@misc{KonigorskiWernickeSlosareketal.2021,
  author    = {Konigorski, Stefan and Wernicke, Sarah and Slosarek, Tamara and Zenner, Alexander Maximilian and Strelow, Nils and Ruether, Darius Ferenc and Henschel, Florian and Manaswini, Manisha and Pottb{\"a}cker, Fabian and Edelman, Jonathan Antonio and Owoyele, Babajide and Danieletto, Matteo and Golden, Eddye and Zweig, Micol and Nadkarni, Girish N. and Bottinger, Erwin},
  title     = {StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {12},
  doi       = {10.25932/publishup-58037},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-580370},
  pages     = {12},
  year      = {2021},
  abstract  = {N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.},
  language  = {en}
}
@article{KonigorskiWernickeSlosareketal.2021,
  author    = {Konigorski, Stefan and Wernicke, Sarah and Slosarek, Tamara and Zenner, Alexander Maximilian and Strelow, Nils and Ruether, Darius Ferenc Ruether and Henschel, Florian and Manaswini, Manisha and Pottb{\"a}cker, Fabian and Edelman, Jonathan Antonio and Owoyele, Babajide and Danieletto, Matteo and Golden, Eddye and Zweig, Micol and Nadkarni, Girish N. and Bottinger, Erwin},
  title     = {StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials},
  series = {Journal of Medical Internet Research},
  volume    = {24},
  journal   = {Journal of Medical Internet Research},
  edition   = {7},
  publisher = {JMIR Publications},
  address   = {Richmond, Virginia, USA},
  issn      = {1438-8871},
  doi       = {10.2196/35884},
  pages     = {12},
  year      = {2021},
  abstract  = {N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.},
  language  = {en}
}
@article{DeFreitasJohnsonGoldenetal.2021,
  author    = {De Freitas, Jessica K. and Johnson, Kipp W. and Golden, Eddye and Nadkarni, Girish N. and Dudley, Joel T. and B{\"o}ttinger, Erwin and Glicksberg, Benjamin S. and Miotto, Riccardo},
  title     = {Phe2vec},
  series = {Patterns},
  volume    = {2},
  journal   = {Patterns},
  number    = {9},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2666-3899},
  doi       = {10.1016/j.patter.2021.100337},
  pages     = {9},
  year      = {2021},
  abstract  = {Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.},
  language  = {en}
}