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data-maker
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removing all the wrong stuff

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master
Steve L. Nyemba 6 years ago
parent b9c003dc39
commit 9abb1e8166
4 changed files with 0 additions and 832 deletions
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511
exports/observation.csv
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@ -1,511 +0,0 @@
observation_id,person_id,observation_concept_id,observation_date,observation_datetime,observation_type_concept_id,value_as_number,value_as_string,value_as_concept_id,qualifier_concept_id,unit_concept_id,provider_id,visit_occurrence_id,observation_source_value,observation_source_concept_id,unit_source_value,qualifier_source_value,value_source_concept_id,value_source_value,questionnaire_response_id
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48984,10275,3637,10121,19454,1331,,,5490.0,,,,8210.0,11463,7184,,,,,
142573,10728,566,219,23469,1331,,,5490.0,,,,11746.0,496,7067,,,,,
64314,10728,955,9090,13934,1331,,,5490.0,,,,1623.0,9552,3593,,,,,
29365,550,1004,11019,22714,1331,,,5490.0,,,,1069.0,10961,10789,,,,,
49651,10728,5987,12068,15316,1331,,,5490.0,,,,11605.0,342,192,,,,,
29895,10728,1004,219,23469,1331,,,5490.0,,,,11746.0,10961,10789,,,,,
129088,10133,7565,3166,23012,1331,,,5490.0,,,,3341.0,776,3975,,,,,
133995,10728,1941,11365,1678,1331,,,5490.0,,,,12444.0,10167,10122,,,,,
72054,10728,7583,566,5478,1331,,,5490.0,,,,3541.0,4980,3722,,,,,
118335,5111,5987,2894,21368,1331,,,5490.0,,,,13287.0,4102,192,,,,,
11262,2750,2734,7502,17073,1331,,,5490.0,,,,10124.0,2539,1131,,,,,
36486,550,10802,4217,20407,1331,,,5490.0,,,,3988.0,2737,1719,,,,,
47382,10728,4351,9090,13934,1331,,,5490.0,,,,6252.0,8212,637,,,,,
84012,905,4351,8139,3884,1331,,,5490.0,,,,10668.0,7216,5174,,,,,
87336,550,6923,4149,20375,1331,,,5490.0,,,,3288.0,12145,5318,,,,,
17728,2750,6810,6337,1859,1331,,,5490.0,,,,2816.0,12107,8083,,,,,
40296,10133,3962,4401,13613,1331,,,5490.0,,,,452.0,9648,8086,,,,,
73394,10728,8841,3781,594,1331,,,5490.0,,,,7968.0,993,4939,,,,,
118410,10728,1004,1893,1726,1331,,,5490.0,,,,4674.0,6220,7308,,,,,
16806,905,4448,9667,19409,1331,,,5490.0,,,,11331.0,8695,7688,,,,,
66479,10133,3962,6053,20022,1331,,,5490.0,,,,,12005,4247,,,,,
90328,10728,4448,2393,7359,1331,,,5490.0,,,,2765.0,8695,7688,,,,,
46784,10133,3080,7412,12094,1331,,,5490.0,,,,183.0,4263,2367,,,,,
82216,10728,562,219,23469,1331,,,5490.0,,,,11746.0,1203,6643,,,,,
412,10728,566,12068,15316,1331,,,5490.0,,,,11605.0,496,7067,,,,,
5045,10728,3087,3398,8402,1331,,,5490.0,,,,3757.0,9062,3458,,,,,
22956,550,2734,9500,16635,1331,,,5490.0,,,,2304.0,5574,169,,,,,
142801,2750,6810,12753,13301,1331,,,5490.0,,,,1862.0,10079,4473,,,,,
151340,10728,7665,6300,5515,1331,,,5490.0,,,,916.0,11748,4356,,,,,
67296,10728,7583,696,9377,1331,,,5490.0,,,,6107.0,6030,5989,,,,,
41923,10728,8600,6300,5515,1331,,,5490.0,,,,916.0,8747,7989,,,,,
117410,10728,8127,11242,9728,1331,,,5490.0,,,,11470.0,9838,570,,,,,
139897,10728,4448,924,21423,1331,,,5490.0,,,,10542.0,12228,8718,,,,,
6300,2750,2734,8041,6798,1331,,,5490.0,,,,711.0,7698,1035,,,,,
71404,10728,4351,3781,594,1331,,,5490.0,,,,7968.0,4928,8818,,,,,
77825,905,1004,5863,1194,1331,,,5490.0,,,,6118.0,6220,7308,,,,,
94778,10728,4448,10522,4738,1331,,,5490.0,,,,9127.0,8695,7688,,,,,
91779,10728,566,9293,1136,1331,,,5490.0,,,,13617.0,496,7067,,,,,
40277,550,6648,11019,22714,1331,,,5490.0,,,,1069.0,5281,1610,,,,,
22792,10728,1941,1876,19568,1331,,,5490.0,,,,3734.0,10167,10122,,,,,
136217,10728,7565,2393,7359,1331,,,5490.0,,,,2765.0,293,5494,,,,,
125663,10728,7230,11242,9728,1331,,,5490.0,,,,11470.0,11277,10988,,,,,
66229,10133,1004,3166,23012,1331,,,5490.0,,,,3341.0,10961,10789,,,,,
9095,905,6805,7302,15180,1331,,,5490.0,,,,8712.0,9060,7178,,,,,
73836,10728,11044,3731,16960,1331,,,5490.0,,,,9412.0,3423,10109,,,,,
116018,10728,7230,10095,3395,1331,,,5490.0,,,,3630.0,11277,10988,,,,,
121817,10728,4448,1893,1726,1331,,,5490.0,,,,4674.0,8695,7688,,,,,
63363,550,2734,10891,5037,1331,,,5490.0,,,,13689.0,84,6098,,,,,
136254,10728,5205,3731,16960,1331,,,5490.0,,,,9412.0,6478,844,,,,,
148707,10728,1435,11806,11363,1331,,,5490.0,,,,3869.0,7993,7839,,,,,
127694,10728,5987,1893,1726,1331,,,5490.0,,,,4674.0,11454,8747,,,,,
78192,10728,566,12498,1026,1331,,,5490.0,,,,6554.0,496,7067,,,,,
52646,550,7709,4631,16086,1331,,,5490.0,,,,2249.0,3948,2570,,,,,
129120,550,1004,4149,20375,1331,,,5490.0,,,,3288.0,10961,10789,,,,,
118757,10728,2123,4717,2168,1331,,,5490.0,,,,1820.0,8751,11099,,,,,
33593,10728,1004,424,1544,1331,,,5490.0,,,,8084.0,6220,7308,,,,,
135746,10728,4351,2393,7359,1331,,,5490.0,,,,2765.0,12161,4725,,,,,
8324,10728,4351,4419,19766,1331,,,5490.0,,,,10053.0,8212,637,,,,,
44090,550,4448,3893,7064,1331,,,5490.0,,,,4949.0,12228,8718,,,,,
1094,10728,1004,2393,7359,1331,,,5490.0,,,,2765.0,6220,7308,,,,,
79187,10699,3080,9293,1136,1331,,,5490.0,,,,7844.0,8879,11361,,,,,
99692,10133,225,7412,12094,1331,,,5490.0,,,,183.0,9529,6714,,,,,
7697,10133,2870,3166,23012,1331,,,5490.0,,,,3341.0,4289,4768,,,,,
5405,10728,4351,11242,9728,1331,,,5490.0,,,,11470.0,1759,10739,,,,,
152451,10728,2142,9293,1136,1331,,,5490.0,,,,13617.0,9372,9736,,,,,
135625,10728,7565,424,1544,1331,,,5490.0,,,,8084.0,293,5494,,,,,
19823,550,5193,2070,12178,1331,,,5490.0,,,,12437.0,10497,7014,,,,,
62394,5111,1296,7887,21946,1331,,,5490.0,,,,4969.0,8029,266,,,,,
81412,10728,6597,6300,5515,1331,,,5490.0,,,,916.0,3346,10111,,,,,
101501,550,1004,3893,7064,1331,,,5490.0,,,,4949.0,6220,7308,,,,,
128554,905,4448,5863,1194,1331,,,5490.0,,,,6118.0,8695,7688,,,,,
27047,550,2734,9990,10656,1331,,,5490.0,,,,12360.0,10721,10970,,,,,
112501,550,10802,8847,13219,1331,,,5490.0,,,,1657.0,8133,1719,,,,,
100620,550,5987,9990,10656,1331,,,5490.0,,,,12360.0,8239,8155,,,,,
137771,905,4351,11886,22117,1331,,,5490.0,,,,1362.0,5029,1419,,,,,
99485,905,7565,5863,1194,1331,,,5490.0,,,,6118.0,293,5494,,,,,
86584,10728,7565,3072,22322,1331,,,5490.0,,,,11466.0,293,5494,,,,,
95473,10728,566,11365,1678,1331,,,5490.0,,,,12444.0,496,7067,,,,,
19487,550,1941,7932,18329,1331,,,5490.0,,,,11598.0,10167,10122,,,,,
35822,550,4351,12054,18743,1331,,,5490.0,,,,3604.0,463,7738,,,,,
8875,10728,1004,10522,4738,1331,,,5490.0,,,,9127.0,6220,7308,,,,,
45253,10728,8841,10095,3395,1331,,,5490.0,,,,3630.0,993,4939,,,,,
105563,550,6847,4769,1790,1331,,,5490.0,,,,2074.0,5605,8226,,,,,
44031,550,7565,4149,20375,1331,,,5490.0,,,,3288.0,293,5494,,,,,
137146,10728,9284,3072,22322,1331,,,5490.0,,,,11466.0,2691,5807,,,,,
64163,5111,3160,7887,21946,1331,,,5490.0,,,,4969.0,468,9518,,,,,
22025,905,4351,5863,1194,1331,,,5490.0,,,,6118.0,8607,3368,,,,,
10329,550,7565,11019,22714,1331,,,5490.0,,,,1069.0,6921,6205,,,,,
63434,2750,6810,4667,15577,1331,,,5490.0,,,,10048.0,12107,8083,,,,,
114290,10728,4351,2393,7359,1331,,,5490.0,,,,2765.0,8607,3368,,,,,
129485,10728,4351,2393,7359,1331,,,5490.0,,,,2765.0,2443,7969,,,,,
22412,10728,4351,6300,5515,1331,,,5490.0,,,,916.0,4928,8818,,,,,
53813,10728,3575,12068,15316,1331,,,5490.0,,,,11605.0,5361,4813,,,,,
133268,10699,2734,11063,13550,1331,,,5490.0,,,,537.0,6391,7789,,,,,
70729,10728,4448,424,1544,1331,,,5490.0,,,,8084.0,8695,7688,,,,,
37223,10728,566,6300,5515,1331,,,5490.0,,,,916.0,496,7067,,,,,
16349,10728,7565,10522,4738,1331,,,5490.0,,,,9127.0,293,5494,,,,,
8537,10699,4493,2622,15821,1331,,,5490.0,,,,10456.0,6634,2711,,,,,
109704,10728,4351,8923,17405,1331,,,5490.0,,,,13047.0,5695,3655,,,,,
55354,10728,1941,11242,9728,1331,,,5490.0,,,,11470.0,10167,10122,,,,,
67069,10728,7565,1893,1726,1331,,,5490.0,,,,4674.0,293,5494,,,,,
100888,2750,4277,4217,20407,1331,,,5490.0,,,,2827.0,1092,10993,,,,,
100249,10728,5987,6300,5515,1331,,,5490.0,,,,916.0,11454,8747,,,,,
2822,10728,1004,3072,22322,1331,,,5490.0,,,,11466.0,6220,7308,,,,,
56020,905,7565,9667,19409,1331,,,5490.0,,,,11331.0,293,5494,,,,,
16263,10728,7565,219,23469,1331,,,5490.0,,,,11746.0,293,5494,,,,,
12783,10699,4351,11563,8635,1331,,,5490.0,,,,1997.0,7144,8490,,,,,
99876,550,7565,3893,7064,1331,,,5490.0,,,,4949.0,293,5494,,,,,
48203,905,1004,9667,19409,1331,,,5490.0,,,,11331.0,6220,7308,,,,,
49641,10133,3962,10655,9580,1331,,,5490.0,,,,6815.0,12005,4247,,,,,
129655,10728,6910,6300,5515,1331,,,5490.0,,,,916.0,11748,4356,,,,,
128043,2750,4277,8041,6798,1331,,,5490.0,,,,711.0,1092,10993,,,,,
64265,10728,2142,6300,5515,1331,,,5490.0,,,,916.0,9372,9736,,,,,
1 observation_id person_id observation_concept_id observation_date observation_datetime observation_type_concept_id value_as_number value_as_string value_as_concept_id qualifier_concept_id unit_concept_id provider_id visit_occurrence_id observation_source_value observation_source_concept_id unit_source_value qualifier_source_value value_source_concept_id value_source_value questionnaire_response_id
2 118208 5557 9425 3823 10549 1331 16669.0 8936.0 11839 6242 1849.0 5535.0
3 112221 5557 1268 8176 4688 1331 16669.0 9908.0 7436 6037 1849.0 5535.0
4 92924 5557 1268 3823 13501 1331 16669.0 8043.0 7436 6037 1849.0 5535.0
5 87525 5557 562 6238 5732 1331 16669.0 2539.0 3555 914 1849.0 5535.0
6 88732 5557 1268 3823 10549 1331 16669.0 8936.0 7436 6037 1849.0 5535.0
7 127541 5230 255 4070 17510 1331 5490.0 8823.0 10678 7672
8 143650 665 5987 6705 10269 1331 5490.0 6992.0 11454 8747
9 69801 665 8220 11554 17750 1331 5490.0 6910.0 4645 7332
10 102810 665 3637 1222 15887 1331 5490.0 5139.0 5309 5692
11 143746 665 8499 3363 666 1331 5490.0 7948.0 5963 2112
12 70261 665 7654 11258 18677 1331 5490.0 12665.0 6282 333
13 40451 665 3637 1330 6520 1331 5490.0 5793.0 6548 7716
14 20543 665 6866 9228 15133 1331 5490.0 9252.0 11880 1768
15 100742 665 5987 11806 11363 1331 5490.0 4102 192
16 128493 665 3637 9520 13609 1331 5490.0 8390.0 5309 5692
17 118347 665 4084 11258 18677 1331 5490.0 12665.0 1637 10082
18 70737 665 9675 6995 21994 1331 5490.0 8156.0 11592 3413
19 16780 9034 2273 8988 12680 1331 5490.0 11630.0 7739 5531
20 48409 9034 2273 8530 10717 1331 5490.0 2681.0 7739 5531
21 95301 665 2123 3877 22319 1331 5490.0 12932.0 4701 11099
22 109187 665 8499 586 20186 1331 5490.0 13320.0 5963 2112
23 131936 665 3637 3019 1916 1331 5490.0 326.0 5309 5692
24 11545 665 9675 11258 18677 1331 5490.0 12665.0 229 9044
25 45240 665 9675 5659 7261 1331 5490.0 3579.0 3906 3214
26 94641 9034 2123 8988 12680 1331 5490.0 11630.0 9951 1400
27 61317 665 8499 12397 21443 1331 5490.0 7050.0 5963 2112
28 96121 665 5987 11258 18677 1331 5490.0 12665.0 271 2597
29 141495 9034 2273 4972 4053 1331 5490.0 10150.0 7739 5531
30 46013 665 7230 4975 8765 1331 5490.0 8632.0 7140 9976
31 27580 665 6866 10230 22399 1331 5490.0 11228.0 11880 1768
32 31663 665 1004 6701 19159 1331 5490.0 12712.0 6220 7308
33 53487 665 9425 9168 23137 1331 5490.0 3789.0 954 6242
34 96920 665 8499 7684 24162 1331 5490.0 2125.0 6421 2112
35 140655 665 566 11289 23244 1331 5490.0 8937.0 496 7067
36 22671 665 3637 7814 14867 1331 5490.0 3367.0 5309 5692
37 73299 665 5987 11801 10623 1331 5490.0 9541.0 8239 8155
38 33464 9034 5772 8988 12680 1331 5490.0 11630.0 5293 3180
39 116430 665 8499 9520 13609 1331 5490.0 8390.0 6421 2112
40 42612 665 566 11258 18677 1331 5490.0 12665.0 496 7067
41 151800 665 2123 6995 21994 1331 5490.0 8156.0 8751 11099
42 66963 665 5987 757 20626 1331 5490.0 2315.0 11454 8747
43 20955 665 9675 757 20626 1331 5490.0 2315.0 229 9044
44 29389 665 6866 9591 9107 1331 5490.0 8111.0 11880 1768
45 47723 665 8881 3877 22319 1331 5490.0 12932.0 9490 836
46 145483 665 8220 11289 23244 1331 5490.0 8937.0 4645 7332
47 148716 665 5987 1387 17146 1331 5490.0 1679.0 4102 192
48 60966 665 6866 6217 3474 1331 5490.0 7540.0 11880 1768
49 74964 665 8220 6705 10269 1331 5490.0 6992.0 4645 7332
50 111786 665 566 757 20626 1331 5490.0 2315.0 496 7067
51 41338 665 9675 10799 349 1331 5490.0 3164.0 5538 9044
52 27522 665 9675 6705 10269 1331 5490.0 6992.0 229 9044
53 64564 665 3637 8188 3756 1331 5490.0 3488.0 4974 7184
54 69978 665 7565 6701 19159 1331 5490.0 12712.0 293 5494
55 34968 665 3637 7684 24162 1331 5490.0 2125.0 5309 5692
56 23218 665 3637 245 13206 1331 5490.0 5309 5692
57 71119 665 8881 2149 3205 1331 5490.0 6073.0 9490 836
58 66535 665 566 6705 10269 1331 5490.0 6992.0 496 7067
59 65054 665 11121 11258 18677 1331 5490.0 12665.0 4843 5773
60 74272 665 566 11554 17750 1331 5490.0 6910.0 496 7067
61 110821 665 9675 2960 7987 1331 5490.0 3465.0 3906 3214
62 146780 665 3637 12397 21443 1331 5490.0 7050.0 6289 5692
63 53661 665 5987 11289 23244 1331 5490.0 8937.0 6804 10990
64 115297 9034 3637 10182 380 1331 5490.0 9044.0 11463 7184
65 88141 665 8220 11258 18677 1331 5490.0 12665.0 4645 7332
66 108101 665 5987 6705 10269 1331 5490.0 6992.0 271 2597
67 152503 665 5987 11554 17750 1331 5490.0 6910.0 271 2597
68 88754 665 5987 2960 7987 1331 5490.0 3465.0 271 2597
69 17476 9034 2273 12445 8047 1331 5490.0 13143.0 7739 5531
70 1955 665 3637 3363 666 1331 5490.0 7948.0 6289 5692
71 118939 665 3637 10248 5700 1331 5490.0 5350.0 6289 5692
72 115691 665 5987 7754 13286 1331 5490.0 13355.0 4102 192
73 111900 665 5987 11554 17750 1331 5490.0 6910.0 6804 10990
74 99133 665 4351 11554 17750 1331 5490.0 6910.0 3158 3294
75 33439 665 4351 10248 5700 1331 5490.0 5350.0 10401 2940
76 76750 665 4351 11258 18677 1331 5490.0 12665.0 3955 4266
77 101677 665 4351 11554 17750 1331 5490.0 6910.0 3955 4266
78 7658 665 4351 757 20626 1331 5490.0 2315.0 7938 758
79 8349 665 4351 11289 23244 1331 5490.0 8937.0 3955 4266
80 83441 665 4351 2960 7987 1331 5490.0 3465.0 3955 4266
81 31987 665 4351 757 20626 1331 5490.0 2315.0 3955 4266
82 149415 7552 10002 9293 1136 1331 5490.0 13107.0 9301 4502
83 109005 889 3080 2677 11631 1331 5490.0 12081 1503
84 51503 889 255 1957 19548 1331 5490.0 10678 7672
85 92084 584 8881 933 23961 1331 5490.0 5399.0 1933 836
86 20482 584 8220 286 19980 1331 5490.0 9158.0 2464 7332
87 142288 584 3637 6366 21753 1331 5490.0 6548 7716
88 26495 584 8220 7954 2585 1331 5490.0 9965.0 2464 7332
89 44120 889 5772 12734 19047 1331 5490.0 4378 10734
90 25470 584 8220 6297 10755 1331 5490.0 9721.0 2464 7332
91 55155 584 8220 12707 5062 1331 5490.0 10641.0 2464 7332
92 59236 584 3228 8590 11654 1331 5490.0 11500.0 6818 10843
93 51768 584 7973 913 21672 1331 5490.0 3441.0 7416 7132
94 46767 889 5987 1789 4216 1331 5490.0 4102 192
95 98773 584 8220 8901 6846 1331 5490.0 1663.0 2464 7332
96 102018 889 1815 995 12664 1331 5490.0 4139.0 7236 6845
97 16147 889 9425 3510 1739 1331 5490.0 3252.0 2685 6242
98 37396 889 4351 4335 5449 1331 5490.0 11376.0 2443 7969
99 110428 889 3637 8343 20660 1331 5490.0 3918.0 5309 5692
100 11390 889 8609 2960 7987 1331 5490.0 8379 1288
101 18726 584 2123 2873 3314 1331 5490.0 7270.0 9951 1400
102 8677 889 5987 2836 20839 1331 5490.0 11801.0 4102 192
103 31211 889 5904 1957 19548 1331 5490.0 2790 3686
104 123923 584 8220 693 17394 1331 5490.0 2464 7332
105 88350 584 8220 1675 17892 1331 5490.0 1614.0 2464 7332
106 130425 584 8220 11192 21322 1331 5490.0 11314.0 4645 7332
107 17957 584 8220 913 21672 1331 5490.0 3441.0 2464 7332
108 148169 889 4351 4162 20465 1331 5490.0 8503.0 4435 7969
109 46432 889 9425 6584 13541 1331 5490.0 6965.0 2685 6242
110 117012 889 4351 765 5507 1331 5490.0 12148.0 1376 374
111 127289 889 1815 9018 16763 1331 5490.0 9990.0 3899 6845
112 129839 889 8499 7312 24175 1331 5490.0 9424.0 6421 2112
113 137161 889 3637 6416 9237 1331 5490.0 1274.0 5309 5692
114 86281 889 5987 564 14434 1331 5490.0 4102 192
115 121552 889 9425 12206 19851 1331 5490.0 10580.0 2685 6242
116 5523 584 8220 8187 8921 1331 5490.0 5043.0 2464 7332
117 131839 584 8220 3520 5888 1331 5490.0 10633.0 2464 7332
118 75866 584 2123 6883 20216 1331 5490.0 6350.0 8751 11099
119 71809 889 5987 6911 21649 1331 5490.0 1570.0 342 192
120 134463 889 10181 3070 14517 1331 5490.0 2327.0 10757 6956
121 39004 584 8220 9583 4888 1331 5490.0 5606.0 2464 7332
122 89639 889 3637 12206 19851 1331 5490.0 10580.0 5309 5692
123 73506 8266 2704 2555 12270 1331 5490.0 9705.0 9842 670
124 139715 2487 10289 8577 23785 1331 5490.0 2909.0 11400 5264
125 92298 2487 10289 2836 20839 1331 5490.0 12203.0 11400 5264
126 112860 2487 10289 5502 18949 1331 5490.0 5401.0 11400 5264
127 94341 2487 10289 10960 23341 1331 5490.0 12230.0 11400 5264
128 84661 2487 5987 8577 23785 1331 5490.0 2909.0 11655 8155
129 127815 8266 2704 8418 22417 1331 5490.0 5987.0 9842 670
130 114637 2487 9284 11406 21765 1331 5490.0 8760.0 2051 9025
131 120662 8266 2734 2555 12270 1331 5490.0 3356.0 6992 3891
132 77654 2487 10289 10543 11301 1331 5490.0 5797.0 11400 5264
133 52859 2487 1941 865 18965 1331 5490.0 1317.0 10167 10122
134 99667 2487 5987 10402 21256 1331 5490.0 4198.0 11655 8155
135 39458 2487 10289 10402 21256 1331 5490.0 4198.0 11400 5264
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432 6300 2750 2734 8041 6798 1331 5490.0 711.0 7698 1035
433 71404 10728 4351 3781 594 1331 5490.0 7968.0 4928 8818
434 77825 905 1004 5863 1194 1331 5490.0 6118.0 6220 7308
435 94778 10728 4448 10522 4738 1331 5490.0 9127.0 8695 7688
436 91779 10728 566 9293 1136 1331 5490.0 13617.0 496 7067
437 40277 550 6648 11019 22714 1331 5490.0 1069.0 5281 1610
438 22792 10728 1941 1876 19568 1331 5490.0 3734.0 10167 10122
439 136217 10728 7565 2393 7359 1331 5490.0 2765.0 293 5494
440 125663 10728 7230 11242 9728 1331 5490.0 11470.0 11277 10988
441 66229 10133 1004 3166 23012 1331 5490.0 3341.0 10961 10789
442 9095 905 6805 7302 15180 1331 5490.0 8712.0 9060 7178
443 73836 10728 11044 3731 16960 1331 5490.0 9412.0 3423 10109
444 116018 10728 7230 10095 3395 1331 5490.0 3630.0 11277 10988
445 121817 10728 4448 1893 1726 1331 5490.0 4674.0 8695 7688
446 63363 550 2734 10891 5037 1331 5490.0 13689.0 84 6098
447 136254 10728 5205 3731 16960 1331 5490.0 9412.0 6478 844
448 148707 10728 1435 11806 11363 1331 5490.0 3869.0 7993 7839
449 127694 10728 5987 1893 1726 1331 5490.0 4674.0 11454 8747
450 78192 10728 566 12498 1026 1331 5490.0 6554.0 496 7067
451 52646 550 7709 4631 16086 1331 5490.0 2249.0 3948 2570
452 129120 550 1004 4149 20375 1331 5490.0 3288.0 10961 10789
453 118757 10728 2123 4717 2168 1331 5490.0 1820.0 8751 11099
454 33593 10728 1004 424 1544 1331 5490.0 8084.0 6220 7308
455 135746 10728 4351 2393 7359 1331 5490.0 2765.0 12161 4725
456 8324 10728 4351 4419 19766 1331 5490.0 10053.0 8212 637
457 44090 550 4448 3893 7064 1331 5490.0 4949.0 12228 8718
458 1094 10728 1004 2393 7359 1331 5490.0 2765.0 6220 7308
459 79187 10699 3080 9293 1136 1331 5490.0 7844.0 8879 11361
460 99692 10133 225 7412 12094 1331 5490.0 183.0 9529 6714
461 7697 10133 2870 3166 23012 1331 5490.0 3341.0 4289 4768
462 5405 10728 4351 11242 9728 1331 5490.0 11470.0 1759 10739
463 152451 10728 2142 9293 1136 1331 5490.0 13617.0 9372 9736
464 135625 10728 7565 424 1544 1331 5490.0 8084.0 293 5494
465 19823 550 5193 2070 12178 1331 5490.0 12437.0 10497 7014
466 62394 5111 1296 7887 21946 1331 5490.0 4969.0 8029 266
467 81412 10728 6597 6300 5515 1331 5490.0 916.0 3346 10111
468 101501 550 1004 3893 7064 1331 5490.0 4949.0 6220 7308
469 128554 905 4448 5863 1194 1331 5490.0 6118.0 8695 7688
470 27047 550 2734 9990 10656 1331 5490.0 12360.0 10721 10970
471 112501 550 10802 8847 13219 1331 5490.0 1657.0 8133 1719
472 100620 550 5987 9990 10656 1331 5490.0 12360.0 8239 8155
473 137771 905 4351 11886 22117 1331 5490.0 1362.0 5029 1419
474 99485 905 7565 5863 1194 1331 5490.0 6118.0 293 5494
475 86584 10728 7565 3072 22322 1331 5490.0 11466.0 293 5494
476 95473 10728 566 11365 1678 1331 5490.0 12444.0 496 7067
477 19487 550 1941 7932 18329 1331 5490.0 11598.0 10167 10122
478 35822 550 4351 12054 18743 1331 5490.0 3604.0 463 7738
479 8875 10728 1004 10522 4738 1331 5490.0 9127.0 6220 7308
480 45253 10728 8841 10095 3395 1331 5490.0 3630.0 993 4939
481 105563 550 6847 4769 1790 1331 5490.0 2074.0 5605 8226
482 44031 550 7565 4149 20375 1331 5490.0 3288.0 293 5494
483 137146 10728 9284 3072 22322 1331 5490.0 11466.0 2691 5807
484 64163 5111 3160 7887 21946 1331 5490.0 4969.0 468 9518
485 22025 905 4351 5863 1194 1331 5490.0 6118.0 8607 3368
486 10329 550 7565 11019 22714 1331 5490.0 1069.0 6921 6205
487 63434 2750 6810 4667 15577 1331 5490.0 10048.0 12107 8083
488 114290 10728 4351 2393 7359 1331 5490.0 2765.0 8607 3368
489 129485 10728 4351 2393 7359 1331 5490.0 2765.0 2443 7969
490 22412 10728 4351 6300 5515 1331 5490.0 916.0 4928 8818
491 53813 10728 3575 12068 15316 1331 5490.0 11605.0 5361 4813
492 133268 10699 2734 11063 13550 1331 5490.0 537.0 6391 7789
493 70729 10728 4448 424 1544 1331 5490.0 8084.0 8695 7688
494 37223 10728 566 6300 5515 1331 5490.0 916.0 496 7067
495 16349 10728 7565 10522 4738 1331 5490.0 9127.0 293 5494
496 8537 10699 4493 2622 15821 1331 5490.0 10456.0 6634 2711
497 109704 10728 4351 8923 17405 1331 5490.0 13047.0 5695 3655
498 55354 10728 1941 11242 9728 1331 5490.0 11470.0 10167 10122
499 67069 10728 7565 1893 1726 1331 5490.0 4674.0 293 5494
500 100888 2750 4277 4217 20407 1331 5490.0 2827.0 1092 10993
501 100249 10728 5987 6300 5515 1331 5490.0 916.0 11454 8747
502 2822 10728 1004 3072 22322 1331 5490.0 11466.0 6220 7308
503 56020 905 7565 9667 19409 1331 5490.0 11331.0 293 5494
504 16263 10728 7565 219 23469 1331 5490.0 11746.0 293 5494
505 12783 10699 4351 11563 8635 1331 5490.0 1997.0 7144 8490
506 99876 550 7565 3893 7064 1331 5490.0 4949.0 293 5494
507 48203 905 1004 9667 19409 1331 5490.0 11331.0 6220 7308
508 49641 10133 3962 10655 9580 1331 5490.0 6815.0 12005 4247
509 129655 10728 6910 6300 5515 1331 5490.0 916.0 11748 4356
510 128043 2750 4277 8041 6798 1331 5490.0 711.0 1092 10993
511 64265 10728 2142 6300 5515 1331 5490.0 916.0 9372 9736

24
exports/observation.sql
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@ -1,24 +0,0 @@
SELECT
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_id' AND CAST(values AS STRING)=CAST(observation.observation_id AS STRING ) ) as observation_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'person_id' AND CAST(values AS STRING)=CAST(observation.person_id AS STRING ) ) as person_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_concept_id' AND CAST(values AS STRING)=CAST(observation.observation_concept_id AS STRING ) ) as observation_concept_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_date' AND CAST(values AS STRING)=CAST(observation.observation_date AS STRING ) ) as observation_date,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_datetime' AND CAST(values AS STRING)=CAST(observation.observation_datetime AS STRING ) ) as observation_datetime,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_type_concept_id' AND CAST(values AS STRING)=CAST(observation.observation_type_concept_id AS STRING ) ) as observation_type_concept_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_as_number' AND CAST(values AS STRING)=CAST(observation.value_as_number AS STRING ) ) as value_as_number,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_as_string' AND CAST(values AS STRING)=CAST(observation.value_as_string AS STRING ) ) as value_as_string,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_as_concept_id' AND CAST(values AS STRING)=CAST(observation.value_as_concept_id AS STRING ) ) as value_as_concept_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'qualifier_concept_id' AND CAST(values AS STRING)=CAST(observation.qualifier_concept_id AS STRING ) ) as qualifier_concept_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'unit_concept_id' AND CAST(values AS STRING)=CAST(observation.unit_concept_id AS STRING ) ) as unit_concept_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'provider_id' AND CAST(values AS STRING)=CAST(observation.provider_id AS STRING ) ) as provider_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'visit_occurrence_id' AND CAST(values AS STRING)=CAST(observation.visit_occurrence_id AS STRING ) ) as visit_occurrence_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_source_value' AND CAST(values AS STRING)=CAST(observation.observation_source_value AS STRING ) ) as observation_source_value,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'observation_source_concept_id' AND CAST(values AS STRING)=CAST(observation.observation_source_concept_id AS STRING ) ) as observation_source_concept_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'unit_source_value' AND CAST(values AS STRING)=CAST(observation.unit_source_value AS STRING ) ) as unit_source_value,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'qualifier_source_value' AND CAST(values AS STRING)=CAST(observation.qualifier_source_value AS STRING ) ) as qualifier_source_value,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_source_concept_id' AND CAST(values AS STRING)=CAST(observation.value_source_concept_id AS STRING ) ) as value_source_concept_id,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'value_source_value' AND CAST(values AS STRING)=CAST(observation.value_source_value AS STRING ) ) as value_source_value,
(SELECT encoded FROM wgan_original_pseudo.map WHERE table='observation' AND field = 'questionnaire_response_id' AND CAST(values AS STRING)=CAST(observation.questionnaire_response_id AS STRING ) ) as questionnaire_response_id
FROM wgan_original.observation
WHERE
REGEXP_CONTAINS(UPPER(observation_source_value),'ICD')

10
exports/sample.csv
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id,first_name,last_name,age,gender
100,steve,nyemba,40,m
101,elon,nyemba,5,m
200,steve,mqueen,80,m
201,james,dean,80,m
300,james,bond,50,m
400,elon,musk,40,m
401,kevin,james,50,m
303,kevin,johnson,40,m
103,Bari,nyemba,5,f
1 id first_name last_name age gender
2 100 steve nyemba 40 m
3 101 elon nyemba 5 m
4 200 steve mqueen 80 m
5 201 james dean 80 m
6 300 james bond 50 m
7 400 elon musk 40 m
8 401 kevin james 50 m
9 303 kevin johnson 40 m
10 103 Bari nyemba 5 f

287
test.py
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@ -1,287 +0,0 @@
import tensorflow as tf
from tensorflow.contrib.layers import l2_regularizer
import numpy as np
import time
import os
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ['CUDA_VISIBLE_DEVICES'] = "4,5"
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('train_dir', 'google_cloud_test/',
"""Directory where to store checkpoint. """)
tf.app.flags.DEFINE_string('save_dir', 'google_cloud_test/',
"""Directory where to save generated data. """)
tf.app.flags.DEFINE_integer('max_steps', 100,
"""Number of batches to run in each epoch.""")
tf.app.flags.DEFINE_integer('max_epochs', 100,
"""Number of epochs to run.""")
tf.app.flags.DEFINE_integer('batchsize', 10,
"""Batchsize.""")
tf.app.flags.DEFINE_integer('z_dim', 10,
"""Dimensionality of random input.""")
tf.app.flags.DEFINE_integer('data_dim', 30,
"""Dimensionality of data.""")
tf.app.flags.DEFINE_integer('demo_dim', 8,
"""Dimensionality of demographics.""")
tf.app.flags.DEFINE_float('reg', 0.0001,
"""L2 regularization.""")
g_structure = [FLAGS.z_dim, FLAGS.z_dim]
d_structure = [FLAGS.data_dim, int(FLAGS.data_dim/2), FLAGS.z_dim]
def _variable_on_cpu(name, shape, initializer=None):
with tf.device('/cpu:0'):
var = tf.get_variable(name, shape, initializer=initializer)
return var
def batchnorm(inputs, name, labels=None, n_labels=None):
mean, var = tf.nn.moments(inputs, [0], keep_dims=True)
shape = mean.shape[1].value
offset_m = _variable_on_cpu(shape=[n_labels,shape], name='offset'+name,
initializer=tf.zeros_initializer)
scale_m = _variable_on_cpu(shape=[n_labels,shape], name='scale'+name,
initializer=tf.ones_initializer)
offset = tf.nn.embedding_lookup(offset_m, labels)
scale = tf.nn.embedding_lookup(scale_m, labels)
result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
return result
def layernorm(inputs, name, labels=None, n_labels=None):
mean, var = tf.nn.moments(inputs, [1], keep_dims=True)
shape = inputs.shape[1].value
offset_m = _variable_on_cpu(shape=[n_labels,shape], name='offset'+name,
initializer=tf.zeros_initializer)
scale_m = _variable_on_cpu(shape=[n_labels,shape], name='scale'+name,
initializer=tf.ones_initializer)
offset = tf.nn.embedding_lookup(offset_m, labels)
scale = tf.nn.embedding_lookup(scale_m, labels)
result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
return result
def input_fn():
features_placeholder = tf.placeholder(shape=[None, FLAGS.data_dim], dtype=tf.float32)
labels_placeholder = tf.placeholder(shape=[None, 6], dtype=tf.float32)
dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
dataset = dataset.repeat(10000)
dataset = dataset.batch(batch_size=FLAGS.batchsize)
dataset = dataset.prefetch(1)
iterator = dataset.make_initializable_iterator()
return iterator, features_placeholder, labels_placeholder
def generator(z, label):
x = z
tmp_dim = FLAGS.z_dim
with tf.variable_scope('G', reuse=tf.AUTO_REUSE, regularizer=l2_regularizer(FLAGS.reg)):
for i, dim in enumerate(g_structure[:-1]):
kernel = _variable_on_cpu('W_' + str(i), shape=[tmp_dim, dim])
h1 = batchnorm(tf.matmul(x, kernel), name='cbn' + str(i), labels=label, n_labels=FLAGS.demo_dim)
h2 = tf.nn.relu(h1)
x = x + h2
tmp_dim = dim
i = len(g_structure) - 1
kernel = _variable_on_cpu('W_' + str(i), shape=[tmp_dim, g_structure[-1]])
h1 = batchnorm(tf.matmul(x, kernel), name='cbn' + str(i),
labels=label, n_labels=FLAGS.demo_dim)
h2 = tf.nn.tanh(h1)
x = x + h2
kernel = _variable_on_cpu('W_' + str(i+1), shape=[FLAGS.z_dim, FLAGS.data_dim])
bias = _variable_on_cpu('b_' + str(i+1), shape=[FLAGS.data_dim])
x = tf.nn.sigmoid(tf.add(tf.matmul(x, kernel), bias))
return x
def discriminator(x, label):
with tf.variable_scope('D', reuse=tf.AUTO_REUSE, regularizer=l2_regularizer(FLAGS.reg)):
for i, dim in enumerate(d_structure[1:]):
kernel = _variable_on_cpu('W_' + str(i), shape=[d_structure[i], dim])
bias = _variable_on_cpu('b_' + str(i), shape=[dim])
x = tf.nn.relu(tf.add(tf.matmul(x, kernel), bias))
x = layernorm(x, name='cln' + str(i), labels=label, n_labels=FLAGS.demo_dim)
i = len(d_structure)
kernel = _variable_on_cpu('W_' + str(i), shape=[d_structure[-1], 1])
bias = _variable_on_cpu('b_' + str(i), shape=[1])
y = tf.add(tf.matmul(x, kernel), bias)
return y
def compute_dloss(real, fake, label):
epsilon = tf.random_uniform(
shape=[FLAGS.batchsize, 1],
minval=0.,
maxval=1.)
x_hat = real + epsilon * (fake - real)
y_hat_fake = discriminator(fake, label)
y_hat_real = discriminator(real, label)
y_hat = discriminator(x_hat, label)
grad = tf.gradients(y_hat, [x_hat])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(grad), 1))
gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
w_distance = -tf.reduce_mean(y_hat_real) + tf.reduce_mean(y_hat_fake)+sum(all_regs)
loss = w_distance + 10 * gradient_penalty
tf.add_to_collection('dlosses', loss)
return w_distance, loss
def compute_gloss(fake, label):
y_hat_fake = discriminator(fake, label)
all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
loss = -tf.reduce_mean(y_hat_fake)+sum(all_regs)
tf.add_to_collection('glosses', loss)
return loss, loss
def tower_loss(scope, stage, real, label):
label = tf.cast(label, tf.int32)
print ([stage,label.shape])
label = label[:, 1] * 4 + tf.squeeze(
tf.matmul(label[:, 2:], tf.constant([[0], [1], [2], [3]], dtype=tf.int32)))
z = tf.random_normal(shape=[FLAGS.batchsize, FLAGS.z_dim])
fake = generator(z, label)
if stage == 'D':
w, loss = compute_dloss(real, fake, label)
losses = tf.get_collection('dlosses', scope)
else:
w, loss = compute_gloss(fake, label)
losses = tf.get_collection('glosses', scope)
total_loss = tf.add_n(losses, name='total_loss')
return total_loss, w
def average_gradients(tower_grads):
average_grads = []
for grad_and_vars in zip(*tower_grads):
grads = []
for g, _ in grad_and_vars:
expanded_g = tf.expand_dims(g, 0)
grads.append(expanded_g)
grad = tf.concat(axis=0, values=grads)
grad = tf.reduce_mean(grad, 0)
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
return average_grads
def graph(stage, opt):
tower_grads = []
per_gpu_w = []
iterator, features_placeholder, labels_placeholder = input_fn()
with tf.variable_scope(tf.get_variable_scope()):
for i in range(1):
with tf.device('/cpu:0'):
with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
(real, label) = iterator.get_next()
loss, w = tower_loss(scope, stage, real, label)
tf.get_variable_scope().reuse_variables()
vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
grads = opt.compute_gradients(loss, vars_)
tower_grads.append(grads)
per_gpu_w.append(w)
grads = average_gradients(tower_grads)
apply_gradient_op = opt.apply_gradients(grads)
mean_w = tf.reduce_mean(per_gpu_w)
train_op = apply_gradient_op
return train_op, mean_w, iterator, features_placeholder, labels_placeholder
def train(data, demo):
with tf.device('/cpu:0'):
opt_d = tf.train.AdamOptimizer(1e-4)
opt_g = tf.train.AdamOptimizer(1e-4)
train_d, w_distance, iterator_d, features_placeholder_d, labels_placeholder_d = graph('D', opt_d)
train_g, _, iterator_g, features_placeholder_g, labels_placeholder_g = graph('G', opt_g)
saver = tf.train.Saver()
init = tf.global_variables_initializer()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
sess.run(init)
sess.run(iterator_d.initializer,
feed_dict={features_placeholder_d: data,
labels_placeholder_d: demo})
sess.run(iterator_g.initializer,
feed_dict={features_placeholder_g: data,
labels_placeholder_g: demo})
for epoch in range(1, FLAGS.max_epochs + 1):
start_time = time.time()
w_sum = 0
for i in range(FLAGS.max_steps):
for _ in range(2):
_, w = sess.run([train_d, w_distance])
w_sum += w
sess.run(train_g)
duration = time.time() - start_time
assert not np.isnan(w_sum), 'Model diverged with loss = NaN'
format_str = 'epoch: %d, w_distance = %f (%.1f)'
print(format_str % (epoch, -w_sum/(FLAGS.max_steps*2), duration))
if epoch % FLAGS.max_epochs == 0:
# checkpoint_path = os.path.join(train_dir, 'multi')
saver.save(sess, FLAGS.train_dir + 'emr_wgan', write_meta_graph=False, global_step=epoch)
# saver.save(sess, train_dir, global_step=epoch)
def generate(demo):
z = tf.random_normal(shape=[FLAGS.batchsize, FLAGS.z_dim])
y = tf.placeholder(shape=[FLAGS.batchsize, 6], dtype=tf.int32)
label = y[:, 1] * 4 + tf.squeeze(tf.matmul(y[:, 2:], tf.constant([[0], [1], [2], [3]], dtype=tf.int32)))
fake = generator(z, label)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, FLAGS.train_dir + 'emr_wgan-' + str(FLAGS.max_epochs))
for m in range(2):
for n in range(2, 6):
idx1 = (demo[:, m] == 1)
idx2 = (demo[:, n] == 1)
idx = [idx1[j] and idx2[j] for j in range(len(idx1))]
num = np.sum(idx)
nbatch = int(np.ceil(num / FLAGS.batchsize))
label_input = np.zeros((nbatch*FLAGS.batchsize, 6))
label_input[:, n] = 1
label_input[:, m] = 1
output = []
for i in range(nbatch):
f = sess.run(fake,feed_dict={y: label_input[i*FLAGS.batchsize:(i+1)*FLAGS.batchsize]})
output.extend(np.round(f))
output = np.array(output)[:num]
np.save(FLAGS.save_dir + 'synthetic_' + str(m) + str(n), output)
def load_data():
data = np.zeros(3000)
idx = np.random.choice(np.arange(3000),size=900)
data[idx] = 1
data = np.reshape(data, (100,30))
idx = np.random.randint(2,6,size=100)
idx2 = np.random.randint(2,size=100)
demo = np.zeros((100,6))
demo[np.arange(100), idx] = 1
demo[np.arange(100), idx2] = 1
return data, demo
if __name__ == '__main__':
data, demo = load_data()
print ([data.shape,demo.shape])
train(data, demo)
# generate(demo)
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