@article{TschornRieckmannAroltetal.2019,
  author    = {Tschorn, Mira and Rieckmann, Nina and Arolt, Volker and Beer, Katja and Haverkamp, Wilhelm and Martus, Peter and Waltenberger, Johannes and M{\"u}ller-Nordhorn, Jacqueline and Str{\"o}hle, Andreas},
  title     = {Erkennungsg{\"u}te dreier deutschsprachiger Screeninginstrumente f{\"u}r Depression bei hospitalisierten Patienten mit koronarer Herzerkrankung},
  series = {Psychiatrische Praxis},
  volume    = {46},
  journal   = {Psychiatrische Praxis},
  number    = {1},
  publisher = {Thieme},
  address   = {Stuttgart},
  issn      = {0303-4259},
  doi       = {10.1055/s-0042-123434},
  pages     = {41 -- 48},
  year      = {2019},
  abstract  = {Ziel Vergleich der Erkennungsg{\"u}te von drei Depressions-Screeninginstrumenten bei Patienten mit koronarer Herzerkrankung (KHK). Methodik 1019 KHK-Patienten erhielten den Patient Health Questionnaire (PHQ-9 und PHQ-2) und die Hospital Anxiety and Depression Scale (HADS-D) sowie ein klinisches Interview (Composite International Diagnostic Interview) als Referenzstandard. Ergebnisse Bez{\"u}glich der Erkennungsg{\"u}te waren PHQ-9 und HADS-D dem PHQ-2 {\"u}berlegen. Optimale Cut-off-Werte waren 7 (PHQ-9 und HADS-D) und 2 (PHQ-2). Schlussfolgerung PHQ-9 und HADS-D haben eine vergleichbare Diskriminationsf{\"a}higkeit f{\"u}r depressive St{\"o}rungen bei KHK-Patienten.},
  language  = {de}
}
@article{GebserSchaubThieleetal.2011,
  author    = {Gebser, Martin and Schaub, Torsten H. and Thiele, Sven and Veber, Philippe},
  title     = {Detecting inconsistencies in large biological networks with answer set programming},
  series = {Theory and practice of logic programming},
  volume    = {11},
  journal   = {Theory and practice of logic programming},
  number    = {5-6},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068410000554},
  pages     = {323 -- 360},
  year      = {2011},
  abstract  = {We introduce an approach to detecting inconsistencies in large biological networks by using answer set programming. To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on answer set programming to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.},
  language  = {en}
}