ohdsi / commonevidencemodel

Common Evidence Model (CEM) is a structure for standardizing evidence about drug-outcome relationship across disparate evidence sources.

CommonEvidenceModel

For more information about the CommonEvidenceModel, post processing steps like generating negative controls, or learning out to get involved, please visit our wiki.

https://github.com/OHDSI/CommonEvidenceModel/wiki

Run Order:

1. Information Prep (to load native data needed for CEM)
2. MeSHTags (preps a MeSH Vocabulary Patch) --Before processing update the config_patient_data.csv --After running, place updated MeSHTags.csv in "evidenceProcessingClean\inst\csv"
3. Run evidenceProcessingClean (this is a long running process, it may take up to a week)
4. Run evidenceProcessingTranslated (currently not running Medline_Cooccurrence for extra long processing time)
5. Run postProcessingEvidenceUnify
6. Run postProcessingNegativeControlsPrep --This process generates a CSV file on an FTP that needs to be loaded by a separate process
7. Run postProcessing

commonevidencemodel's Issues

postProcessingNegativeControlsExport

Create export function that:

1. exports data tables (CEM_EVIDENCE, LU_ tables)
2. Generate a DDL for the tables exported
3. add the two required tables for ATLAS (EVIDENCE_JOB, NC_RESULT)

Output is data tables and DDL.

Indications / Contraindications

Could probably pull in Indication and Contraindications as well to the Indications column

select * from devv5.concept_ancestor
join concept on ancestor_concept_id = concept_id and CONCEPT_CLASS_ID in ('Ind / CI', 'Indication')
where descendant_concept_id = 1112807 -- Aspirin

Update Raw Data Sources

• AEOLUS
• MEDLINE
• SEMMEDDB
• US SPL (continue to use SPLICER for right now)
• UMLS
• EU PL ADR
• OMOP Vocab (in a new vocabulary_staging schema)

Then based on these updates, fix the SOURCE file.

• Source file updated

Get rid of UPPERS()

UPPERS are slowing down the queries.

Add Link out Ability

Via a WebAPI call

Searching Publications Associated Exposure/Conditions

When performing an analysis, we generally match patients based on propensity score however there is a process one must go through to remove covariates that are associated. It would be nice to be informed by the data as well as CEM.LITERATURE which to exclude.

Bridge between feature extraction and CEM.

You could do this on the top 20 out of balance covariates.

Thank you @alondhe for the idea.

[PUBLISH-A-THON] Clarithromycin & Gemifloxacin

These were on the label, why did CEM fail to catch them:

CONCEPT_ID	CONCEPT_NAME
194083	Vaginitis and vulvovaginitis
194081	Acute cystitis
201078	Atrophic vaginitis
4081648	Acute vaginitis

Concept Universe - Conditions

https://github.com/OHDSI/CommonEvidenceModel/blob/master/postProcessingNegativeControls/inst/sql/sql_server/findConceptUniverse.sql#L23

May have done this backwards, review

Fix Condition DC count

https://github.com/OHDSI/CommonEvidenceModel/blob/master/postProcessingNegativeControlsPrep/inst/sql/sql_server/findConceptUniverse.sql#L7

Right now my DC count is a hack because the real DC count wouldn't come back in a timely manner. Need to fix.

SPLICER.SOURCE_CODE_2 not mapped

Need to fix this portion once this issue is fixed:
https://github.com/OHDSI/CommonEvidenceModel/blob/master/postProcessingNegativeControls/inst/sql/sql_server/splicerConditions.sql

Processing without Patient Data for Negative Controls

Right now the processing requires access the patient level data, need to think of a way to do that without it.

Pre-compute Patient Counts

For people who don't have access to patient data, precompute patient counts for them to use.

ID missing from MEDLINE_AVILLACH

Took it out because I was having trouble with Postgres, but need to get it back in.

MEDLINE Publication Heirarchy

Need to create a hierarchy of publication types. For example Clinical trials
trump case reports. However we should do that in an informed way understanding
how they types get used and their relationships between each other.

EVIDENCE.CEM_UNIFIED

• Ensure all items of interest are pulled into this table
• co-occurrence data
• have negative controls processing read off of this
• export data for download (CEM_UNIFIED

Post Data Processing Code

Add code to process raw data sources into the repository

Negative Controls - Export DB Name

Export DB Name in Definition

Redevelop Negative Controls Model

Recreate this process given new data:

Voss EA, Boyce RD, Ryan PB, van der Lei J, Rijnbeek PR, Schuemie MJ. Accuracy of an automated knowledge base for identifying drug adverse reactions. J Biomed Inform. 2017 Feb;66:72-81. doi: 10.1016/j.jbi.2016.12.005. Epub 2016 Dec 16. PubMed PMID: 27993747; PubMed Central PMCID: PMC5316295.

Make process for developing the model repeatable.

FAERS & Chi-Squared

When using FAERS in Negative Control Building I'm folloing the Evan's paper however I wasn't able to calculate X^2 need to go back and fix that.

MedDRA --> Standard Condition Map

There are two main issues when going from MedDRA --> SNOMED:

1. Directionality in Vocabularies
The original mapping used in the OMOP Vocab is SNOMED -> MedDRA. Sometimes a specific SNOMED term gets mapped to a broad MedDRA term, in that direction it is correct. However if you flip the direction, it may not still hold true.

Take the example of MedDRA term Malaria in the OMOP Vocab. It has direct mappings to these SNOMEDs:
432690 Maternal malaria during pregnancy - baby delivered
434424 Malaria in mother complicating pregnancy, childbirth AND/OR puerperium
438067 Malaria
4058268 H/O: malaria

It is correct to say that “Malaria in mother complicating pregnancy, childbirth AND/OR puerperium” in SNOMED is a “Malaria” in MedDRA.
But it is not correct to say “Malaria” (MedDRA) is a “Malaria in mother complicating pregnancy, childbirth AND/OR puerperium” (SNOMED).
What we really want to say is “Malaria” (MedDRA) is a “Malaria” (SNOMED)

This isn’t a problem with the Vocab, just a new use case – we want to be able to go MedDRA --> SNOMED without casting to large of net.

2. Concept Ancestor
I was trying to use CONCEPT_ANCESTOR before in my mapping but it casts just too wide of a net. Using CONCEPT_RELATIONSHIP is best.

After speaking with Patrick we really need to generate an appropriate mapping for MedDRA --> SNOMED for CEM. I’m going to do the following:

1. Pull MedDRAs of interest from FAERS/AEOLUS and get frequency of usage from it.
2. “[01] Only 1 Vocab Map” - Using the 'SNOMED - MedDRA eq' relationship, any time there is only one SNOMED mapped to a MedDRA automatically assume it is what we want.
3. “[02] Exact String Match” - Look for exact match string between MedDRA CONCEPT_NAME to SNOMED CONCEPT_NAME
4. “[03] Select a Parent” - If there are multiple SNOMEDs mapped to a MedDRA look at their ancestors and pick the one they all share and that is closest in relationship distance. Exclude broad concepts like “Disease” or “Clinical Finding”
5. Else no map

Using this process, I was able to map about 40% of the codes (doesn’t mean the map is perfect – especially with the “[03] Select a Parent” option).

The next step would be to put this through USAGI. I would auto accept my mapping using “[01] Only 1 Vocab Map” and “[02] Exact String Match”. I would review “[03] Select a Parent” to make sure it didn’t go to high or to a bad spot. Then use USAGI to map things that didn’t get a map.

I am interested if you have any thoughts about our need for a MedDRA  SNOMED mapping for CEM and how to go about it.

---

For AEOLUS we shouldn't just use a Meddra to SNOMED mapping, we have
learned this isn't very good.

I should look for some type of combination of:

1. Leverage frequency to know which codes are most important
2. Do exact string matching
3. See if the meddra mapping helps at all
4. USAGI

WITH CTE_FAERS_MEDDRA_USAGE AS (
	SELECT SOURCE_CODE_2 AS MEDDRA_CONCEPT_ID, c.CONCEPT_NAME AS MEDDRA_CONCEPT_NAME, c.VOCABULARY_ID AS MEDDRA_VOCABULARY_ID,
		COUNT(DISTINCT UNIQUE_IDENTIFIER) AS FAERS_DISTINCT_REPORT_TYPES, SUM(CASE_COUNT) AS FAERS_SUM_REPORT
	FROM CLEAN.AEOLUS a
		JOIN vocabulary.CONCEPT c
			ON CAST(c.CONCEPT_ID AS VARCHAR(100)) = a.SOURCE_CODE_2
			--AND c.CONCEPT_ID = 36718566
	GROUP BY SOURCE_CODE_2, c.CONCEPT_NAME, c.VOCABULARY_ID
), 
CTE_MEDDRA_TO_SNOMED AS (
	SELECT CAST(c.CONCEPT_ID AS VARCHAR(100)) AS MEDDRA_CONCEPT_ID, c.CONCEPT_NAME AS MEDDRA_CONCEPT_NAME, c.VOCABULARY_ID AS MEDDRA_VOCABUALRY_ID, 
		cr.RELATIONSHIP_ID,
		c2.CONCEPT_ID AS SNOMED_CONCEPT_ID, c2.CONCEPT_NAME AS SNOMED_CONCEPT_NAME, c2.VOCABULARY_ID AS SNOMED_VOCABULARY_ID, c2.DOMAIN_ID AS SNOMED_DOMAIN_ID
	FROM vocabulary.CONCEPT c
		JOIN vocabulary.CONCEPT_RELATIONSHIP cr
			ON cr.CONCEPT_ID_2 = c.CONCEPT_ID
			AND UPPER(cr.RELATIONSHIP_ID) = 'SNOMED - MEDDRA EQ'
		JOIN vocabulary.CONCEPT c2
			ON c2.CONCEPT_ID = cr.CONCEPT_ID_1
			AND UPPER(c2.DOMAIN_ID) = 'CONDITION'
), 
CTE_COUNT_SNOMED AS (
	SELECT MEDDRA_CONCEPT_ID, COUNT(DISTINCT SNOMED_CONCEPT_ID) AS SNOMED_COUNT
	FROM CTE_MEDDRA_TO_SNOMED
	GROUP BY MEDDRA_CONCEPT_ID
), 
CTE_SELECT_A_PARENT AS (
	SELECT *
	FROM (
		SELECT ms.MEDDRA_CONCEPT_ID, ms.MEDDRA_CONCEPT_NAME,
			c1.CONCEPT_ID AS SNOMED_CONCEPT_ID, c1.CONCEPT_NAME, SUM(MAX_LEVELS_OF_SEPARATION) AS SUM_DISTANCES, COUNT(DISTINCT ms.SNOMED_CONCEPT_ID) AS SNOMED_COUNT, 
			ROW_NUMBER() OVER (PARTITION BY ms.MEDDRA_CONCEPT_ID ORDER BY SUM(MAX_LEVELS_OF_SEPARATION)) AS ROW_NUM
		FROM CTE_MEDDRA_TO_SNOMED ms
			JOIN CTE_COUNT_SNOMED cs
				ON cs.MEDDRA_CONCEPT_ID = ms.MEDDRA_CONCEPT_ID
				AND SNOMED_COUNT > 1
			JOIN vocabulary.CONCEPT_ANCESTOR ca
				ON ca.DESCENDANT_CONCEPT_ID = ms.SNOMED_CONCEPT_ID
			JOIN vocabulary.CONCEPT c1
				ON c1.CONCEPT_ID = ca.ANCESTOR_CONCEPT_ID
				AND UPPER(c1.DOMAIN_ID) = 'CONDITION' 
				AND UPPER(c1.STANDARD_CONCEPT) = 'S'
		GROUP BY ms.MEDDRA_CONCEPT_ID, ms.MEDDRA_CONCEPT_NAME, c1.CONCEPT_ID, c1.CONCEPT_NAME, cs.SNOMED_COUNT
		HAVING COUNT(DISTINCT ms.SNOMED_CONCEPT_ID) = cs.SNOMED_COUNT
	) z
	WHERE z.ROW_NUM = 1
),
CTE_EXACT_MATCH AS (
	SELECT CAST(c.CONCEPT_ID As VARCHAR(100)) AS MEDDRA_CONCEPT_ID, c2.CONCEPT_ID AS SNOMED_CONCEPT_ID
	FROM vocabulary.CONCEPT c
		JOIN vocabulary.CONCEPT c2
			ON UPPER(c2.CONCEPT_NAME) = UPPER(c.CONCEPT_NAME)
			AND UPPER(c2.DOMAIN_ID) = 'CONDITION'
			AND UPPER(c2.STANDARD_CONCEPT) = 'S'
	WHERE UPPER(c.VOCABULARY_ID) = 'MEDDRA'
), 
CTE_MAPPING_MEDDRA AS (
	SELECT DISTINCT 
		mu.MEDDRA_CONCEPT_ID, mu.MEDDRA_CONCEPT_NAME, MEDDRA_VOCABULARY_ID,
		ms.RELATIONSHIP_ID, 
		CASE
			WHEN cs.SNOMED_COUNT = 1 THEN '[01] Only 1 Vocab Map'
			WHEN em.SNOMED_CONCEPT_ID IS NOT NULL THEN '[02] Exact String Match'
			WHEN p.SNOMED_CONCEPT_ID IS NOT NULL THEN '[03] Select a Parent'
			ELSE NULL
		END AS SNOMED_MAPPING_TYPE,
		CASE
			WHEN cs.SNOMED_COUNT = 1 THEN ms.SNOMED_CONCEPT_ID
			WHEN em.SNOMED_CONCEPT_ID IS NOT NULL THEN em.SNOMED_CONCEPT_ID
			WHEN p.SNOMED_CONCEPT_ID IS NOT NULL THEN p.SNOMED_CONCEPT_ID
			ELSE NULL
		END AS SNOMED_CONCEPT_ID,
		cs.SNOMED_COUNT, 
		mu.FAERS_DISTINCT_REPORT_TYPES, 
		mu.FAERS_SUM_REPORT
	FROM CTE_FAERS_MEDDRA_USAGE mu
		LEFT OUTER JOIN CTE_COUNT_SNOMED cs
			ON cs.MEDDRA_CONCEPT_ID = mu.MEDDRA_CONCEPT_ID
		LEFT OUTER JOIN CTE_MEDDRA_TO_SNOMED ms
			ON mu.MEDDRA_CONCEPT_ID = ms.MEDDRA_CONCEPT_ID
		LEFT OUTER JOIN CTE_EXACT_MATCH em
			ON em.MEDDRA_CONCEPT_ID = mu.MEDDRA_CONCEPT_ID
		LEFT OUTER JOIN CTE_SELECT_A_PARENT p
			ON p.MEDDRA_CONCEPT_ID = mu.MEDDRA_CONCEPT_ID
			AND p.SNOMED_CONCEPT_ID NOT IN (
				/*BROAD CONCEPTS*/
				432867,4024561,138525,77670,4232079,40484136,40479768,40485074,40485073,40479766,4231941,40479422,4335808,4238518,4149782,312437,4262174,4219740,46273647,46273520,46273646,46270397,46273519,40483794,46273645,46270434,4326993,4169985,4035422,4147003,4324765,72405,4201372,139900,440417,4219481,77073,78227,4048139,4181453,200219,320741,75037,439678,36716009,4311853,78506,73554,80180,4003476,444247,4133004,254761,4275649,4269128,4278454,4262812,4212238,4215628,4029334,4065081,4078796,4242744,4206197,4220969,4147995,4124682,4301516,4096625,192767,135777,4236724,43021941,4014218,77646,4018496,4018970,320425,316999,4133718,4040820,40480937,36716182,4151057,4018956,4048512,4146173,4135647,40481302,140673,4220328,4309070,81902,435839,4138412,4095428,4016543,4138275,133299,257315,4023316,4292524,45766599,439265,4104506,4138282,73673,139906,141104,200881,433595,4040388,4199409,4055474,438608,4055473,4195780,4050234,4050550,4052210,4055479,4110161,4050549,4135097,313902,4056453,4180276,4056454,4152294,4136693,4015344,4014219,255573,316501,4055475,4138276,4050544,434531,4106354,4168183,194797,4004352,313878,4038043,134438,4016831,4222174,4230268,135892,443410,4136692,4055597,4055596,440572,4055595,4055594,437152,255848,77955,4085549,4055477,4050548,139110,4050546,319843,74733,4055476,4145607,4050547,4052209,4052208,4149044,4052207,4005907,4110155,198599,4050678,4050682,317712,437147,4052217,4056573,4112796,4116723,4054058,134186,4052214,195125,75126,4050554,433736,4232869,4055590,4050674,4213373,73085,4050555,435684,4055593,4052218,4116717,195760,4328821,195400,4050542,40482807,37018842,439266,199762,138378,40484571,44783380,4056455,4055589,4113929,4189541,4050552,4241439,4062253,4059242,4087577,4110164,4056568,43020630,314208,441725,4055472,4056460,4056457,4050553,201164,44799731,44800498,42709754,4056458,4056561,4056564,437149,4056560,434224,4050557,4055598,4056567,74808,4056562,4055591,442321,440257,4116728,439270,439267,132703,4050679,319967,438024,442581,313360,4050680,4050681,321726,44796137,44796163,4056571,4055599,4327479,4052213,4056570,437120,4050551,194209,4056456,444188,4147300,4132551,196851,4132550,443878,193907,195124,197153,197151,197140,199959,40491866,4054892,40489827,4190345,4015757,141094,313062,4056440,140214,4152341,315399,4056439,443745,40484607,321389,196849,4134184,4136573,75344,4052977,4170638,4056151,4224480,439190,436277,437427,315116,373648,40486118,4298193,4153359,4105482,4113801,37016155,4056585,4053138,439271,320888,438903,37018671,40480185,36716800,4086198,4055866,4108639,4113815,4331101,4196291,378253,4271776,195118,4137343,4187244,4134171,4138152,4157453,40487489,4181456,140664,74448,37018679,4027540,4220002,4181454,193627,4153730,443860,40493018,444235,4155082,4138150,40480453,4177851,75628,4108642,4106356,4164349,138102,4110709,444237,4178956,4189293,4292542,4292541,4194597,444435,4106355,444206,4213507,443582,4116086,4170730,4108638,444049,4106352,444193,4051134,4324788,443583,4055859,4177053,77355,4115696,4159964,4058364,4108643,4086201,4086200,4043679,40492779,40480978,72997,40482662,443557,317585,4136555,4054054,37395579,435098,4221189,4055870,4135090,4016263,75047,438515,4134182,4323193,4323893,4117771,75949,36717463,258369,4052998,4050380,320746,4157457,4162993,199870,4041510,4188191,4148916,46272749,433015,74399,36716697,4028073,4153380,4159164,444218,314463,4163710,4237912,43021868,321319,4329734,442256,74396,4152928,4030291,43020948,4132130,4146947,40491477,4108742,4209843,135759,4116245,135765,40489940,4266193,4266805,4109481,43022059,4187240,4109468,4056169,4193597,44806517,4154439,4262590,442135,4035610,40480128,4056152,4129730,4054408,443955,4153973,4056158,4051280,4082514,4057588,4035744,4052992,4052991,4052990,4291025,4170635,4086196,81611,4113004,4340264,136354,443388,40492938,4050399,4129215,4054739,36716183,45757442,4164342,4050093,79903,4163900,4128888,436782,439103,4316226,4216555,4180167,4296204,4108467,4123196,4060006,76777,4054547,80493,73290,81939,4190229,74726,4134174,4298595,46269695,193344,4313487,4266654,4031484,4290726,4290964,4095251,4072641,4124681,80494,4189855,73553,4274404,4018369,258895,4019340,443425,4199961,4121460,4119454,4201174,4119941,4053147,4056314,4334729,4162251,4112752,4050400,4051308,141232,4052978,4112758,139750,44782977,40485985,321109,4152843,4328721,4304010,135333,4050224,4050526,4056157,4039212,4225906,433178,4242095,4203449,4236021,4272468,4242704,4028677,437537,4059102,4052985,4219135,4052976,46269882,46269877,46269887,440383,44801154,44799035,44795104,44794371,44794370,4152280,4062233,4062232,442077,4326519,4051426,4051425,4056312,4053144,315282,4050090,43020949,435972,4155297,4179980,4111920,4154440,4056305,4051307,4277276,437663,4268842,4268846,133547,4095427,4138833,4029803,4168057,4081246,37017125,4129078,4341654,134736,314054,40491478,200882,200171,36715557,4054854,440893,4059090,134441,4152478,138996,439966,4221679,4299106,4152477,44782946,4239727,4110707,4171257,444209,4266786,4054736,4299294,440260,437461,43020946,430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082835,4223595,4182613,4181678,4176705,4193523,4171799,4006473,4077761,4312226,4174627,4277505,4278438,4275514,4182460,4219057,4071689,4333743,4080561,4321148,4145214,4022933,4095790,4182462,4300154,4270567,4291437,4196732,4095633,4182622,4021777,4182311,4182612,4103190,4103183,4353966,4021775,4090072,4023995,4115558,4277352,4021774,4182614,4353840,4147185,4169123,4141523,4086181,4217077,4176545,4306520,4105888,4182312,4154773,4269130,4092490,4257441,4182607,4278435,4299315,4094294,4213737,4038492,4300146,4107669,4182769,4182000,4182621,4182320,4182457,4274410,4267233,4265005,4278442,4353828,4182619,4182001,4245113,4140549,4024014,4181973,4280285,4182458,4209224,4312259,4168498,4182166,4179873,4143544,4087297,4267229,4278455,4162739,4274025,4193506,4271400,4024558,4047779,36714559,4160062,444089,43531052,4042833,4287929,4053327,4266188,4051670,443723,37017423,43530975,43530976,45763639,43530974,4042500,4185470,4179872,4180630,45771080,43531053,43531055,443404,4028071,4178626,4288085,4035837,4296108,4297095,4297094,4301709,4296109,4296110,4178804,4300956,4300957,4301711,4179244,4182005,4128624,4087641,4087640,4087642,4179757,4182445,4208104,4159874,45765791,45773405,444363,4294057,4167864,4022204,4182468,4024017,4021908,4263283,4180036,4266769,4224515,4314326,137193,4132108,4246288,4245700,4277661,4287344,4030364,4038502,4182757,4275520,4268987,4271399,4182774,4162087,4008090,4129486,4086316,4278598,4024705,4182770,4091642,4181848,4271775,4266772,4136529,4269559,4216341,4095460,4094893,4025219,4295641,4299322,4299324,4295643,4164485,4299320,4295645,4295646,4300143,4041434,4040740,4041287,4041435,4042143,4042140,43531058,43531061,4126883,43021801,4199275,4158309,4129672,4128494,4115698,4186153,4115244,4154314,4126405,4113691,4213101,4185192,4126569,4101673,4179308,4185196,4199402,4115708,4254076,4128489,4128322,4159361,4021487,4177614,4125617,4113569,4128162,4113570,44788977,4128197,4128997,4124318,4129010,4126889,4091645,4095791,4128830,4116970,4125619,4126396,4129673,4127860,4126412,4128170,4122740,4182037,4154783,44792185,4104311,4297708,4103189,4114162,4201384,4126255,4157351,4103184,4104314,4179307,4200648,4201711,4126416,4102730,4104150,4122738,4126888,4126564,4156026,4183510,4183516,4126406,4113704,4113236,4124656,4184239,4179304,4091050,4126397,4182768,4115700,4021613,4117137,4156630,4126887,4179305,43531059,43531060,4041987,4126750,4126422,4122885,4128171,4113221,4186155,44803043,4113688,4126402,4128984,4126403,4226938,4222872,4122716,4156025,4090721,4185503,4114163,4174252,4116633,43531065,4126881,4306009,4185751,4142309,4200028,4183511,4103661,4182165,4179299,4128846,44811702,4115704,4128199,4117930,4182038,43531057,4200056,4201690,4185193,4177611,4177612,4270691,4270702,4279235,4273455,4273458,4275363,4266924,4265118,4280432,4270700,4298314,4181680,4275360,4150046,4179301,4181978,4266768,4265569,4132559,4276724,4175850,4275362,4103829,4041273,4303401,4278603,4182765,4324769,4266007,4270580,4182002,4089844,4181981,4182282,4278604,4182775,4270440,4024016,4182452,4264133,4168259,4093991,4268982,4278600,4287625,4181847,4294060,4246062,4188147,4132573,4266760,4266653,4278599,4128096,4116634,4173845,4133149,4134446,4271503,443916,4202472,4130197,4295638,4295639,4278605,4181823,4179598,43021226,43530897,42709887,4262752,4223759,4141833,4130200,44805233,4244894,4228656,4257530,4260210,4268534,4143065,4182152,4266192,4288388,4280293,4089402,4172421,4172201,4182615,4182616,436435,4182313,4128492,4128194,4077967,37018424,4266771,4269557,4246059,4244837,4091528,4104961,4300155,4161665,4295651,4079147,4262998,4203098,4113999,4102111,4102113,4116962,40481517,4171231,4245478,4115390,44810437,432455,4299325,4117129,4175540,4300142,4280436,4275651,4203631,4271784,4266773,4266764,4024710,4277360,4149865,4103172,4269561,4028076,4106059,4186392,4024566,4273609,4108611,4103192,4354249,4182315,4262874,4263521,4237172,4125412,4117123,4266186,4021902,4170179,4011630,4103662,4353949,4133152,4228128,4025220,4095779,4182611,4294061,4152342,4224742,4172247,4266774,4181204,4090739,4156036,4158351,4152356,4196709,4090071,4020924,4271398,4268989,4268990,4275516,4268985,4275517,4268986,4090497,4275515,4271397,4269000,4080011,4214848,4090513,4264438,4166498,4175400,4177128,4176983,4179748,4168104,4176979,4166499,4053782,36713290,4178411,4268101,4202478,4269132,4271508,4276589,4156503,4271130,4266780,4278443,44784102,4161988,4103193,4024003,4053317,4182004,4081176,4277363,4181987,4181974,4182122,4273330,4332151,4187213,4295648,4299326,4230027,4230028,4230150,4235556,4181681,4266926,4042015,4182463,4266767,444201,4119969,4041280,4103317,4051204,444205,4088927,4266766,4181816,4260324,4275648,4082925,4172024,4305025,4303802,4168644,4264994,4299337,4164339,4161553,4264993,43530872,4023059,4300144,4207195,4217143,4234703,4266213,4273017,4267227,4221066,4114158,4278440,4278434,4311117,4139999,4145668,4133151,4268992,4021903,4268981,4278437,4271774,4226404,4300139,4182153,4263298,4169942,4181998,4182618,4181684,4182617,4182314,4271639,4169984,4086189,4021905,4174619,4196708,4025203,4041990,4269987,4275361,4269986,443403,4021907,4221555,4271531,4108454,4181677,4021906,4181976,4271773,4181979,4268543,4212577,44807963,4262734,4083787,4270439,4246374,4181995,4141968,4275519,4270442,4312116,4113619,4021783,4278441,4081173,4093335,4182319,4264695,4277508,4245475,4181683,4277509,4278439,4246061,4305682,4182461,40483632,4306659,44811708,44811709,44811710,44811704,44811705,4182316,4275722,4102121,4102117,4080740,4181977,44790589,44790587,44790588,4191716,4114166,4052407,4241182,4241183,4194614,4032012,4267228,4267226,4181814,4025351,4263127,4040732,4024015,4287482,46272922,4246262,4164313,4241430,4295649,4246247,4271570,4182628,443251,4277370,4114160,4266929,4271920,4042020,4300153,4216788,4263380,4163439,4300140,4025352,4267235,4267236,4177703,4172399,40492407,4079709,4182006,4128977,4096860,4059947,4246058,4245578,4274409,437382,4262600,4193463,4161274,4246385,4080870,4155883,4271509,4095634,4269138,4182620,4181827,4230709,4237659,4227253,4125411,4278436,4174922,4266001,4037885,4326403,4264995,4287341,4267522,4353957,4021480,42709875,4021667,4028271
			)
), 
CTE_FINAL_MAP AS (
	SELECT DISTINCT m.MEDDRA_CONCEPT_ID, m.MEDDRA_CONCEPT_NAME, m.MEDDRA_VOCABULARY_ID, 
		m.SNOMED_MAPPING_TYPE,
		c1.CONCEPT_ID AS SNOMED_CONCEPT_ID, c1.CONCEPT_NAME AS SNOMED_CONCEPT_NAME, c1.VOCABULARY_ID AS SNOMED_VOCABULARY_ID, 
		m.FAERS_DISTINCT_REPORT_TYPES, m.FAERS_SUM_REPORT
	FROM CTE_MAPPING_MEDDRA m
		LEFT OUTER JOIN vocabulary.CONCEPT c1
			ON c1.CONCEPT_ID = m.SNOMED_CONCEPT_ID
)
SELECT c.CONCEPT_CODE AS SOURCE_CODE, fm.MEDDRA_CONCEPT_ID AS SOURCE_CONCEPT_ID, c.VOCABULARY_ID AS SOURCE_VOCABULARY_ID,
	c.CONCEPT_NAME AS SOURCE_CODE_DESCRIPTION, 
	SNOMED_CONCEPT_ID AS TARGET_CONCEPT_ID, SNOMED_CONCEPT_NAME AS TARGET_CONCEPT_NAME,
	SNOMED_VOCABULARY_ID AS TARGET_VOCABULARY_ID,
	'1/1/1970' AS VALID_START_DATE,
	'12/31/2099' AS VALID_END_DATE,
	NULL AS INVALID_REASON, 
	SNOMED_MAPPING_TYPE, FAERS_DISTINCT_REPORT_TYPES, FAERS_SUM_REPORT
FROM CTE_FINAL_MAP fm
	JOIN vocabulary.CONCEPT c
		ON CAST(c.CONCEPT_ID AS VARCHAR(100)) = fm.MEDDRA_CONCEPT_ID
ORDER BY FAERS_SUM_REPORT DESC, SOURCE_CODE_DESCRIPTION

Drugs Always before Conditions

When looking for universe, always put drugs before conditions

Also fix the SQL for faster pull.

Concept exists across data sources

Need to bring back the idea of "confidence that a concept exists across data sources"

Drugs to get rid of

967823	Sodium Chloride

19049105	Potassium Chloride

[NEGATIVE CONTROLS] Exclude / Include

OHDSI/Atlas#537

Unmapped Drug Classes

When trying to find Drug negative controls from a condition, there are drug classes in the evidence that I should exclude when trying to pick a drug. Right now I'm finding them by hand.

SELECT SOURCE_CODE_1, SOURCE_CODE_NAME_1, CONCEPT_ID_1, COUNT(DISTINCT UNIQUE_IDENTIFIER)
FROM CEM_TRANSLATED.dbo.MEDLINE_AVILLACH s
WHERE s.CONCEPT_ID_2 IN (
	4055484,378414
)
GROUP BY SOURCE_CODE_1, SOURCE_CODE_NAME_1, CONCEPT_ID_1
ORDER BY 4 DESC, 2

[WEB API PATCH] TOO_BROAD & DESCENDANT/EXACT/PARENT/ANCESTOR Changes

This commit contains the changes:
db50d15

Files to review:

• postProcessingNegativeControlsPrep/inst/sql/sql_server/broadConcepts.sql
• EVIDENCE.NC_LU_BROAD_CONDITIONS now called EVIDENCE.NC_LU_BROAD_CONCEPTS
• postProcessingNegativeControls/inst/sql/sql_server/optimizeEvidence.sql
• postProcessingNegativeControls/inst/sql/sql_server/pullEvidence.sql (this now pulls evidence for Splicer, Publications, and FAERS)
• postProcessingNegativeControls/inst/sql/sql_server/pullEvidencePost.sql (NEW)
• postProcessingNegativeControls/inst/sql/sql_server/pullEvidencePrep.sql (NEW)
• postProcessingNegativeControls/inst/sql/sql_server/summarizeEvidence.sql

[WEB API PATCH] New Columns

I created two new columns:

• OPTIMIZED_OUT
• NOT_PREVALENT

Found in this commit:
956cf70

Files that changed

• postProcessingNegativeControls/inst/sql/sql_server/optimizeEvidence.sql
  (added new information)
• postProcessingNegativeControls/inst/sql/sql_server/exportNegativeControls.sql
  (carried forward to export)

Descendant/Parent/Exact/Ancestor for all Column

Make Code Release

Once raw data sources are up-to-date, the SOURCE file is updated, CEM has been run make a release of the code in Git.

Too Broad Conditions

Review this thread:
http://forums.ohdsi.org/t/broad-drugs/4219/2

Optimize Tick

For concepts that were "optimized" out of negative control selection, add a column for that.

CUI --> CONCEPT_ID for SemMedDB

Right now for the CUI to CONCEPT_ID mapping we use UMLS, however this means we are missing RxNorm extension maps. We could double back and just look for direct string mappings or some other ways to improve the mapping.

[Negative Controls] Raw Data Row Counts

Remove row counts from Atlas processing.

LAERTES virtuoso semantic web server

What is the plan going forward for the OHDSI LAERTES virtuoso semantic web server?

Do we need to build a new CEM workflow to create a compatible semantic (RDF) database extract from CEM and load that data into Virtuoso (replacing the current LAERTES data load), or just shut down the Virtuoso server as part of the LAERTES sunset activities?

Negative Control Processing - Condition to Drugs

Need to update process to go the opposite way, when given conditions produce drug negative controls.

After Table Creation alter access to group

alter table owner to rw_grp

[PUBLISH-A-THON] Drug name: fingolimod

Evidence is not coming back for this drug. Need to review.

SELECT DISTINCT SOURCE_ID
FROM EVIDENCE.EVIDENCE_UNIFIED
WHERE CONCEPT_ID_1 IN (
	21036605,21066061,21095475,21115160,21134685,21144572,21144573,36222380,36222381,36233737,36233738,40226579,40226580,40226581,40226582,40226583,40226584,40226585,40855000,40867373,41010885,41189695,41229826
)

Thank you Laura Hester

omop_vocab_load kettle job todos

Hi, I ran through this kettle job with some stops and starts. These todos are potential enhancements to help others deploy at their location. These could be in the form of notes or job tasks:

1. schema staging_vocabulary needs created under cem if this is the first run

2. The vocab tables for the CDM need to be loaded into staging_vocabulary if this is the first run

Thanks,
-R

Sunset LAERTES

Slow CoOccurrence Query

Hi Erica,

Some suggestions:

For readability, instead of having a SQL snippet as parameter I would create a Boolean parameter @avillach , and use it like:
{@avillach} ? { AND lower(qualifier.value) = 'adverse effects'}

There are some things that may better be precomputed once In a permanent table (with indexes) that you delete at the end.

For example, quite a lot goes in to filtering PMIDs, and for example this lower() function means you’re not using any index so slowing down your query: https://github.com/OHDSI/CommonEvidenceModel/blob/master/evidenceProcessingClean/inst/sql/sql_server/medline_coocurrence.sql#L107
You actually compute a lot of stuff for PMIDs only to filter them out. I would create a single table of PMIDs to include once at the start of the process, and then join everything with that one.

Another opportunity for precomputing is the MeSH hierarchy. Here you join three large tables: https://github.com/OHDSI/CommonEvidenceModel/blob/master/evidenceProcessingClean/inst/sql/sql_server/medline_coocurrence.sql#L107
And again:
https://github.com/OHDSI/CommonEvidenceModel/blob/master/evidenceProcessingClean/inst/sql/sql_server/medline_coocurrence.sql#L55
I’m not clear why you’re joining with the mesh_term table here anyway, since you end up using the ui you already had.

Why not create a single new relationship table combining mesh_ancestor and mesh_relationship. You can restrict one side of the table to uis that appear in the chemical list, so this table should be a lot smaller and therefore quicker to search.

You may also want to break up the query in parts just to find out what is taking so long.

Cheers,
@schuemie

• qualifier boolean parameter
• one list of PMIDs
• MeSH Hierarchy

Negative Controls Clean Up

Make the negative controls process clean up its tables after processing.

new datasets to add

A manually annotated dataset created by the FDA: https://bionlp.nlm.nih.gov/tac2017adversereactions/ -- see the Data Access section.

And, these for liver toxicity: https://www.fda.gov/ScienceResearch/BioinformaticsTools/LiverToxicityKnowledgeBase/ucm226811.htm

Chi-Square Into Summary Table from AEOLUS

Eliminating Case Issues

Since Postgres is case sensitive, maybe at the staging level cast things we query on to LOWER() (or I prefer UPPER() but I know no one else does) so that we know we don't have to deal with it in the CEM stage.

Tagging @leeevans

Thanks to @rkboyce for this idea!

Actually instead of doing this in STAGING I'll do it when pulling into CEM.

UPPERS are slowing down the queries, eliminate them where possible.

Too Broad

Let the Too Broad concepts run for DRUGS too.

967823	Sodium Chloride
19049105	Potassium Chloride

Add more conditions:

CONCEPT_ID	CONCEPT_NAME
444239	Postprocedural state finding
136237	Cardiovascular system problem
4319325	Acquired deformity of trunk
4168222	Intra-abdominal and pelvic swelling, mass and lump
138255	Abnormal cardiovascular function
137967	Muscle, ligament and fascia disorders
195603	Vulval and/or perineal noninflammatory disorders
4164707	Canceled operative procedure

Next Steps with CEM

• In ATLAS when reviewing concept sets, display relevant publications (might help analyst in making better concept set)
• Top 5 conditions associated to the drug via spontaneous reports
• Include Paola Saroufirn in discussions for next steps (will bring a PharmD perspective)

Supporting WebAPI

Here would be a good place to start:
http://www.ohdsi.org/web/wiki/doku.php?id=documentation:software:splicer

Tagging @anthonysena

Negative control outcome generation returning observation domain concepts

Negative control outcome generation for the following concept set returns observation domain concepts

{"items" :[{"concept":{"CONCEPT_ID":1118084,"CONCEPT_NAME":"celecoxib","STANDARD_CONCEPT":"S","INVALID_REASON":"V","CONCEPT_CODE":"140587","DOMAIN_ID":"Drug","VOCABULARY_ID":"RxNorm","CONCEPT_CLASS_ID":"Ingredient","INVALID_REASON_CAPTION":"Valid","STANDARD_CONCEPT_CAPTION":"Standard"},"isExcluded":false,"includeDescendants":false,"includeMapped":false},{"concept":{"CONCEPT_ID":1177480,"CONCEPT_NAME":"Ibuprofen","STANDARD_CONCEPT":"S","INVALID_REASON":"V","CONCEPT_CODE":"5640","DOMAIN_ID":"Drug","VOCABULARY_ID":"RxNorm","CONCEPT_CLASS_ID":"Ingredient","INVALID_REASON_CAPTION":"Valid","STANDARD_CONCEPT_CAPTION":"Standard"},"isExcluded":false,"includeDescendants":false,"includeMapped":false},{"concept":{"CONCEPT_ID":1115008,"CONCEPT_NAME":"Naproxen","STANDARD_CONCEPT":"S","INVALID_REASON":"V","CONCEPT_CODE":"7258","DOMAIN_ID":"Drug","VOCABULARY_ID":"RxNorm","CONCEPT_CLASS_ID":"Ingredient","INVALID_REASON_CAPTION":"Valid","STANDARD_CONCEPT_CAPTION":"Standard"},"isExcluded":false,"includeDescendants":false,"includeMapped":false}]}

Pull Evidence - Implement Evans approach for FAERS Signals

Right now I'm missing the Evans approach for looking for signals in FAERS. Need to add back in.

https://github.com/OHDSI/CommonEvidenceModel/blob/master/postProcessingNegativeControls/inst/sql/sql_server/pullEvidence.sql

Add all the Methods Into OHDSI CEM

• FAERS / AEOLUS
• MEDLINE / COOCCURRENCE
• MEDLINE / AVILLACH
• MEDLINE / PUBMED
• MEDLINE / WINNENBURG
• MEDLINE / SEMMEDDB
• SPL / SPLICER
• SPL / EUPLADR