Using Semantic Web Technologies
to Reproduce
a Pharmacovigilance Case Study
a Pharmacovigilance Case Study
a Pharmacovigil...
computational (open) data study

prov:Entity

prov:Activity

prov:Entity

(open) data

process

results
pharmacovigilance
detect side effects of drugs: disproportional correlation
between a drug and an associated adverse event...
2x2 contingency table
28.862
28.663
28.887

28.837
28.862
28.767
computation is never trivial
computation is never trivial
? to communicate
PROV helps to communicate
PROV helps
3.525
+1

23,865,029
+1,847,073
all drug names were unified into generic names by a
text-mining approach. Spelling errors ...
original

PRR = 2.520

reproduction

PRR = 2.504
PROV helps to communicate
PROV helps to communicate
>> share your provenance graph
>> share your provenance graph
debuggin...
Using Semantic Web Technologies  to Reproduce  a Pharmacovigilance Case Study
Using Semantic Web Technologies  to Reproduce  a Pharmacovigilance Case Study
Using Semantic Web Technologies  to Reproduce  a Pharmacovigilance Case Study
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Using Semantic Web Technologies to Reproduce a Pharmacovigilance Case Study

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We show how the use of PROV-O helps to analyse, discuss and communicate the reconstruction of a scientific workflow of a pharmacovigilance paper.

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  • Using Semantic Web Technologies to Reproduce a Pharmacovigilance Case Study

    1. 1. Using Semantic Web Technologies to Reproduce a Pharmacovigilance Case Study a Pharmacovigilance Case Study a Pharmacovigilance Case Study Michiel Hildebrand, Rinke Hoekstra & Jacco van Ossenbruggen
    2. 2. computational (open) data study prov:Entity prov:Activity prov:Entity (open) data process results
    3. 3. pharmacovigilance detect side effects of drugs: disproportional correlation between a drug and an associated adverse event prov:Entity prov:Activity prov:Entity
    4. 4. 2x2 contingency table
    5. 5. 28.862 28.663 28.887 28.837 28.862 28.767 computation is never trivial computation is never trivial
    6. 6. ? to communicate PROV helps to communicate PROV helps
    7. 7. 3.525 +1 23,865,029 +1,847,073 all drug names were unified into generic names by a text-mining approach. Spelling errors were detected by GNU Aspell and carefully confirmed by working pharmacists. ? debugging requires intermediate datasets debugging requires intermediate datasets 1,664,078 Foods beverages, treatments (e.g. X-ray radiation), and unspecified names (e.g. beta-blockers) were omitted -142 2,231,038 +9 reproduction
    8. 8. original PRR = 2.520 reproduction PRR = 2.504
    9. 9. PROV helps to communicate PROV helps to communicate >> share your provenance graph >> share your provenance graph debugging requires intermediate datasets debugging requires intermediate datasets >> share each prov:Entity >> share each prov:Entity computation is never trivial computation is never trivial (applies also to “preprocessing” & “well known” formula’s) (applies also to “preprocessing” & “well known” formula’s) >> share each computational prov:Activity >> share each computational prov:Activity

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