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drugs_condition_dictionary_finder.py
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drugs_condition_dictionary_finder.py
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import csv
import re
import pathlib
import pandas as pd
this_path = pathlib.Path(__file__).parent.resolve()
drug_variant_to_canonical = {}
drug_canonical_to_data = {}
condition_variant_to_canonical = {}
exclusions = {'ACACIA',
'ACETAMIDE',
'ACETATE',
'ACETOPHENONE',
'ACETYLCHOLINE',
'ACETYLENE',
'ACRIFLAVINE',
'ACT',
'ACTINIUM',
'ADENINE',
'ADENOSINE',
'ADRENALONE',
'AGMATINE',
'AIM',
'ALANINE',
'ALLANTOIN',
'ALLSPICE',
'ALMOND',
'ALUMINIUM',
'AMBER',
'AMEN',
'AMMONIA',
'AMYLAMINE',
'AMYLOPECTIN',
'AMYLOSE',
'ANETHOLE',
'ANILINE',
'ANTIMONY',
'ANTIPYRINE',
'APIGENIN',
'APPLE',
'APRICOT',
'ARGININE',
'ARTICHOKE',
'ASPARAGINE',
'ASPARAGUS',
'AVOCADO',
'BA',
'BANANA',
'BARIUM',
'BARLEY',
'BASIL',
'BEAN',
'BEEF',
'BEESWAX',
'BEET',
'BELLADONNA',
'BENTONITE',
'BENZIMIDAZOLE',
'BENZOIN',
'BENZOPHENONE',
'BENZYLAMINE',
'BERBERINE',
'BERKELIUM',
'BETAINE',
'BILBERRY',
'BLACKBERRY',
'BLUEBERRY',
'BLUEFISH',
'BORNEOL',
'BORON',
'BROCCOLI',
'BROMOFORM',
'BUCKWHEAT',
'BUTYLAMINE',
'CABALETTA',
'CABBAGE',
'CADAVERINE',
'CADMIUM',
'CAFFEINE',
'CALCIUM',
'CAMPHANE',
'CAMPHENE',
'CAMPHOR',
'CANTALOUPE',
'CAPSAICIN',
'CAPSICUM',
'CARAWAY',
'CARBAZOLE',
'CARBONATE',
'CARDAMOM',
'CARNOSINE',
'CAROB',
'CARROT',
'CARVACROL',
'CASEIN',
'CASHEW',
'CATFISH',
'CAULIFLOWER',
'CELERY',
'CELLOBIOSE',
'CESIUM',
'CHERRY',
'CHICKEN',
'CHLORINE',
'CHOLECYSTOKININ',
'CHOLESTEROL',
'CHOLINE',
'CHROMIUM',
'CHRYSIN',
'CHYMOTRYPSIN',
'CINCHOPHEN',
'CINNAMALDEHYDE',
'CINNAMON',
'CLOVE',
'COBALT',
'COCAINE',
'COCARBOXYLASE',
'COCOA',
'COCONUT',
'COPPER',
'CORN',
'CORTICOSTERONE',
'COTTON',
'COUMARIN',
'CRANBERRY',
'CREATINE',
'CREATININE',
'CRESOL',
'CREST',
'CROCIN',
'CROTONALDEHYDE',
'CUCUMBER',
'CUMIDINE',
'CUMIN',
'CURCUMIN',
'CYANAMIDE',
'CYCLOHEXANOL',
'CYCLOHEXANONE',
'CYCLOPROPANE',
'CYSTEINE',
'CYSTINE',
'CYTISINE',
'DATE',
'DEXTRAN',
'DIAMORPHINE',
'DICHROMATE',
'DIETHYLSTILBESTROL',
'DIGITOXIN',
'DIHYDROTACHYSTEROL',
'DILL',
'DINITROPHENOL',
'DIOSMIN',
'DIPHENYLGUANIDINE',
'DUCK',
'DUROQUINONE',
'ECGONINE',
'ECHINACEA',
'EGG',
'EGGPLANT',
'ELM',
'EMETINE',
'EMODIN',
'ENDURA',
'EOSIN',
'EPHEDRINE',
'ERGOMETRINE',
'ERGOSTEROL',
'ERGOTAMINE',
'ERYTHRITOL',
'ESCULIN',
'ESTRADIOL',
'ESTRIOL',
'ESTRONE',
'ESTROGEN',
'ETHANOL',
'ETHANOLAMINE',
'ETHER',
'EUCALYPTOL',
'EUGENOL',
'FARNESOL',
'FENCHONE',
'FENNEL',
'FIBRIN',
'FIG',
'FISETIN',
'FLAVONE',
'FLEET',
'FLOUNDER',
'FLUORESCEIN',
'FLUORESCIN',
'FLUORIDE',
'FLUORSPAR',
'FORMALDEHYDE',
'FRANKINCENSE',
'FRUCTOSE',
'FUCOSE',
'FUCOXANTHIN',
'GADOLINIUM',
'GALACTOSE',
'GARLIC',
'GELATIN',
'GENISTEIN',
'GERANIOL',
'GINGER',
'GINSENG',
'GLUCOSAMINE',
'GLUTATHIONE',
'GLYCERIN',
'GLYCINE',
'GLYCOLIDE',
'GOLD',
'GOLDENSEAL',
'GOOSE',
'GOSSYPOL',
'GRAPE',
'GRAPEFRUIT',
'GUAIACOL',
'GUANIDINE',
'GUANINE',
'GUANOSINE',
'GUVACINE',
'HADDOCK',
'HARMALINE',
'HARMINE',
'HAZELNUT',
'HELIUM',
'HEMATIN',
'HEMIN',
'HEMOGLOBIN',
'HEPARIN',
'HERRING',
'HESPERIDIN',
'HEXESTROL',
'HISTAMINE',
'HISTIDINE',
'HONEY',
'HYALURONIDASE',
'HYDROGEN',
'HYDROTALCITE',
'HYOSCYAMINE',
'HYPERICIN',
'HYPOCHLORITE',
'HYPOPHOSPHITE',
'HYPOXANTHINE',
'IMIDAZOLE',
'INDIRUBIN',
'INDIUM',
'INDOLE',
'INOSITOL',
'INULIN',
'IODIDE',
'IODINE',
'IODOBENZENE',
'IODOFORM',
'IPECAC',
'IRON',
'ISATIN',
'ISOEUGENOL',
'ISOFLAVONE',
'ISOLEUCINE',
'ISOPENTANE',
'ISOQUERCITRIN',
'ISOQUINOLINE',
'KALE',
'KAOLIN',
'KAVA',
'LACTOSE',
'LAMB',
'LANOLIN',
'LANTHANUM',
'LECITHIN',
'LEEK',
'LEMON',
'LENTIL',
'LETTUCE',
'LEUCINE',
'LICORICE',
'LINDANE',
'LITHIUM',
'LOBELINE',
'LOBSTER',
'LUPEOL',
'LUTEIN',
'LUTEOLIN',
'LYCOPENE',
'LYSINE',
'LYSOZYME',
'MACKEREL',
'MAGNESIUM',
'MALTODEXTRIN',
'MALTOSE',
'MANGANESE',
'MANGO',
'MANNITOL',
'MANNOSE',
'MENADIONE',
'MENTHOL',
'MENTHONE',
'METHANE',
'METHIONINE',
'METHYLAMINE',
'MOLYBDATE',
'MOLYBDENUM',
'MORPHOLINE',
'MUSKMELON',
'MYRICETIN',
'MYRRH',
'NARINGENIN',
'NECTARINE',
'NEODYMIUM',
'NEON',
'NIACIN',
'NICOTINAMIDE',
'NICOTINE',
'NIKETHAMIDE',
'NIOBIUM',
'NITRATE',
'NITRITE',
'NITROGEN',
'NITROGLYCERIN',
'NITROPRUSSIDE',
'NORLEUCINE',
'NUTMEG',
'OAT',
'OKRA',
'OLEANDRIN',
'ONION',
'OPIUM',
'ORANGE',
'ORNITHINE',
'ORRIS',
'OUABAIN',
'OXYGEN',
'OXYTOCIN',
'OYSTER',
'OZONE',
'PAPAIN',
'PAPAVERINE',
'PAPAYA',
'PAPRIKA',
'PARALDEHYDE',
'PARSLEY',
'PARSNIP',
'PATROL',
'PEA',
'PEACH',
'PEANUT',
'PEAR',
'PECAN',
'PECTIN',
'PENTAERYTHRITOL',
'PEPPERMINT',
'PEPSIN',
'PERCH',
'PERCHLORATE',
'PETROLATUM',
'PHENACETIN',
'PHENOL',
'PHENOLPHTHALEIN',
'PHENOTHIAZINE',
'PHENYLACETALDEHYDE',
'PHENYLALANINE',
'PHOSPHOCREATINE',
'PHOSPHORUS',
'PHTHALOCYANINE',
'PHYSOSTIGMINE',
'PINEAPPLE',
'PINITOL',
'PIPERAZINE',
'PIPERINE',
'PISTACHIO',
'PLATINUM',
'PLUM',
'POMEGRANATE',
'PORK',
'POTASSIUM',
'POTATO',
'POULTRY',
'PROCAINE',
'PROFLAVINE',
'PROGESTERONE',
'PROLINE',
'PROTAMINE',
'PROTHROMBIN',
'PROTOCATECHUALDEHYDE',
'PROTOPORPHYRIN',
'PSEUDOEPHEDRINE',
'PSEUDOTROPINE',
'PUMICE',
'PUMPKIN',
'PURSLANE',
'PUTRESCINE',
'RABBIT',
'RADISH',
'RASPBERRY',
'REPOSAL',
'RESORCINOL',
'RHAMNOSE',
'RHEIN',
'RHUBARB',
'RIBOFLAVIN',
'RIBOSE',
'RICE',
'ROSEMARY',
'ROSIN',
'ROTENONE',
'RUBIDIUM',
'RUTIN',
'RYE',
'SACCHARIN',
'SAFFLOWER',
'SAGE',
'SALICYLAMIDE',
'SALOL',
'SAMARIUM',
'SARCOSINE',
'SCALLOP',
'SCOPOLAMINE',
'SELENIUM',
'SENNA',
'SERINE',
'SHRIMP',
'SILICON',
'SILVER',
'SMELT',
'SNAIL',
'SONATA',
'SORBITOL',
'SOYBEAN',
'SPARTEINE',
'SPEARMINT',
'SPERMACETI',
'SPERMIDINE',
'SPERMINE',
'SPHINGOSINE',
'SPINACH',
'SQUALENE',
'SQUASH',
'STRAWBERRY',
'SUCCINIMIDE',
'SUCROSE',
'SWORDFISH',
'TABLOID',
'TAGATOSE',
'TALC',
'TANGERINE',
'TANTALUM',
'TARTRONATE',
'TAURINE',
'TENUATE',
'TERPINEOL',
'TESTOSTERONE',
'TETRAMETHYLAMMONIUM',
'THEOBROMINE',
'THEOPHYLLINE',
'THIAMINE',
'THREONINE',
'THROMBIN',
'THYME',
'THYMINE',
'THYMOL',
'THYROID',
'TING',
'TITANIUM',
'TOCOPHEROL',
'TOLUENE',
'TOMATO',
'TRAGACANTH',
'TREHALOSE',
'TRIBUTYRIN',
'TRIOLEIN',
'TROUT',
'TRYPSIN',
'TUBOCURARINE',
'TUNA',
'TURKEY',
'TURMERIC',
'TURNIP',
'TURPENTINE',
'TYRAMINE',
'TYROSINASE',
'TYROSINE',
'URACIL',
'UREA',
'URETHANE',
'VALERIAN',
'VALINE',
'VANADIUM',
'VANILLA',
'VEAL',
'VENISON',
'VERBENONE',
'VERSED',
'VITAMIN',
'VORTEX',
'WATER',
'WATERMELON',
'WHEAT',
'WORMWOOD',
'XANTHINE',
'XENON',
'XYLITOL',
'XYLOSE',
'YEAST',
'ZINC',
'ZINGERONE'}
# Drug names which are sufficiently generic that they may occur lower case
words_to_allow_lower_case = {'amphetamine',
'andrographolide',
'apomorphine',
'arbutin',
'arecoline',
'aspirin',
'atropine',
'bacitracin',
'barbital',
'benzocaine',
'benzofuran',
'benzylpenicillin',
'biguanide',
'biotin',
'cannabinol',
'cantharidin',
'carbromal',
'cathine',
'chloramphenicol',
'chloroform',
'chloroquine',
'codeine',
'colchicine',
'cortisone',
'emend',
'epinephrine',
'estrogen',
'ethylenediamine',
'ethylmorphine',
'factive',
'fibrinolysin',
'hexylresorcinol',
'hydroquinine',
'hydroquinone',
'lustral',
'methadone',
'methenamine',
'morphine',
'oxyquinoline',
'paregoric',
'penicillin',
'pentobarbital',
'phenobarbital',
'picropodophyllin',
'picrotoxin',
'pilocarpine',
'podophyllin',
'psyllium',
'pyrazole',
'pyridoxine',
'pyruvaldehyde',
'quercetin',
'quinacrine',
'quinidine',
'quinine',
'streptomycin',
'strychnine',
'sulfadiazine',
'sulfaguanidine',
'sulfamerazine',
'sulfamethazine',
'sulfamethylthiazole',
'sulfanilamide',
'sulfapyridine',
'sulfaquinoxaline',
'sulfathiazole',
'thymoquinone',
'thyroglobulin',
'trichloroethylene',
'trinitrotoluene',
'tryptophan',
'yohimbine'}
# Allows for detection of
# Aluminum Hydroxide,
# Alu-Tab,
# AlternaGEL,
# Interferon beta-1a
# 3,4-Diaminopyridine
# Excludes names like: "Aluminum Hydroxide and Magnesium Hydroxide"
# TODO: Issue with differently capitalized drugs, e.g. 3,4-D(d)iaminopyridine
variant_regex = re.compile(r'^[A-Za-z0-9,]+[ -]?[A-Za-z0-9\-]+(?:[ -][A-Z])?$')
path_to_drugs_data = "../data/drug_names_terminology/"
path_to_conditions_data = "../data/neuro_diseases_terminology/"
def add_variant(canonical_name, variant, dict_type="drug"):
if dict_type == "drug":
if variant not in drug_variant_to_canonical:
drug_variant_to_canonical[variant] = set()
drug_variant_to_canonical[variant].add(canonical_name)
else:
if variant not in condition_variant_to_canonical:
condition_variant_to_canonical[variant] = set()
condition_variant_to_canonical[variant].add(canonical_name)
def generate_conditions_lookup_dictionary(df):
synonyms_dict = {}
for index, row in df.iterrows():
icd_title = row['ICD Title']
mesh_name = row['MeSH Common name']
if pd.notna(row['MeSH Synonyms']):
synonyms_list = row['MeSH Synonyms'].split('|')
for synonym in synonyms_list:
synonym = synonym.strip().lower()
mesh_name = mesh_name.lower()
synonyms_dict = add_variant(mesh_name, synonym, synonyms_dict)
elif pd.notna(row['ICD Title']):
icd_title = icd_title.lower()
synonyms_dict = add_variant(icd_title, icd_title, synonyms_dict)
elif pd.notna(row['MeSH Common name']):
mesh_nam = mesh_name.lower()
synonyms_dict = add_variant(mesh_name, mesh_name, synonyms_dict)
return synonyms_dict
def add_drug(id, synonyms):
synonyms = [s.strip() for s in synonyms]
# TODO: add using an exclusion list as a parameter option to the function
# if re.sub("[- ].+", "", synonyms[0].upper()) in exclusions:
# return
if not variant_regex.match(synonyms[0]):
return
if synonyms[0] not in drug_canonical_to_data:
drug_canonical_to_data[synonyms[0]] = {"name": synonyms[0], "synonyms": set()}
if id.startswith("a"):
drug_canonical_to_data[synonyms[0]]["medline_plus_id"] = id
elif id.startswith("https://www.nhs.uk"):
drug_canonical_to_data[synonyms[0]]["nhs_url"] = id
elif id.startswith("https://en.wikipedia"):
drug_canonical_to_data[synonyms[0]]["wikipedia_url"] = id
elif id.startswith("DB"):
drug_canonical_to_data[synonyms[0]]["drugbank_id"] = id
else:
drug_canonical_to_data[synonyms[0]]["mesh_id"] = id
for variant in synonyms:
# if re.sub(" .+", "", variant.upper()) in exclusions:
# return
if variant_regex.match(variant):
drug_canonical_to_data[synonyms[0]]["synonyms"].add(variant)
add_variant(synonyms[0], variant)
add_variant(synonyms[0], variant.upper())
if variant.lower() in words_to_allow_lower_case:
add_variant(synonyms[0], variant.lower())
with open(this_path.joinpath(path_to_drugs_data + "drugs_dictionary_medlineplus.csv"), 'r',
encoding="utf-8") as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
headers = None
for row in spamreader:
if not headers:
headers = row
continue
id = row[0]
name = row[1]
# if id == "a699048":
# print("check")
synonyms = row[2].split(r"|")
name = re.sub(
" (Injection|Oral Inhalation|Transdermal|Ophthalmic|Topical|Vaginal Cream|Nasal Spray|Transdermal Patch|Rectal)",
"", name)
add_drug(id, [name] + synonyms)
with open(this_path.joinpath(path_to_drugs_data + "drugs_dictionary_nhs.csv"), 'r', encoding="utf-8") as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
headers = None
for row in spamreader:
if not headers:
headers = row
continue
id = row[0]
name = row[1]
synonyms = row[2].split(r"|")
add_drug(id, [name] + synonyms)
with open(this_path.joinpath(path_to_drugs_data + "drugs_dictionary_wikipedia.csv"), 'r', encoding="utf-8") as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
headers = None
for row in spamreader:
if not headers:
headers = row
continue
id = row[0]
name = row[1]
synonyms = row[2].split(r"|")
add_drug(id, [name] + synonyms)
with open(this_path.joinpath(path_to_drugs_data + "drugs_dictionary_mesh.csv"), 'r', encoding="utf-8") as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
headers = None
for row in spamreader:
if not headers:
headers = row
continue
id = row[0]
name = row[1]
synonyms = row[2].split(r"\|")
add_drug(id, [name] + synonyms)
# adding for the full db with product names included as synonyms
# TODO: create a parametrized function from this, not hard-coded inline as it is
is_new_format = False
if is_new_format:
with open(this_path.joinpath(path_to_drugs_data + "drugdb_full_database_parsed.csv"), 'r',
encoding="utf-8") as csvfile:
spamreader = csv.reader(csvfile, delimiter=';')
headers = None
for row in spamreader:
# print(row)
if not headers:
headers = row
continue
id = row[0]
name = row[1]
synonyms = row[4].split(r"|")
products = row[5].split(r"|")
syn_prod = synonyms + products
add_drug(id, [name] + syn_prod)
# no product names considered
else:
with open(this_path.joinpath(path_to_drugs_data + "drugbank vocabulary.csv"), 'r', encoding="utf-8") as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
headers = None
for row in spamreader:
if not headers:
headers = row
continue
id = row[0]
name = row[2]
synonyms = row[5].split(r"|")
add_drug(id, [name] + synonyms)
def find_drugs(tokens: list, is_ignore_case: bool = False):
drug_matches = []
is_exclude = set()
# Search for 2 token sequences
for token_idx, token in enumerate(tokens[:-1]):
cand = token + " " + tokens[token_idx + 1]
if is_ignore_case:
match = drug_variant_to_canonical.get(cand.upper(), None)
else:
match = drug_variant_to_canonical.get(cand, None)
if match:
for m in match:
drug_matches.append((drug_canonical_to_data[m], token_idx, token_idx + 1))
is_exclude.add(token_idx)
is_exclude.add(token_idx + 1)
for token_idx, token in enumerate(tokens):
if token_idx in is_exclude:
continue
if is_ignore_case:
match = drug_variant_to_canonical.get(token.upper(), None)
else:
match = drug_variant_to_canonical.get(token, None)
if match:
for m in match:
drug_matches.append((drug_canonical_to_data[m], token_idx, token_idx))
return drug_matches
def find_drugs_normalized_output(sentence: str, tokens: list, is_ignore_case: bool = False,
return_word_not_label: bool = False):
# this code add additional model_annotations with the start and end character of the found drugs
# needed training outputs for SpaCy NER
drug_matches = []
is_exclude = set()
all_char_indices = []
# Search for 2 token sequences
for token_idx, token in enumerate(tokens[:-1]):
cand = token + " " + tokens[token_idx + 1]
if is_ignore_case:
match = drug_variant_to_canonical.get(cand.upper(), None)
else:
match = drug_variant_to_canonical.get(cand, None)
if match and cand.upper() != "MG": # issue with 10 mg -> mg considered a DRUG
if return_word_not_label:
char_indices = [(i.start(), i.end()) + (cand.lower(),) for i in
re.finditer(cand.upper(), sentence.upper())]
else:
char_indices = [(i.start(), i.end()) + ("DRUG",) for i in re.finditer(cand.upper(), sentence.upper())]
all_char_indices = all_char_indices + char_indices
for m in match:
drug_matches.append((drug_canonical_to_data[m], token_idx, token_idx + 1))
is_exclude.add(token_idx)
is_exclude.add(token_idx + 1)
for token_idx, token in enumerate(tokens):
if token_idx in is_exclude:
continue
if is_ignore_case:
match = drug_variant_to_canonical.get(token.upper(), None)
else:
match = drug_variant_to_canonical.get(token, None)
if match and token.upper() != "MG":
if return_word_not_label:
char_indices = [(i.start(), i.end()) + (token.lower(),) for i in
re.finditer(token.upper(), sentence.upper())]
else:
char_indices = [(i.start(), i.end()) + ("DRUG",) + (token.lower(),) for i in
re.finditer(token.upper(), sentence.upper())]
all_char_indices = all_char_indices + char_indices
for m in match:
drug_matches.append((drug_canonical_to_data[m], token_idx, token_idx))
all_char_indices = list(set(all_char_indices))
return drug_matches, all_char_indices # {"entites":all_char_indices}
def load_conditions_dict():
conditions_db = pd.read_csv(
path_to_conditions_data + "output/diseases_dictionary_mesh_icd.csv")
synonyms_dict = generate_conditions_lookup_dictionary(conditions_db)
def find_match(token, is_ignore_case: bool = False):
if is_ignore_case:
match_drug = drug_variant_to_canonical.get(token.upper(), None)
match_condition = condition_variant_to_canonical.get(token.lower(), None)
else:
match_drug = drug_variant_to_canonical.get(token, None)
match_condition = condition_variant_to_canonical.get(token, None)
return match_drug, match_condition
def find_drugs_and_conditions_normalized_BIO_output(tokens: list, is_ignore_case: bool = True):
load_conditions_dict()
bio_output = []
tokens = eval(tokens)
to_skip = False
for token_idx, token in enumerate(tokens):
if to_skip:
to_skip = False
continue
match_drug, match_condition = find_match(token, is_ignore_case)
if match_drug and token.upper() != "MG":
bio_output.append("B-DRUG")
elif match_condition:
bio_output.append("B-COND")
else:
# try if two tokens work
if token_idx + 1 == len(tokens):
bio_output.append("O")
continue
else:
cand = token + " " + tokens[token_idx + 1]
match_drug, match_condition = find_match(cand, is_ignore_case)
if match_drug:
bio_output.append("B-DRUG")
bio_output.append("I-DRUG")
to_skip = True
elif match_condition:
bio_output.append("B-COND")
bio_output.append("I-COND")
to_skip = True
else:
bio_output.append("O")
return bio_output # {"entites":all_char_indices}
# for local testing
if __name__ == '__main__':
sentence = "Pilot Test of Flavoxate and Flavoxatum in Multiple Sclerosis: Safety and Tolerability."
print(find_drugs_normalized_output(sentence, sentence.split(" "), is_ignore_case=True))