = {}
for i, cat in enumerate(CATEGORIES):
result[cat] = {}
# Ignore the line of the category itself
s = slice(cat_ranges[i] + 1, cat_ranges[i + 1])
lines_in_cat = lines[s]
if len(lines_in_cat) % 3 != 0:
breakpoint()
raise ValueError("Something's wrong")
for i in range(0, len(lines_in_cat), 3):
_name = lines_in_cat[i]
_value = lines_in_cat[i + 1]
_line_3 = lines_in_cat[i + 2].split(" ")
# Convert value to float
_value = _to_float(_value)
# Process line 3 to get range and unit
_range = []
_unit = []
for i, v in enumerate(_line_3):
if v[0].isdigit() and len(_range) < 2:
_range.append(_to_float(v))
else:
_unit.append(v)
_l = [_value, _unit, _range]
result[cat][_name] = _l
print(result)
Output:
{'CARDIAC RISK': {'CHOLESTEROL': [161.0, ['mg/dL'], [120.0, 240.0]], 'CHOLESTEROLHDL RATIO': [2.39, [], [1.25, 5.0]], 'HIGH DENSITY LIPOPROTEINCHDL)': [67.3, ['me/dL'], [35.0, 75.0]], 'LOW DENSITY LIPOPROTEIN (LDL)': [78.7, ['a', 'midI.'], [60.0, 190.0]], 'TRIGLYCERIDES': [75.0, ['a', 'made'], [10.0, 200.0]]}, 'chemistries': {'ALBUMIN': [4.4, ['pidl'], [3.5, 5.5]], 'ALKALINE PHOSPHATASE': [49.0, ['UAL'], [30.0, 120.0]], 'BLOOD UREA NITROGEN (BUN)': [17.0, ['meidL'], [6.0, 2500.0]], 'CREATININE': [0.85, ['matdL'], [60.0, 1.5]], 'FRUCTOSAMINE': [182.0, ['mmoV/l'], [1.2, 1.79]], 'GAMMA GLUTAMYUTRANSFERASE': [9.0, ['UIL'], [2.0, 65.0]], 'GLOBULIN': [2.8, ['gidL.'], [1.0, 4.0]], 'GLUCOSE': [61.0, ['me/dl.'], [70.0, 125.0]], 'HEMOGLOBIN AIC': [5.1, ['%'], [3.0, 6.0]], 'SGOT (AST)': [25.0, ['UM'], [0.0, 41.0]], 'SOPI (ALT)': [22.0, ['IMI'], [0.0, 45.0]], 'TOTAL BILIRUBIN': [0.52, ['mmeldi.'], [0.1, 1.2]], 'TOTAL PROTEIN': [720.0, ['gidl.'], [6.0, 8.5]]}}