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@INPROCEEDINGS{Hahn:887969,
      author       = {Hahn, Lisa and Eickhoff, Simon and Habel, Ute and
                      Stickeler, Elmar and Goecke, Tamme W. and Stickel, Susanne
                      and Schnakenberg, Patricia and Franz, Matthias and Dukart,
                      Jürgen and Chechko, Natalia},
      title        = {{A}ccurate early identification of postpartum depression
                      using demographic, clinical and digital phenotyping},
      reportid     = {FZJ-2020-04555},
      year         = {2020},
      abstract     = {Introduction: Postpartum depression (PPD) affects up to
                      $13\%$ of women. Although demographic and clinical risk
                      factors have been identified, there are no accurate
                      predictors for PPD to such an extent that at risk mothers
                      can be identified and benefit from early
                      interventions.Methods: We recruited 308 mothers at the
                      University Hospital Aachen. Demographic and clinical
                      measures incl. self-reported mood and stress assessment
                      scales were collected from two to five days to 12 weeks
                      postpartum, on a weekly and daily basis respectively. After
                      12 weeks, participants were defined into three groups
                      according to DSM-5 criteria: healthy controls (HC; N=247),
                      women with PPD (N=28), and women with adjustment disorder
                      (AD; N=33). We used a logistic regression algorithm to
                      evaluate the potential predictive power of baseline
                      demographic, clinical, and digital phenotyping for early
                      identification of PPD. We performed 1000 permutations using
                      three-fold cross validation to obtain accuracy
                      estimates.Results: Most accurate early differentiation
                      between PPD vs. HC and AD vs. HC was achieved by using
                      baseline demographic and clinical risk factors in addition
                      to postnatal depression scores at week 3 (PPD vs. HC:
                      balanced accuracy: 0.78, sensitivity: 0.73, specificity:
                      0.82; AD vs. HC: balanced accuracy: 0.89, sensitivity: 0.85,
                      specificity: 0.93). Accurate differentiation of PPD vs. AD
                      was only possible at week 6 with mood scores being most
                      accurate resulting in a balanced accuracy of 0.76
                      (sensitivity: 0.76, specificity: 0.76).Conclusion: In
                      conclusion, combinations of mood level, postnatal depression
                      scores, and baseline risk factors allowed for an accurate
                      early identification of women at risk for PPD.},
      month         = {Apr},
      date          = {2020-04-30},
      organization  = {2020 Society of Biological Psychiatry
                       Annual Meeting, New York (virtual)
                       (USA), 30 Apr 2020 - 2 May 2020},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/887969},
}