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CreateTableDataset.py
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CreateTableDataset.py
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'''
Created on 10 Mar 2016
@author: mbaxkhm4
Created at the University of Manchester, School of Computer Science
Licence GNU/GPL 3.0
'''
import MySQLdb
def processTable(tableid,clas):
print tableid
db = MySQLdb.connect("localhost","root","","table_db")
containsDemographic = 0
containsPatientsCap = 0
containsPatientsCell = 0
containsBaseline = 0
containsAge = 0
containsN = 0
containsTrialCap = 0
containsCharacteristicCap = 0
containsEglibility= 0
containsInclusionExclusionCell=0
containsToxicity = 0
containsHaematologic = 0
containsCriteria = 0
NoColumn = 0
NoRows = 0
ContainsInclusion = 0
ContainsExclusion = 0
CellNumeric = 0
CellText = 0
CellEmpty = 0
CellSemiNumeric =0
TotalCellNo = 0
ContainsAdverse = 0
ContainsSideEffect = 0
ContainsSignOrSymptomAnnotation = 0
Caption = ""
cursor = db.cursor()
sql = "select * from arttable where idTable="+str(tableid)
cursor.execute(sql)
results = cursor.fetchall()
for res in results:
Caption = res[2]
cursor = db.cursor()
sql = "select * from cell where Table_idTable="+str(tableid)
cursor.execute(sql)
results = cursor.fetchall()
for res in results:
if(res[2]=="Empty"):
CellEmpty=CellEmpty+1
if(res[2]=="Text"):
CellText=CellText+1
if(res[2]=="Partially Numeric"):
CellSemiNumeric=CellSemiNumeric+1
if(res[2]=="Numeric"):
CellNumeric=CellNumeric+1
TotalCellNo = TotalCellNo+1
if NoRows< res[4]:
NoRows = res[4]
if NoColumn< res[5]:
NoColumn = res[5]
if ("patient" in res[9]) or ("Patient" in res[9]):
containsPatientsCell = 1
if ("age" in res[9].lower()):
containsAge = 1
if ("n=" in res[9].lower()) or ("n =" in res[9].lower()):
containsN = 1
if(( "inclusion" in res[9].lower()) or ("exclusion" in res[9].lower())):
containsInclusionExclusionCell = 1
NoRows = NoRows+1
NoColumn = NoColumn+1
if ("patient" in Caption) or ("Patient" in Caption):
containsPatientsCap = 1
if("characteristic" in Caption) or ("Characteristic" in Caption):
containsCharacteristicCap = 1
if("demograph" in Caption) or ("Demograph" in Caption):
containsDemographic = 1
if("baseline" in Caption) or ("Baseline" in Caption):
containsBaseline = 1
if("Trial" in Caption) or ("trial" in Caption):
containsTrialCap = 1
if("Inclusion" in Caption) or ("inclusion" in Caption):
ContainsInclusion = 1
if("Exclusion" in Caption) or ("exclusion" in Caption):
ContainsExclusion = 1
if("Adverse" in Caption) or ("adverse" in Caption):
ContainsAdverse = 1
if("side effect" in Caption.lower()):
ContainsSideEffect = 1
if("toxicity" in Caption.lower()):
containsToxicity = 1
if("haematologic" in Caption.lower()):
containsHaematologic = 1
if("eligibi" in Caption.lower()):
containsEglibility = 1
if("criteri" in Caption.lower()):
containsCriteria = 1
cursor = db.cursor()
sql = "select * from annotation inner join cell on cell.idCell=annotation.Cell_idCell where annotation.AgentName='MetaMap' and AnnotationDescription LIKE '%Symptom%' and Table_idTable="+str(tableid)
cursor.execute(sql)
results = cursor.fetchall()
if len(results)>0:
ContainsSignOrSymptomAnnotation = 1
CellEmpty = (float(CellEmpty)/float(TotalCellNo))*100
CellText = (float(CellText)/float(TotalCellNo))*100
CellNumeric = (float(CellNumeric)/float(TotalCellNo))*100
CellSemiNumeric = (float(CellSemiNumeric)/float(TotalCellNo))*100
outputline = str(tableid)+","+str(NoRows)+","+str(NoColumn)+","+ str(TotalCellNo)+","+str(CellEmpty)+","+str(CellNumeric)+","+str(CellSemiNumeric)+","+str(CellText)+","+str(ContainsAdverse)+","+str(containsAge)+","+str(containsBaseline)+","+str(containsCharacteristicCap)+","+str(containsDemographic)+","+str(ContainsExclusion)+","+str(ContainsInclusion)+","+str(containsN)+","+str(containsPatientsCap)+","+str(containsPatientsCell)+","+str(ContainsSideEffect)+","+str(ContainsSignOrSymptomAnnotation)+","+str(containsEglibility)+","+str(containsToxicity)+","+str(containsInclusionExclusionCell)+","+str(containsHaematologic) +","+str(containsTrialCap) +","+str(containsCriteria)+","+clas+"\n"
return outputline
if __name__=="__main__":
dmeographicTables = [10,20,26,30,34,39,43,47,51,55,60,64,66,72,1656,3508,3572,12673,160,198,276,297,341,355,365,466,521,567,649,679,713,876,882,884,947,984,1067,1078,1122,1125,1146,1168,1180,1200,1206,1271,1273,1293,
1401,1481,1499,1527,1577,1584,1631,1675,1693,1715,1767,1837,1879,102,111,116,148, 150, 190,268,271,275,280,312,339,348,363,370,371,380,391,408,409,414,434,444,470,484,584,760,1588,1592,1597,1608,1612,1628,1636,1639,
1646,1656,1659,1666,1671,1688,1703,1707,1712,1724,1727,1734,1744, 9639,9658,9675,9697,9705,9718,9762,9788,9820,9844,9888,9890,9893,9895,9948,9954,
9997,10003,10050,10052,10055,10068,10072,10075,10132,10136,10169,10195,10212,10234,10255,10296,10297,
10299,10307,10311,10742,10746,10846,10863,10922,10927,10939,10945,10946,10947,10996,11026,89, 95,97,122,130,145,158,161,181,183,241,250,291,296,298,361,376,405,532,550,560,593,596,598,606,2932,2950,3727,4752]
nonInterestingTables = [1,2,3,4,5,6,8,9,11,12,13,14,16,17,18,19,21,22,23,24,25,28,29,31,32,35,37,38,40,41,42,44,45,46,48,49,50,52,53,54,
56,57,58,59,61,62,63,65,67,68,69,70,71,73,74,75,76,77,78,539,764,6348,7866,7867,99,100,101,106,134,230,265,1027,1096,1106,1141,
1172,1224,197,388,389,595,655,656,1139,1140,1479,1702,2365,8624,8625,8626,8633,8986,8987,8988,9053,9054,9055,9434,9887,
79,80,81,82,83,84,85,86,87,88,90,91,92,93,94,96,98,103,104,107,108,109,110,112,113,114,117,118,120,121,123,124,125,126,127,128,129,
131,132,133,134,135,136,137,138,1090,1096,290,292,294,295,299,3318]
inclusionExclusionTables = [15,33,105,497,622,674,848,874,875,1095,1121,1291,1332,1381,3061,3882,3883,4027,4408,4876,4915,4944,5541,5725,
6177,6313,6332,6562,6923,7355,7386,7572,7781,7932,8073,8191,8324,8372,8408,8478,8989,9210,9252,9393,9701,
9708,10199,10962,11246,11269,11298,11809,11983,11984,12138,12159,12214,12337,12343,12604,12674,260,866,1717,3291,
3850,4419,5201,5409,6122,866,7238,7238,7546,7572,8080,8176,8327,10519,10603,10901,11343]
adverseEventsTables = [7,36,115,394,428,440,464,765,766,771,799,944,1062,1091,1098,1102,1111,1112,1127,1170,1181,1209,1217,1243,1274,1277,
1279,1280,1285,1296,1297,1309,1390,1465,1466,1486,1515,1517,1544,1551,1585,1618,1624,1635,1664,1676,1764,1792,1800,1838,1871,1875,1916,1930,1947,1951,1958,
1959,1962,1963,1978,2013,2027,2045,2066,2161,2171,2249,2279,2286,2295,2316,2338,2374,2406,2532,2662,2699,2750,2778,2782,2885,2888,2923,
3155,3156,3162,3176,3251,3333,3338,3369,3371,3378,3379,3391,3399,3400,3419,3420,3478,3479,3484,3514,3543,3558,3592,3661,3736,3762,3766,
3811,3829,3848,3849,3888,3931,3979,4004,4055,4068,4093,4111,4136,4163,4202,4205,4206,4209,4223,4227,4312,4350,4351,4359,
4379,4380,4385,4406,4442,4485,4496,4532,4552,4559,4588,4589,4594,4595,4596,4610,4615,4617,4625,4629,4650,4664,4672,4673,4674,4675,4682,
4726,4781,4798,4850,4859,4882,4883,4923,4947,4963,4973,4975,4987,4988,4999,5000,5063,5092,5226,5240,5243,5284,5305,5322,8049,
8071,8107,8117,8139,8148, 293, 653, 1048,1658,1862,2466,2467,4127,4868,4901,4951,5292,5602,6186,6857,7052,7103,7104,7105,7506,7507,7508,7509,
7961,8163,8446,9505,11072,12375,12465,12466,12519,12559,61,68,140,257,314,439,486,492,627,742,3297,3296,3317,653]
# target = open("learnng2.csv", 'w')
# target.write("tableid,NoRows,NoColumn,TotalCellNo,CellEmpty,CellNumeric,CellSemiNumeric,CellText,ContainsAdverse,containsAge,containsBaseline,containsCharacteristicCap,containsDemographic,ContainsExclusion,ContainsInclusion,containsN,containsPatientsCap,containsPatientsCell,ContainsSideEffect,ContainsSignOrSymptomAnnotation,containsEglibility,containsToxicity,containsInclusionExclusionCell,containsHaematologic,containsCriteria,containsTrialCap,clas\n")
# for table in adverseEventsTables:
# target.write(processTable(table,"AdverseEvent"))
# for table in inclusionExclusionTables:
# target.write(processTable(table,"InclusionExclusion"))
# for table in nonInterestingTables:
# target.write(processTable(table,"Other"))
# for table in dmeographicTables:
# target.write(processTable(table,"BaselineCharacteristic"))
# target.close()
print len(dmeographicTables)
print len(nonInterestingTables)
print len(inclusionExclusionTables)
print len(adverseEventsTables)
#add like haematological