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from meshutils import nct_to_mesh_term, mesh_term_to_id, df_mesh, df_mesh_ct
from cid import CaseInsensitiveDict
from dictutils import *
import re
import streamlit as st
# mesh list extract
def meshtrm_lst_xtract(nct_value):
try:
mesh_term = nct_to_mesh_term[nct_value]
mesh_term_list = list(mesh_term)
return mesh_term_list
except:
pass
@st.cache(suppress_st_warning=True, allow_output_mutation=True)
# type extract fun
def type_extract(mesh_term_list):
mesh_term_list = [mesh_term_list] if isinstance(mesh_term_list, str) else mesh_term_list
# print('mesh_term_list: ',mesh_term_list)
# l2_map_lst=[]
uid_lst = []
if mesh_term_list is not None:
for val in mesh_term_list:
# print('value inside uid forloop:',val)
try:
# print('Inside get uid')
uid = mesh_term_to_id[val]
uid_lst.append(uid)
# print(uid_lst)
if uid_lst is None:
uid_lst = []
except:
pass
# print('error in get uid list')
# get mesh num
mesh_num_xtract_lst = []
for val in uid_lst:
try:
# print('Inside get mesh num')
mesh_num_xtract = df_mesh.loc[df_mesh['ui'] == val, 'mesh_number'].iloc[0]
mesh_num_xtract_lst.append(mesh_num_xtract)
# print(mesh_num_xtract_lst)
if ',' in mesh_num_xtract_lst[0]:
mesh_num_xtract_lst = mesh_num_xtract_lst[0].split(", ")
# print('mesh_num_xtract_lst after spltting',mesh_num_xtract_lst)
except:
pass
# print('error in get mesh num')
# mesh number extract l2
l2_map_lst = []
for val in mesh_num_xtract_lst:
# print('Inside l2map for loop',val)
search_value = val[:3]
# print('printing search value:',search_value)
try:
l2_map = df_mesh.loc[df_mesh['mesh_number'] == search_value, 'name'].iloc[0]
# print(l2_map)
l2_map_lst.append(l2_map)
# print(l2_map_lst)
if l2_map_lst is None:
l2_map_lst = []
except:
pass
l2_map_lst = list(set(l2_map_lst))
# print('finaloutput',l2_map_lst)
return l2_map_lst
def split_values(col_val):
# """split words seperated by special characters"""
# print(col_val)
if col_val != '':
char_list = ['|', ',', '/', '.', ';', './', ',/', '/ ', ' /']
# res = ' '.join([ele for ele in char_list if(ele in col_val)])
res = [ele for ele in char_list if (ele in col_val)]
# print('printing string of found char',res)
colstring = str(col_val)
f_res = []
try:
while len(res) > 0:
res = res[-1]
f_res = colstring.split(''.join(res))
# print(f_res)
# return f_res
f_res = [x for x in f_res if x is not None]
return ', '.join(f_res)
except:
pass
else:
return col_val
def map_entry_terms(myText):
obj = CaseInsensitiveDict(entry_dict)
pattern = re.compile(r'(?<!\w)(' + '|'.join(re.escape(key) for key in obj.keys()) + r')(?!\w)', flags=re.IGNORECASE)
text = pattern.sub(lambda x: obj[x.group()], myText)
# text = pattern.sub(lambda x: obj[x.group()], text)
return text.strip().split('/')
def remove_none(some_list):
some_list = [some_list] if isinstance(some_list, str) else some_list
if some_list is not None:
some_list = list(filter(lambda x: x != None, some_list))
return some_list
def retain_all_ta(some_list):
some_list = [some_list] if isinstance(some_list, str) else some_list
# some_list.split(',')
value = 'all_ta'
# print(value)
if some_list is not None:
if value in some_list:
some_list = [value]
return some_list
else:
return some_list
def unique_list(l):
l = map(str.strip, l) # remove whitespace from list element
# print(l)
ulist = []
[ulist.append(x) for x in l if x not in ulist]
return ulist
def split_for_type_extract(my_list, char):
# print('entering the function:',my_list)
try:
my_list = [my_list] if isinstance(my_list, str) else my_list
if my_list is not None:
# print(my_list)
my_list = list(map(lambda x: x.split(char)[0], my_list))
# my_list = [x for x in my_list if x is not None]
return my_list
except:
pass
def special_ask(col_value):
col_value = col_value.lower()
if col_value == 'obesity':
ta_list = 'met'
return ta_list.split()
elif col_value == 'healthy subject':
ta_list = 'all_ta'
return ta_list.split()
elif col_value == 'healthy subjects':
ta_list = 'all_ta'
return ta_list.split()
elif col_value == 'healthy participants':
ta_list = 'all_ta'
return ta_list.split()
elif col_value == 'healthy participant':
ta_list = 'all_ta'
return ta_list.split()
elif col_value == 'inflammation':
ta_list = 'ai'
return ta_list.split()
else:
pass
def remove_stopwords(query):
stopwords = ['acute-on-chronic', 'acute', 'chronic',
'diseases of the', '-19', '- 19', '19', '.']
if query is not None:
querywords = query.split()
resultwords = [word for word in querywords if word.lower() not in stopwords]
result = ' '.join(resultwords)
return result
else:
''
def gb_2_us(text, mydict):
try:
for us, gb in mydict.items():
text = text.replace(gb, us)
return text
except:
return ''
def fix_text_with_dict(text, mydict):
text = ','.join([repl_dict.get(i, i) for i in text.split(', ')])
return text
def replace_text(mytext):
cancer = ['cancer', 'neoplasm', 'carcinoma', 'lymphoma', 'adenoma', 'myoma', 'meningioma',
'malignancy', 'tumor', 'malignancies', 'chemotherapy']
# fracture = ['fractures', 'fracture']
heart_failure = ['heart failure', 'cardiac']
ectomy = 'prostatectomy'
covid = 'covid'
transplant = 'transplant'
healthy = 'healthy'
park = 'parkinson'
allergy = ['allergy', 'allergic']
virus = 'virus'
cornea = ['cornea', 'eye', 'ocular', 'macular']
vaccine = 'vaccines'
ureter = 'ureter'
mutation = 'mutation'
stemcell = 'stem cells'
behavior = ['behavior', 'depressive', 'depression', 'anxiety', 'satisfaction', 'grief']
molar = ['molar', 'dental', 'maxillary']
diet = 'diet'
biopsy = 'biopsy'
physiology = 'physiology'
infection = ['infection', 'bacteremia', 'fungemia']
preg = ['pregnancy', 'pregnant', 'labor', 'birth']
imaging = ['x-ray', 'imaging', 'mri']
surgery = 'surgery'
angina = 'angina'
use_disorder = ['use disorder', 'obsessive', 'panic', 'posttraumatic stress',
'post-traumatic stress', 'schizophrenia']
if mytext:
try:
if any(text in mytext.lower() for text in cancer):
mytext = 'neoplasms'
return mytext
if any(text in mytext.lower() for text in heart_failure):
mytext = 'cardiovascular diseases'
return mytext
if covid in mytext.lower():
mytext = 'covid-19'
return mytext
if ectomy in mytext.lower():
mytext = 'urogenital surgical procedures'
return mytext
if transplant in mytext.lower():
mytext = 'body regions'
return mytext
if healthy in mytext.lower():
mytext = 'healthy volunteers'
return mytext
if any(text in mytext.lower() for text in allergy):
mytext = 'immune system diseases'
return mytext
if park in mytext.lower():
mytext = 'parkinson disease'
return mytext
if park in mytext.lower():
mytext = 'immune system diseases'
return mytext
if virus in mytext.lower():
mytext = 'viruses'
return mytext
if any(text in mytext.lower() for text in cornea):
mytext = 'eye diseases'
return mytext
if vaccine in mytext.lower():
mytext = 'vaccines'
return mytext
if ureter in mytext.lower():
mytext = 'ureter'
return mytext
if mutation in mytext.lower():
mytext = 'mutation'
return mytext
if stemcell in mytext.lower():
mytext = 'stem cells'
return mytext
if any(text in mytext.lower() for text in behavior):
mytext = 'behavior'
return mytext
if any(text in mytext.lower() for text in molar):
mytext = 'molar'
return mytext
if diet in mytext.lower():
mytext = 'diet'
return mytext
if biopsy in mytext.lower():
mytext = 'biopsy'
return mytext
if physiology in mytext.lower():
mytext = 'physiology'
return mytext
if any(text in mytext.lower() for text in infection):
mytext = 'infections'
return mytext
if any(text in mytext.lower() for text in preg):
mytext = 'reproductive and urinary physiological phenomena'
return mytext
if any(text in mytext.lower() for text in imaging):
mytext = 'diagnosis'
return mytext
if surgery in mytext.lower():
mytext = 'medicine'
return mytext
if angina in mytext.lower():
mytext = 'angina pectoris'
return mytext
if any(text in mytext.lower() for text in use_disorder):
mytext = 'mental disorders'
return mytext
else:
return mytext
except:
return ''
# For studies in CTgov
def is_nct(col_value):
# Returns mesh term list based on NCT ID
val = col_value[:3]
if val == 'NCT':
try:
if col_value in df_mesh_ct.values:
mesh_term_list = meshtrm_lst_xtract(col_value)
l2map = type_extract(mesh_term_list)
return l2map
except:
pass
else:
'Study Not in Database, Please enter condition or conditions treated'
return
# For studies not in CTgov
def is_not_nct(col_value):
# Returns mesh term list based on NCT ID
# Returns disease type l2 tag in Mesh dictionary
if col_value is not None:
mesh_term_list = col_value
l2map = type_extract(mesh_term_list)
return l2map
else:
None
return