Depression screening using mobile phone usage metadata: a machine learning approach.
Objective: 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...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 27; no. 4; pp. 522 - 531 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142372013&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142372013 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Apr2020 vid: 27 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 142372013 142372013 NLM31977041 142372013 10.1093/jamia/ocz221 NLM31977041 142372013 ppf: 522 ppct: 9 formats: tig: atl: Depression screening using mobile phone usage metadata: a machine learning approach. aug: au: Razavi, Rouzbeh Gharipour, Amin Gharipour, Mojgan affil: Department of Management and Information Systems, Kent State University, Kent, OH, USA sug: subj: Mobile Applications Algorithms Depression Diagnosis Telemedicine Sensitivity and Specificity Severity of Illness Indices Logistic Regression Pharmacokinetics Adult Depression Classification Psychological Tests Adult: 19-44 years ab: Objective: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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