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dc.contributor.authorBraithwaite, B
dc.contributor.authorPaananen, J
dc.contributor.authorTaipale, H
dc.contributor.authorTanskanen, A
dc.contributor.authorTiihonen, J
dc.contributor.authorHartikainen, S
dc.contributor.authorTolppanen, A-M
dc.date.accessioned2020-06-16T10:03:35Z
dc.date.available2020-06-16T10:03:35Z
dc.date.issued2020
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/8191
dc.description.abstractObjective To study the feasibility of evaluating feature importance with Shapley Values and ensemble methods in the context of pharmacoepidemiology and medication safety. Methods We detected medications associated with Alzheimer's disease (AD) by examining the additive feature attribution with combined approach of Gradient Boosting and Shapley Values in the Medication use and Alzheimer's disease (MEDALZ) study, a nested case-control study of 70,719 verified AD cases in Finland. Our methodological approach is to do binary classification using Gradient boosting (an ensemble of weak classifiers) in a supervised learning manner. Then we apply Shapley Values (from cooperative game theory) to analyze how feature combinations affect the classification result. Medication use with a five to one year time-window before AD diagnosis was ascertained from Prescription register. Results Antipsychotics with low or medium dose, antidepressants with medium to high dose, and cardiovascular medications with medium to high dose were identified as the contributing features for separating cases with AD from controls. Medium to high amount of irregularity in the purchase pattern were an indicating feature for separating AD cases from controls. The similarity of medication purchases between AD cases and controls made the feature evaluation challenging. Conclusions The combined approach of Gradient Boosting and feature evaluation with Shapley Values identified features that were consistent with findings from previous hypothesis-driven studies. Additionally, the results from the additive feature attribution identified new candidates for future studies on AD risk factors. Our approach also shows promise for studies based on observational studies, where feature identification and interactions in populations are of interest; and the applicability of using Shapley Values for evaluating feature relevance in pattern recognition tasks.
dc.language.isoenglanti
dc.publisherElsevier BV
dc.relation.ispartofseriesInternational journal of medical informatics
dc.relation.urihttp://dx.doi.org/10.1016/j.ijmedinf.2020.104142
dc.rightsCC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectpattern recognition
dc.subjectknowledge organization
dc.subjectfeature attribution
dc.subjectcooperative game theory
dc.subjectShapley value
dc.subjectGradient boosting
dc.subjectAlzheimer's disease
dc.subjectnested case-control study
dc.subjectpharmacoepidemiology
dc.titleDetection of medications associated with Alzheimer's disease using ensemble methods and cooperative game theory
dc.description.versionpublished version
dc.contributor.departmentSchool of Pharmacy, Activities
dc.contributor.departmentSchool of Medicine / Biomedicine,School of Medicine / Clinical Medicine
uef.solecris.id71269915en
dc.type.publicationTieteelliset aikakauslehtiartikkelit
dc.rights.accessrights© 2020 The Authors
dc.relation.doi10.1016/j.ijmedinf.2020.104142
dc.description.reviewstatuspeerReviewed
dc.format.pagerange104142
dc.publisher.countryAlankomaat
dc.relation.issn1386-5056
dc.relation.volume141
dc.rights.accesslevelopenAccess
dc.type.okmA1
uef.solecris.openaccessHybridijulkaisukanavassa ilmestynyt avoin julkaisu


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