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@INBOOK{Househ:280260,
      author       = {Maharlou, Hamidreza and Krause, Elischa and Müntefering,
                      Fabian and Voges, Jan and Weihs, Antoine and Lucht, Michael
                      and Grabe, Hans J. and Pollmann, Iris and Mueller,
                      Franz-Josef and Weber, Heike and Oeltze-Jafra, Steffen},
      editor       = {Househ, Mowafa S. and Tariq, Zain Ul Abideen and
                      Al-Zubaidi, Mahmood and Shah, Uzair and Huesing, Elaine},
      title        = {{T}he {P}4{D} {D}ashboard: {A} {P}latform for {M}onitoring
                      {C}linical {S}tudies},
      address      = {Amsterdam},
      publisher    = {IOS Press},
      reportid     = {DZNE-2025-00938},
      series       = {Studies in Health Technology and Informatics},
      pages        = {495 - 499},
      year         = {2025},
      comment      = {MEDINFO 2025 — Healthcare Smart × Medicine Deep /
                      Househ, Mowafa S. (Editor) ; : IOS Press, , ; ISSN:
                      09269630=18798365 ; ISBN: 9781643686080 ;
                      doi:10.3233/SHTI250889},
      booktitle     = {MEDINFO 2025 — Healthcare Smart ×
                       Medicine Deep / Househ, Mowafa S.
                       (Editor) ; : IOS Press, , ; ISSN:
                       09269630=18798365 ; ISBN: 9781643686080
                       ; doi:10.3233/SHTI250889},
      abstract     = {The P4D (Personalized, Predictive, Precise $\&$ Preventive
                      Medicine for Major Depression) dashboard
                      (https://p4dashboard.vercel.app) is a web-based platform for
                      monitoring and generating data-driven insights within a
                      multi-site clinical depression study. Part of the broader
                      P4D initiative, which aims to advance personalized medicine
                      for depression through deep phenotyping, genotyping, and
                      machine learning, the dashboard addresses the challenge of
                      integrating heterogeneous data sources. Dynamic
                      visualizations and interactive filtering methods enable
                      users to define and explore sub-cohorts, facilitating the
                      understanding of complex patterns and tailoring data views
                      to their specific needs. The dashboard also summarizes key
                      metrics, allowing real-time monitoring of the data
                      collection and the generation of actionable reports. The P4D
                      dashboard has successfully identified data irregularities,
                      such as missing followup assessments due to early patient
                      discharge and site-specific recruitment disparities,
                      enabling timely interventions to enhance data quality. With
                      its adaptable and scalable framework, the dashboard may be
                      applied to other clinical cohort studies in the future.},
      keywords     = {Humans / Precision Medicine: methods / User-Computer
                      Interface / Depressive Disorder, Major: diagnosis /
                      Depressive Disorder, Major: therapy / Machine Learning /
                      Clinical monitoring (Other) / data integration (Other) /
                      depression (Other) / dynamic visualization (Other) /
                      interactive dashboard (Other)},
      cin          = {AG Hoffmann / AG Grabe},
      ddc          = {300},
      cid          = {I:(DE-2719)1510600 / I:(DE-2719)5000001},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)7},
      doi          = {10.3233/SHTI250889},
      url          = {https://pub.dzne.de/record/280260},
}