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dc.contributor.authorRazavi, Rouzbeh
dc.contributor.authorGharipour, Amin
dc.contributor.authorGharipour, Mojgan
dc.date.accessioned2020-05-25T04:26:25Z
dc.date.available2020-05-25T04:26:25Z
dc.date.issued2020
dc.identifier.issn1067-5027
dc.identifier.doi10.1093/jamia/ocz221
dc.identifier.urihttp://hdl.handle.net/10072/394117
dc.description.abstractOBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, this study examines the possibility of screening for depressive symptoms continuously based on patients' mobile usage patterns. MATERIALS AND METHODS: 412 research participants reported a range of their mobile usage statistics. Beck Depression Inventory-2nd ed (BDI-II) was used to measure the severity of depression among participants. A wide array of machine learning classification algorithms was trained to detect participants with depression symptoms (ie, BDI-II score ≥ 14). The relative importance of individual variables was additionally quantified. RESULTS: Participants with depression were found to have fewer saved contacts on their devices, spend more time on their mobile devices to make and receive fewer and shorter calls, and send more text messages than participants without depression. The best model was a random forest classifier, which had an out-of-sample balanced accuracy of 0.768. The balanced accuracy increased to 0.811 when participants' age and gender were included. DISCUSSIONS/CONCLUSION: The significant predictive power of mobile usage attributes implies that, by collecting mobile usage statistics, mental health mobile applications can continuously screen for depressive symptoms for initial diagnosis or for monitoring the progress of ongoing treatments. Moreover, the input variables used in this study were aggregated mobile usage metadata attributes, which has low privacy sensitivity making it more likely for patients to grant required application permissions.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherOxford University Press (OUP)
dc.relation.ispartofpagefrom522
dc.relation.ispartofpageto530
dc.relation.ispartofissue4
dc.relation.ispartofjournalJournal of the American Medical Informatics Association
dc.relation.ispartofvolume27
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchMedical and Health Sciences
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.subject.fieldofresearchcode11
dc.subject.keywordsdepression
dc.subject.keywordsmachine learning
dc.subject.keywordsmobile health
dc.subject.keywordsmobile usage
dc.titleDepression screening using mobile phone usage metadata: a machine learning approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationRazavi, R; Gharipour, A; Gharipour, M, Depression screening using mobile phone usage metadata: a machine learning approach, Journal of the American Medical Informatics Association, 2020, 27 (4), pp. 522-530
dcterms.dateAccepted2019-12-15
dc.date.updated2020-05-25T04:25:05Z
gro.hasfulltextNo Full Text
gro.griffith.authorGharipour, Amin


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