diff --git a/dataSources/PubMed/__pycache__/util.cpython-313.pyc b/dataSources/PubMed/__pycache__/util.cpython-313.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a66a1a424a46ddaa8cbc32cbaaaf6141e51b56cf
Binary files /dev/null and b/dataSources/PubMed/__pycache__/util.cpython-313.pyc differ
diff --git a/dataSources/PubMed/data_keyword_num.py b/dataSources/PubMed/data_keyword_num.py
index b3d14db47dffa926095c13d90e3d0eac08632225..2672a80ddd57394127e4f2176567d0232657d589 100644
--- a/dataSources/PubMed/data_keyword_num.py
+++ b/dataSources/PubMed/data_keyword_num.py
@@ -61,24 +61,27 @@ for term in TERMS:
ncd_mesh = term + "[Mesh]"
print("TERM: ", ncd_mesh)
+ keywords_term = ""
+
for keyword in KEYWORDS:
- data[term][keyword] = {}
keyword_term = keyword.replace(" ", "+").replace("&", "%26").replace("/", "%2F")
print("KEYWORD: ", keyword_term)
- search_term = ncd_mesh + '+AND+"' + keyword_term + '"'
+ keywords_term += keyword_term + "+OR+"
+
+ search_term = ncd_mesh + "+AND+(+" + keywords_term + "+)"
- print("SEARCH: ", search_term)
+ print("SEARCH: ", search_term)
- for interval in INTERVALS:
- print("INTERVAL: ", interval)
- counts = get_count_for_year(2024, search_term, interval)
- print(counts)
- data[term][keyword][interval] = counts
+ for interval in INTERVALS:
+ print("INTERVAL: ", interval)
+ counts = get_count_for_year(2024, search_term, interval)
+ print(counts)
+ data[term][interval] = counts
- with open(f"{RESULTS_DIR}/{term}.txt", "w+") as file:
- print(data[term], file=file)
+ # with open(f"{RESULTS_DIR}/{term}.txt", "w+") as file:
+ # print(data[term], file=file)
print("DATA: ", data)
\ No newline at end of file
diff --git a/dataSources/PubMed/data_num.py b/dataSources/PubMed/data_num.py
index 31cef23f9e99baa97e1b116bb9d17b5477145c1c..8421e93bc1bf779589726ecd9d7db0c0316b6ede 100644
--- a/dataSources/PubMed/data_num.py
+++ b/dataSources/PubMed/data_num.py
@@ -1,21 +1,21 @@
+import sys
+import os
from requests import get
from datetime import datetime, timedelta
import time
+import urllib.parse
-TERMS = [
- '"Noncommunicable+Diseases"', # NCDs (All)
- '"Diabetes+Mellitus"', # Diabetes (type 1 or 2)
- '"Neoplasms"', # Cancer
- '"Respiratory+Tract+Diseases"', # Chronic respiratory disease
- '"Cardiovascular+Diseases"', # Cardiovascular diseases
- '"Mental+Health"', # Mental Health
- '"Diabetes+Mellitus%2C+Type+1"', # Diabetes type 1
- '"Diabetes+Mellitus%2C+Type+2"' # Diabetes type 2
-]
+# Ajouter le répertoire parent au chemin de recherche
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../")))
+
+from variables.pubmed import *
+from dataSources.PubMed.util import *
+
+RESULTS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "./results"))
INTERVALS = [
- "day",
- "week",
+ # "day",
+ # "week",
"month"
]
@@ -32,13 +32,24 @@ def get_count_for_year(year, term, interval = "month"):
elif interval == "month":
next_date = (current_date.replace(day=28) + timedelta(days=4)).replace(day=1)
- url = f'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term={term}&retmode=json&mindate={current_date.strftime("%Y/%m/%d")}&maxdate={next_date.strftime("%Y/%m/%d")}&usehistory=y'
- response = get(url)
- search_res = response.json()
- counts.append(int(search_res["esearchresult"]["count"]))
+ while (True):
+ try:
+ url = f'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&api_key={PUBMED_API_KEY}&term={term}&retmode=json&mindate={current_date.strftime("%Y/%m/%d")}&maxdate={next_date.strftime("%Y/%m/%d")}'
+ print("Ok 1")
+ response = get(url)
+ print(response)
+ print("Ok 2")
+ search_res = response.json()
+ print("Ok 3")
+ counts.append(int(search_res["esearchresult"]["count"]))
- current_date = next_date
- time.sleep(1) # si plus de 3 requêtes par seconde sinon adresse IP bann (normalement)
+ print("Ok 4")
+ current_date = next_date
+ break
+ except Exception as e:
+ print(e)
+
+ time.sleep(0.2)
max_count = max(counts)
min_count = min(counts)
@@ -46,17 +57,235 @@ def get_count_for_year(year, term, interval = "month"):
return {"max": max_count, "min": min_count, "avg": avg_count}
-data = {}
+interval = "month"
+tot = 0
+for ncd in NCDS:
+ search_term = url_encode(ncd)
+ print(f"SEARCH_TERM: {search_term}")
+ counts = get_count_for_year(2024, search_term, interval)
+
+ tot += counts["avg"]
+
+search_term = url_encode(" OR ".join(NCDS))
+print(f"SEARCH_TERM: {search_term}")
+counts = get_count_for_year(2024, search_term, interval)
+
+
+print(tot)
+print(counts["avg"])
+
+exit(0)
+
+result = {}
+
+ncds_mesh = get_mesh_term(NCDS_MESH_TERM)
+ncds_mesh_noexp = get_mesh_noexp_term(NCDS_MESH_TERM)
+
+print(url_encode(" OR ".join(ncds_mesh)))
+print(url_encode(" OR ".join(ncds_mesh_noexp)))
+
+keywords_mesh = get_mesh_term(KEYWORDS_MESH_TERM)
+keywords_mesh_noexp = get_mesh_noexp_term(KEYWORDS_MESH_TERM)
+
+print(url_encode(" OR ".join(keywords_mesh)))
+print(url_encode(" OR ".join(keywords_mesh_noexp)))
+
+keywords_mesh_site_proposition = get_mesh_term(KEYWORDS_MESH_SITE_PROPOSITION)
+keywords_mesh_site_proposition_noexp = get_mesh_noexp_term(KEYWORDS_MESH_SITE_PROPOSITION)
+
+print(url_encode(" OR ".join(keywords_mesh_site_proposition)))
+print(url_encode(" OR ".join(keywords_mesh_site_proposition_noexp)))
+
+keywords_mesh_proposition = get_mesh_term(KEYWORDS_MESH_PROPOSITION)
+keywords_mesh_proposition_noexp = get_mesh_noexp_term(KEYWORDS_MESH_PROPOSITION)
+
+print(url_encode(" OR ".join(keywords_mesh_proposition)))
+print(url_encode(" OR ".join(keywords_mesh_proposition_noexp)))
+
+keywords_hubheading = [ f'"{subheading}"[Subheading]' for subheading in KEYWORDS_MESH_SUBHEADING]
+keywords_hubheading_noexp = [ f'"{subheading}"[Subheading:noexp]' for subheading in KEYWORDS_MESH_SUBHEADING]
+
+print(url_encode(" OR ".join(keywords_hubheading)))
+print(url_encode(" OR ".join(keywords_hubheading_noexp)))
+
+for interval in INTERVALS:
+ result[interval] = {}
+
+ for ncd in NCDS:
+ result[interval][ncd] = {}
+
+ search_term = url_encode(ncd)
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd]["WITHOUT_KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd} AND ( " + " OR ".join(KEYWORDS) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd]["KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd} AND ( " + " OR ".join(keywords_mesh) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd]["KEYWORDS_MESH"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd} AND ( " + " OR ".join(keywords_mesh_noexp) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd]["KEYWORDS_MESH_NOEXP"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd} AND ( " + " OR ".join(keywords_mesh + keywords_hubheading + keywords_mesh_site_proposition + keywords_mesh_proposition) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd]["KEYWORDS_MESH_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd} AND ( " + " OR ".join(keywords_mesh_noexp + keywords_hubheading_noexp + keywords_mesh_site_proposition_noexp + keywords_mesh_proposition_noexp) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd]["KEYWORDS_MESH_NOEXP_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(" OR ".join(NCDS))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS"]["WITHOUT_KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd} AND ( {" OR ".join(KEYWORDS)} ) )']
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS"]["KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd} AND ( {" OR ".join(keywords_mesh)} ) )']
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS"]["KEYWORDS_MESH"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd} AND ( {" OR ".join(keywords_mesh_noexp)} ) )']
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS"]["KEYWORDS_MESH_NOEXP"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd} AND ( {" OR ".join(keywords_mesh + keywords_hubheading + keywords_mesh_site_proposition + keywords_mesh_proposition)} ) )']
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS"]["KEYWORDS_MESH_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd} AND ( {" OR ".join(keywords_mesh_noexp + keywords_hubheading_noexp + keywords_mesh_site_proposition_noexp + keywords_mesh_proposition_noexp)} ) )']
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS"]["KEYWORDS_MESH_NOEXP_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ # ------------------------------------
+
+ for ncd_mesh in ncds_mesh:
+ result[interval][ncd_mesh] = {}
+
+ search_term = url_encode(ncd_mesh)
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh]["WITHOUT_KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh} AND ( " + " OR ".join(KEYWORDS) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh]["KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh} AND ( " + " OR ".join(keywords_mesh) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh]["KEYWORDS_MESH"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh} AND ( " + " OR ".join(keywords_mesh_noexp) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh]["KEYWORDS_MESH_NOEXP"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh} AND ( " + " OR ".join(keywords_mesh + keywords_hubheading + keywords_mesh_site_proposition + keywords_mesh_proposition) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh]["KEYWORDS_MESH_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh} AND ( " + " OR ".join(keywords_mesh_noexp + keywords_hubheading_noexp + keywords_mesh_site_proposition_noexp + keywords_mesh_proposition_noexp) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh]["KEYWORDS_MESH_NOEXP_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(" OR ".join(ncds_mesh))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH"]["WITHOUT_KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh} AND ( {" OR ".join(KEYWORDS)} ) )' for ncd_mesh in ncds_mesh]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH"]["KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh} AND ( {" OR ".join(keywords_mesh)} ) )' for ncd_mesh in ncds_mesh]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH"]["KEYWORDS_MESH"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh} AND ( {" OR ".join(keywords_mesh_noexp)} ) )' for ncd_mesh in ncds_mesh]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH"]["KEYWORDS_MESH_NOEXP"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh} AND ( {" OR ".join(keywords_mesh + keywords_hubheading + keywords_mesh_site_proposition + keywords_mesh_proposition)} ) )' for ncd_mesh in ncds_mesh]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH"]["KEYWORDS_MESH_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh} AND ( {" OR ".join(keywords_mesh_noexp + keywords_hubheading_noexp + keywords_mesh_site_proposition_noexp + keywords_mesh_proposition_noexp)} ) )' for ncd_mesh in ncds_mesh]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH"]["KEYWORDS_MESH_NOEXP_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ # ----------------------------------------
+
+ for ncd_mesh_noexp in ncds_mesh_noexp:
+ result[interval][ncd_mesh_noexp] = {}
+
+ search_term = url_encode(ncd_mesh_noexp)
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh_noexp]["WITHOUT_KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh_noexp} AND ( " + " OR ".join(KEYWORDS) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh_noexp]["KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh_noexp} AND ( " + " OR ".join(keywords_mesh) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh_noexp]["KEYWORDS_MESH"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh_noexp} AND ( " + " OR ".join(keywords_mesh_noexp) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh_noexp]["KEYWORDS_MESH_NOEXP"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh_noexp} AND ( " + " OR ".join(keywords_mesh + keywords_hubheading + keywords_mesh_site_proposition + keywords_mesh_proposition) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh_noexp]["KEYWORDS_MESH_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(f"{ncd_mesh_noexp} AND ( " + " OR ".join(keywords_mesh_noexp + keywords_hubheading_noexp + keywords_mesh_site_proposition_noexp + keywords_mesh_proposition_noexp) + " )")
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval][ncd_mesh_noexp]["KEYWORDS_MESH_NOEXP_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ search_term = url_encode(" OR ".join(ncds_mesh_noexp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH NOEXP"]["WITHOUT_KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh_noexp} AND ( {" OR ".join(KEYWORDS)} ) )' for ncd_mesh_noexp in ncds_mesh_noexp]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH NOEXP"]["KEYWORDS"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh_noexp} AND ( {" OR ".join(keywords_mesh)} ) )' for ncd_mesh_noexp in ncds_mesh_noexp]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH NOEXP"]["KEYWORDS_MESH"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh_noexp} AND ( {" OR ".join(keywords_mesh_noexp)} ) )' for ncd_mesh_noexp in ncds_mesh_noexp]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH NOEXP"]["KEYWORDS_MESH_NOEXP"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh_noexp} AND ( {" OR ".join(keywords_mesh + keywords_hubheading + keywords_mesh_site_proposition + keywords_mesh_proposition)} ) )' for ncd_mesh_noexp in ncds_mesh_noexp]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH NOEXP"]["KEYWORDS_MESH_ALL"] = get_count_for_year(2024, search_term, interval)
+
+ tmp = [f'( {ncd_mesh_noexp} AND ( {" OR ".join(keywords_mesh_noexp + keywords_hubheading_noexp + keywords_mesh_site_proposition_noexp + keywords_mesh_proposition_noexp)} ) )' for ncd_mesh_noexp in ncds_mesh_noexp]
+ search_term = url_encode(" OR ".join(tmp))
+ print(f"SEARCH_TERM: {search_term}")
+ result[interval]["ALL NCDS MESH NOEXP"]["KEYWORDS_MESH_NOEXP_ALL"] = get_count_for_year(2024, search_term, interval)
-for term in TERMS:
- data[term] = {}
- mesh = term + "[Mesh]"
- print("TERM: ", mesh)
+print(result)
- for interval in INTERVALS:
- print("INTERVAL: ", interval)
- counts = get_count_for_year(2024, mesh, interval)
- print(counts)
- data[term][interval] = counts
+with open(f"{RESULTS_DIR}/results.json", "w+") as json_file:
+ json.dump(result, json_file, indent=4)
-print(data)
\ No newline at end of file
+print("END")
\ No newline at end of file
diff --git a/dataSources/PubMed/doc/data_num_keyword_no_mesh.json b/dataSources/PubMed/doc/data_num_keyword_no_mesh.json
new file mode 100644
index 0000000000000000000000000000000000000000..439a04c1ef3183b9ac893cc943d51b1c3da3607f
--- /dev/null
+++ b/dataSources/PubMed/doc/data_num_keyword_no_mesh.json
@@ -0,0 +1,42 @@
+{
+ 'Noncommunicable+Diseases': {
+ 'month': {
+ 'max': 47, 'min': 17, 'avg': 34.0
+ }
+ },
+ 'Diabetes+Mellitus': {
+ 'month': {
+ 'max': 1622, 'min': 1015, 'avg': 1324.0833333333333
+ }
+ },
+ 'Neoplasms': {
+ 'month': {
+ 'max': 8468, 'min': 5817, 'avg': 7558.916666666667
+ }
+ },
+ 'Respiratory+Tract+Diseases': {
+ 'month': {
+ 'max': 4927, 'min': 3119, 'avg': 4292.583333333333
+ }
+ },
+ 'Cardiovascular+Diseases': {
+ 'month': {
+ 'max': 4565, 'min': 2981, 'avg': 4083.0833333333335
+ }
+ },
+ 'Mental+Health': {
+ 'month': {
+ 'max': 421, 'min': 269, 'avg': 358.0833333333333
+ }
+ },
+ 'Diabetes+Mellitus%2C+Type+1': {
+ 'month': {
+ 'max': 221, 'min': 121, 'avg': 173.33333333333334
+ }
+ },
+ 'Diabetes+Mellitus%2C+Type+2': {
+ 'month': {
+ 'max': 750, 'min': 447, 'avg': 608.4166666666666
+ }
+ }
+}
\ No newline at end of file
diff --git "a/dataSources/PubMed/results/tmp/\"Diabetes+Mellitus\".json" "b/dataSources/PubMed/results/tmp/\"Diabetes+Mellitus\".json"
index 2f1225b2c0c0a34e23afddf6c991fc0d34f665b5..f2cd357786655d4d7e0342867f757ffc9c189850 100644
--- "a/dataSources/PubMed/results/tmp/\"Diabetes+Mellitus\".json"
+++ "b/dataSources/PubMed/results/tmp/\"Diabetes+Mellitus\".json"
@@ -1 +1,287 @@
-{'Availability': {'week': {'max': 18, 'min': 2, 'avg': 6.452830188679245}, 'month': {'max': 39, 'min': 16, 'avg': 24.916666666666668}}, 'Affordability': {'week': {'max': 3, 'min': 0, 'avg': 0.5094339622641509}, 'month': {'max': 6, 'min': 0, 'avg': 2.1666666666666665}}, 'Essential medecins': {'week': {'max': 0, 'min': 0, 'avg': 0.0}, 'month': {'max': 0, 'min': 0, 'avg': 0.0}}, 'Care therapy': {'week': {'max': 1, 'min': 0, 'avg': 0.09433962264150944}, 'month': {'max': 2, 'min': 0, 'avg': 0.4166666666666667}}, 'Care health': {'week': {'max': 3, 'min': 0, 'avg': 0.7924528301886793}, 'month': {'max': 7, 'min': 1, 'avg': 2.9166666666666665}}, 'Health Expenditures': {'week': {'max': 3, 'min': 0, 'avg': 0.8113207547169812}, 'month': {'max': 6, 'min': 0, 'avg': 3.1666666666666665}}, 'Health care costs': {'week': {'max': 4, 'min': 0, 'avg': 1.320754716981132}, 'month': {'max': 8, 'min': 3, 'avg': 5.5}}, 'Market': {'week': {'max': 7, 'min': 0, 'avg': 1.6603773584905661}, 'month': {'max': 16, 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+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Health legislation': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Policy evaluation': {'week': {'max': 1, 'min': 0, 'avg': 0.1320754716981132
+ }, 'month': {'max': 1, 'min': 0, 'avg': 0.5
+ }
+ }, 'Policy analysis': {'week': {'max': 2, 'min': 0, 'avg': 0.09433962264150944
+ }, 'month': {'max': 2, 'min': 0, 'avg': 0.4166666666666667
+ }
+ }, 'Policy formulation': {'week': {'max': 1, 'min': 0, 'avg': 0.018867924528301886
+ }, 'month': {'max': 1, 'min': 0, 'avg': 0.08333333333333333
+ }
+ }, 'Regulation': {'week': {'max': 39, 'min': 11, 'avg': 24.60377358490566
+ }, 'month': {'max': 109, 'min': 85, 'avg': 95.83333333333333
+ }
+ }, 'Governance': {'week': {'max': 2, 'min': 0, 'avg': 0.49056603773584906
+ }, 'month': {'max': 5, 'min': 0, 'avg': 1.8333333333333333
+ }
+ }, 'Global initiatives and organizations ': {'week': {'max': 2, 'min': 0, 'avg': 0.2830188679245283
+ }, 'month': {'max': 2, 'min': 0, 'avg': 1.0833333333333333
+ }
+ }, 'Universal Health Care': {'week': {'max': 1, 'min': 0, 'avg': 0.05660377358490566
+ }, 'month': {'max': 1, 'min': 0, 'avg': 0.25
+ }
+ }, 'Expansion': {'week': {'max': 8, 'min': 0, 'avg': 2.0943396226415096
+ }, 'month': {'max': 16, 'min': 2, 'avg': 8.333333333333334
+ }
+ }, 'Health insurance': {'week': {'max': 10, 'min': 0, 'avg': 5.09433962264151
+ }, 'month': {'max': 29, 'min': 13, 'avg': 19.5
+ }
+ }, 'Coverage': {'week': {'max': 6, 'min': 0, 'avg': 2.9245283018867925
+ }, 'month': {'max': 17, 'min': 5, 'avg': 11.166666666666666
+ }
+ }, 'Funding and investment': {'week': {'max': 3, 'min': 0, 'avg': 0.5849056603773585
+ }, 'month': {'max': 5, 'min': 0, 'avg': 2.3333333333333335
+ }
+ }, 'Health planning': {'week': {'max': 1, 'min': 0, 'avg': 0.11320754716981132
+ }, 'month': {'max': 2, 'min': 0, 'avg': 0.5833333333333334
+ }
+ }, 'Health reform': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Policy monitoring': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Public health campaign': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Policy lobbying': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Patient advocacy': {'week': {'max': 2, 'min': 0, 'avg': 0.1320754716981132
+ }, 'month': {'max': 2, 'min': 0, 'avg': 0.5833333333333334
+ }
+ }, 'Justice': {'week': {'max': 2, 'min': 0, 'avg': 0.24528301886792453
+ }, 'month': {'max': 3, 'min': 0, 'avg': 0.9166666666666666
+ }
+ }, 'Awareness campaign': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Education': {'week': {'max': 88, 'min': 33, 'avg': 55.924528301886795
+ }, 'month': {'max': 292, 'min': 175, 'avg': 221.58333333333334
+ }
+ }, 'Corporate accountability': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Social determinants of health': {'week': {'max': 5, 'min': 0, 'avg': 2.69811320754717
+ }, 'month': {'max': 14, 'min': 8, 'avg': 10.75
+ }
+ }, 'Empowerment': {'week': {'max': 5, 'min': 0, 'avg': 0.9622641509433962
+ }, 'month': {'max': 5, 'min': 1, 'avg': 3.3333333333333335
+ }
+ }, 'Community': {'week': {'max': 40, 'min': 8, 'avg': 24.566037735849058
+ }, 'month': {'max': 120, 'min': 71, 'avg': 96.16666666666667
+ }
+ }, 'Peer support': {'week': {'max': 4, 'min': 0, 'avg': 0.8679245283018868
+ }, 'month': {'max': 5, 'min': 1, 'avg': 3.3333333333333335
+ }
+ }, 'Civil society': {'week': {'max': 1, 'min': 0, 'avg': 0.03773584905660377
+ }, 'month': {'max': 1, 'min': 0, 'avg': 0.16666666666666666
+ }
+ }, 'Patient education': {'week': {'max': 11, 'min': 1, 'avg': 4.981132075471698
+ }, 'month': {'max': 28, 'min': 11, 'avg': 19.166666666666668
+ }
+ }, 'Parent education': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Educational materials': {'week': {'max': 1, 'min': 0, 'avg': 0.1509433962264151
+ }, 'month': {'max': 2, 'min': 0, 'avg': 0.6666666666666666
+ }
+ }, 'Community heatlh education': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }, 'Awareness ': {'week': {'max': 14, 'min': 2, 'avg': 6.547169811320755
+ }, 'month': {'max': 38, 'min': 18, 'avg': 25.333333333333332
+ }
+ }, 'Community engagement': {'week': {'max': 2, 'min': 0, 'avg': 0.24528301886792453
+ }, 'month': {'max': 2, 'min': 0, 'avg': 1.0
+ }
+ }, 'Health literacy': {'week': {'max': 5, 'min': 0, 'avg': 1.5471698113207548
+ }, 'month': {'max': 10, 'min': 3, 'avg': 6.25
+ }
+ }, 'Medical education': {'week': {'max': 6, 'min': 0, 'avg': 3.5849056603773586
+ }, 'month': {'max': 20, 'min': 6, 'avg': 14.25
+ }
+ }, 'Training program': {'week': {'max': 5, 'min': 0, 'avg': 1.4150943396226414
+ }, 'month': {'max': 11, 'min': 2, 'avg': 5.833333333333333
+ }
+ }, 'Technology education': {'week': {'max': 1, 'min': 0, 'avg': 0.05660377358490566
+ }, 'month': {'max': 1, 'min': 0, 'avg': 0.25
+ }
+ }, 'Medical devices': {'week': {'max': 5, 'min': 0, 'avg': 1.3962264150943395
+ }, 'month': {'max': 8, 'min': 2, 'avg': 5.416666666666667
+ }
+ }, 'Information Dissemination': {'week': {'max': 2, 'min': 0, 'avg': 0.11320754716981132
+ }, 'month': {'max': 2, 'min': 0, 'avg': 0.4166666666666667
+ }
+ }, 'Behavioral change': {'week': {'max': 1, 'min': 0, 'avg': 0.20754716981132076
+ }, 'month': {'max': 3, 'min': 0, 'avg': 1.0833333333333333
+ }
+ }, 'Nutrition education': {'week': {'max': 3, 'min': 0, 'avg': 0.5660377358490566
+ }, 'month': {'max': 4, 'min': 1, 'avg': 2.3333333333333335
+ }
+ }, 'Risk communication': {'week': {'max': 1, 'min': 0, 'avg': 0.07547169811320754
+ }, 'month': {'max': 1, 'min': 0, 'avg': 0.25
+ }
+ }, 'Sector integration': {'week': {'max': 0, 'min': 0, 'avg': 0.0
+ }, 'month': {'max': 0, 'min': 0, 'avg': 0.0
+ }
+ }
+}
\ No newline at end of file
diff --git a/dataSources/PubMed/util.py b/dataSources/PubMed/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d7c8c9484861021f1709ea7244654658c2bab6c
--- /dev/null
+++ b/dataSources/PubMed/util.py
@@ -0,0 +1,28 @@
+import urllib.parse
+
+def url_encode(text):
+ return urllib.parse.quote_plus(text, safe='[]():"')
+
+def get_mesh_term(terms):
+ res = []
+
+ for item in terms.values():
+ if isinstance(item, list):
+ tmp = [f'"{i}"[Mesh]' for i in item]
+ res.append(" AND ".join(tmp))
+ else:
+ res.append(f'"{item}"[Mesh]')
+
+ return res
+
+def get_mesh_noexp_term(terms):
+ res = []
+
+ for item in terms.values():
+ if isinstance(item, list):
+ tmp = [f'"{i}"[Mesh:noexp]' for i in item]
+ res.append(f'( {" AND ".join(tmp)} )')
+ else:
+ res.append(f'"{item}"[Mesh:noexp]')
+
+ return res
diff --git a/models/ZeroShotClassifier/HuggingFace/llm.py b/models/ZeroShotClassifier/HuggingFace/llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..d210bc4bd0aee94fa6e6f05de4e2291d3e6b33ed
--- /dev/null
+++ b/models/ZeroShotClassifier/HuggingFace/llm.py
@@ -0,0 +1,30 @@
+from transformers import pipeline
+import torch
+
+MODELS = [
+ "deepseek-ai/DeepSeek-V3" #https://huggingface.co/deepseek-ai/DeepSeek-V3
+]
+
+def create_generator(model = MODELS[0]):
+ print(f" CUDA available: {torch.cuda.is_available()}")
+ print(f"CUDA version: {torch.version.cuda}")
+ print(f"GPUs number: {torch.cuda.device_count()}")
+ device = 0 if torch.cuda.is_available() else -1
+ return pipeline("text-generation", model=model, device=device)
+
+
+def generate(generator, sequence, debug = False):
+ results = generator(sequence)
+
+ if debug:
+ print(f"Sequence: {sequence}")
+ print(f"Labels: {results['labels']}")
+ print(f"Scores: {results['scores']}")
+
+ return results
+
+generator = create_generator()
+
+res = generate(generator, "Hi, how are you?")
+
+print(res)
diff --git a/models/tests/__pycache__/ollama.cpython-313.pyc b/models/tests/__pycache__/ollama.cpython-313.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..44f2937a284ffb97993af5fef33a77c59a10c0d2
Binary files /dev/null and b/models/tests/__pycache__/ollama.cpython-313.pyc differ
diff --git a/models/tests/ollama_test.py b/models/tests/ollama_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c0bdf31811c59791ed4d285b3d659a24f6e47b3
--- /dev/null
+++ b/models/tests/ollama_test.py
@@ -0,0 +1,12 @@
+from ollama import chat
+from ollama import ChatResponse
+
+response: ChatResponse = chat(model='llama3.2', messages=[
+ {
+ 'role': 'user',
+ 'content': 'Why is the sky blue?',
+ },
+])
+print(response['message']['content'])
+# or access fields directly from the response object
+print(response.message.content)
\ No newline at end of file
diff --git a/models/tests/sources.txt b/models/tests/sources.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d868a50379766993bdd7f6d5f1302b1dad2a2f9a
--- /dev/null
+++ b/models/tests/sources.txt
@@ -0,0 +1,20 @@
+- https://github.com/ollama/ollama-python
+- https://github.com/ollama/ollama
+
+Check Bedrock:
+- https://aws.amazon.com/bedrock/
+
+Check Tokenizers:
+- https://huggingface.co/docs/tokenizers/quicktour
+
+Fine-tunning Zero-shot classifier:
+- https://stackoverflow.com/questions/76213873/how-to-finetune-a-zero-shot-model-for-text-classification
+
+
+TODO:
+- Store data from Pubmed localy for NCDs
+- Calculate data published
+- Better testing script
+- Get avrage token number for pubmed article
+- Make prica estimations for LLMs
+- Look and teste new LLMs (local and cloud)
\ No newline at end of file
diff --git a/variables/__pycache__/pubmed.cpython-313.pyc b/variables/__pycache__/pubmed.cpython-313.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f342258b8039b2dfbad3a7041bbe0bce7765fc20
Binary files /dev/null and b/variables/__pycache__/pubmed.cpython-313.pyc differ
diff --git a/variables/pubmed.py b/variables/pubmed.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f8b298feffc045fe17fceb562183ff81d5cc38c
--- /dev/null
+++ b/variables/pubmed.py
@@ -0,0 +1,215 @@
+PUBMED_API_KEY = "63d31fa6fc74a5b632d3560046df75748208"
+
+NCDS = [
+ "Non-Communicable Diseases",
+ "Diabetes",
+ "Cancer",
+ "Chronic respiratory disease",
+ "Cardiovascular diseases",
+ "Mental Health",
+ "Diabetes type 1",
+ "Diabetes type 2"
+]
+
+NCDS_MESH_TERM = {
+ 'Non-Communicable Diseases': "Noncommunicable Diseases",
+ 'Diabetes': "Diabetes Mellitus",
+ 'Cancer': "Neoplasms",
+ 'Chronic respiratory disease': "Respiratory Tract Diseases",
+ 'Cardiovascular diseases': "Cardiovascular Diseases",
+ 'Mental Health': "Mental Health",
+ 'Diabetes type 1': "Diabetes Mellitus, Type 1",
+ 'Diabetes type 2': "Diabetes Mellitus, Type 2"
+}
+
+KEYWORDS = [
+ "Availability",
+ "Affordability",
+ "Essential medecins",
+ "Care therapy",
+ "Care health",
+ "Health Expenditures",
+ "Health care costs",
+ "Market",
+ "Special populations",
+ "Child Health",
+ "Womens Health",
+ "Age",
+ "Minority",
+ "Primary Care",
+ "Specialty Care",
+ "Patient acceptance",
+ "Patient centered care",
+ "Prevention and control",
+ "Mass screening",
+ "Palliative care",
+ "Quality",
+ "Telemedicine",
+ "Digital health",
+ "Supplies",
+ "Human Resources",
+ "Enablers/barriers",
+ "Gender equity",
+ "Racial",
+ "Equity",
+ "Clinical",
+ "Health promotion",
+ "Health education",
+ "Research & Innovation",
+ "Therapeutic Development",
+ "Technological Development",
+ "Self-management",
+ "Self-monitoring",
+ "Dosing",
+ "Injections",
+ "Secondary Care",
+ "Integrated Care",
+ "Treatment management",
+ "Immunization",
+ "Vaccination",
+ "Adherence",
+ "Control",
+ "Rehabilitation services",
+ "Clinical guidelines",
+ "Health policy",
+ "Healthcare policy",
+ "National health policy",
+ "Regional health policy",
+ "Health legislation",
+ "Policy evaluation",
+ "Policy analysis",
+ "Policy formulation",
+ "Regulation",
+ "Governance",
+ "Global initiatives and organizations",
+ "Universal Health Care",
+ "Expansion",
+ "Health insurance",
+ "Coverage",
+ "Funding and investment",
+ "Health planning",
+ "Health reform",
+ "Policy monitoring",
+ "Public health campaign",
+ "Policy lobbying",
+ "Patient advocacy",
+ "Justice",
+ "Awareness campaign",
+ "Education",
+ "Corporate accountability",
+ "Social determinants of health",
+ "Empowerment",
+ "Community",
+ "Peer support",
+ "Civil society",
+ "Patient education",
+ "Parent education",
+ "Educational materials",
+ "Community heatlh education",
+ "Awareness",
+ "Community engagement",
+ "Health literacy",
+ "Medical education",
+ "Training program",
+ "Technology education",
+ "Medical devices",
+ "Information Dissemination",
+ "Behavioral change",
+ "Nutrition education",
+ "Risk communication",
+ "Sector integration"
+]
+
+KEYWORDS_MESH_TERM = {
+ 'Health Expenditures': "Health Expenditures",
+ 'Health care costs': "Health Care Costs",
+ 'Patient centered care': "Patient-Centered Care",
+ 'Mass screening': "Mass Screening",
+ 'Palliative care': "Palliative Care",
+ 'Telemedicine': "Telemedicine",
+ 'Digital health': "Digital Health",
+ 'Gender equity': "Gender Equity",
+ 'Health promotion': "Health Promotion",
+ 'Health education': "Health Education",
+ 'Self-management': "Self-Management",
+ 'Injections': "Injections",
+ 'Secondary Care': "Secondary Care",
+ 'Immunization': "Immunization",
+ 'Vaccination': "Vaccination",
+ 'Health policy': "Health Policy",
+ 'Universal Health Care': "Universal Health Care",
+ 'Health insurance': "Insurance, Health",
+ 'Health planning': "Health Planning",
+ 'Patient advocacy': "Patient Advocacy",
+ 'Education': "Education",
+ 'Social determinants of health': "Social Determinants of Health",
+ 'Empowerment': "Empowerment",
+ 'Awareness': "Awareness",
+ 'Health literacy': "Health Literacy",
+ 'Medical education': "Education, Medical",
+ 'Information Dissemination': "Information Dissemination"
+}
+
+KEYWORDS_MESH_SUBHEADING = {
+ 'Prevention and control': "prevention and control"
+}
+
+KEYWORDS_MESH_SITE_PROPOSITION = {
+ 'Affordability': "Costs and Cost Analysis",
+ 'Special populations': "Health Disparate Minority and Vulnerable Populations",
+ 'Technological Development': "Sustainable Development",
+ 'Self-monitoring': "Blood Glucose Self-Monitoring",
+ 'Treatment management': "Patient Care Management",
+ 'Healthcare policy': "Health Policy",
+ 'National health policy': "Health Policy",
+ 'Policy analysis': "Policy Making",
+ 'Awareness campaign': "Immunization Programs",
+ 'Civil society': "Social Justice",
+ 'Parent education': "Prenatal Education",
+ 'Educational materials': "Educational Technology",
+ 'Community heatlh education': "Health Education"
+}
+
+KEYWORDS_MESH_PROPOSITION = {
+'Availability': "Biological Availability",
+'Care therapy': "Palliative Care",
+'Care health': "Primary Health Care",
+'Market': "marketing",
+'Age': "Age Groups",
+'Minority': "Minority Groups",
+'Primary Care': "Primary Health Care",
+'Patient acceptance': "Patient Acceptance of Health Care",
+'Quality': "Quality Assurance, Health Care",
+'Supplies': "Equipment and Supplies",
+'Human Resources': "Workforce",
+'Racial': "Racial Groups",
+'Equity': "Diversity, Equity, Inclusion",
+'Clinical': "ambulatory care facilities",
+'Research & Innovation': "Research",
+'Therapeutic Development': ["Therapeutics", "Growth and Development"],
+'Integrated Care': "Delivery of Health Care, Integrated",
+'Adherence': "Assessment of Medication Adherence",
+'Control': "Control Groups",
+'Rehabilitation services': "Rehabilitation",
+'Clinical guidelines': ["ambulatory care facilities", "Guidelines as Topic"],
+'Regional health policy': ["geographic locations", "Health Policy"],
+'Health legislation': "Legislation as Topic",
+'Policy evaluation': "policy",
+'Policy formulation': "Policy Making",
+'Regulation': "Social Control, Formal",
+'Governance': "Clinical Governance",
+'Coverage': "Preexisting Condition Coverage",
+'Funding and investment': ["economics", "investments"],
+'Health reform': "Health Care Reform",
+'Public health campaign': "public health",
+'Policy lobbying': ["policy", "lobbying"],
+'Justice': "Social Justice",
+'Community': "Residence Characteristics",
+'Patient education': "Patient Education as Topic",
+'Training program': "Education",
+'Technology education': ["technology", "Education"],
+'Medical devices': "Equipment and Supplies",
+'Behavioral change': "behavior",
+'Nutrition education': ["nutritional sciences", "Education"],
+'Risk communication': ["risk", "communication"]
+}
\ No newline at end of file