从分析的图像中提取医学标志物名称、数值和单位？

Question

我正在使用Amazon Textract来分析匿名的血液测试，它由标记、它们的值、单位、参考区间组成。

我想把它们提取到一个这样的字典中。

{"globulin": [2.8, gidL, [1.0, 4.0]], "cholesterol": [161, mg/dL, [120, 240]], .... }

这是一个OCR生成的文本的例子。

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.

请告知什么是提取这些信息的最佳方式，我已经尝试了Amazon Comprehend医疗 - 它做的工作，但不是所有的图像.Tried SpaCy。https:/github.comNLPatVCUmedaCy。,https:/towardsdatascience.comnamed-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da。

Answer 1

这可能不是一个很好的NLP应用，因为文本不是任何种类的自然语言。相反，它们是可以使用规则提取的结构化数据。编写规则无疑是一种方法。

1. 你可以先尝试对OCR结果上的类别进行模糊匹配，即 "CARDIAC RISK "和 "CHEMISTRIES"，将字符串分割成各自的类别。

2. 如果你确定每个条目只需要3行，你可以简单地通过换行来分割它们，并从那里提取数据。

3. 一旦您将它们分割成条目

这是我在你提供的数据上运行的一些示例代码。它需要 fuzzyset 包，你可以通过运行 python3 -m pip install fuzzyset. 由于有些条目没有单位，我稍微修改了你想要的输出格式，并将单位做成了一个列表，这样就可以很容易地将其清空。它还存储了在第三行发现的随机字母。

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)

输出。

{'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]]}}