@article{CornbergStoehrNaumannetal.2022,
  author    = {Cornberg, Markus and Stoehr, Albrecht and Naumann, Uwe and Teuber, Gerlinde and Klinker, Hartwig and Lutz, Thomas and M{\"o}ller, Hj{\"o}rdis and Hidde, Dennis and Lohmann, Kristina and Simon, Karl-Georg},
  title     = {Real-world safety, effectiveness, and patient-reported outcomes in patients with chronic hepatitis C virus infection treated with glecaprevir/pibrentasvir: updated data from the German Hepatitis C-Registry (DHC-R)},
  series = {Viruses},
  volume    = {14},
  journal   = {Viruses},
  number    = {7},
  issn      = {1999-4915},
  doi       = {10.3390/v14071541},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-281939},
  year      = {2022},
  abstract  = {Using data from the German Hepatitis C-Registry (Deutsche Hepatitis C-Register, DHC-R), we report the real-world safety and effectiveness of glecaprevir/pibrentasvir (GLE/PIB) treatment and its impact on patient-reported outcomes (PROs) in underserved populations who are not typically included in clinical trials, yet who will be crucial for achieving hepatitis C virus (HCV) elimination. The DHC-R is an ongoing, non-interventional, multicenter, prospective, observational cohort study on patients treated for chronic HCV infection in Germany. The data cutoff was 17 January 2021. The primary effectiveness endpoint was sustained virologic response at post-treatment Week 12 (SVR12). Safety outcomes were assessed in all patients receiving GLE/PIB. PROs were assessed using the SF-36 survey. Of 2354 patients, 1964 had valid SVR12 data (intention-to-treat analysis). Of these, 1905 (97.0\%) achieved SVR12 with rates similar across the comorbidities analyzed, except for people who actively use drugs (PWUD (active)) (86.4\%). Excluding those who discontinued treatment and did not achieve SVR12, or were reinfected with HCV, the rate was 99.3\%, with similar results regardless of comorbidity. PWUD (active) and those with psychiatric disorders had the most meaningful improvements in PROs. Adverse events (AEs) occurred in 631/2354 patients (26.8\%), and serious AEs in 44 patients (1.9\%). GLE/PIB was highly effective and well tolerated in this real-world study of patient groups key to HCV elimination.},
  language  = {en}
}
@article{LodaKrebsDanhofetal.2019,
  author    = {Loda, Sophia and Krebs, Jonathan and Danhof, Sophia and Schreder, Martin and Solimando, Antonio G. and Strifler, Susanne and Rasche, Leo and Kort{\"u}m, Martin and Kerscher, Alexander and Knop, Stefan and Puppe, Frank and Einsele, Hermann and Bittrich, Max},
  title     = {Exploration of artificial intelligence use with ARIES in multiple myeloma research},
  series = {Journal of Clinical Medicine},
  volume    = {8},
  journal   = {Journal of Clinical Medicine},
  number    = {7},
  issn      = {2077-0383},
  doi       = {10.3390/jcm8070999},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197231},
  pages     = {999},
  year      = {2019},
  abstract  = {Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented "A Rule-based Information Extraction System" (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.},
  language  = {en}
}