Your browser doesn't support javascript.
loading
Evaluation of machine learning strategies for imaging confirmed prostate cancer recurrence prediction on electronic health records.
Beinecke, Jacqueline Michelle; Anders, Patrick; Schurrat, Tino; Heider, Dominik; Luster, Markus; Librizzi, Damiano; Hauschild, Anne-Christin.
Affiliation
  • Beinecke JM; Department of Mathematics and Computer Science at the Philipps University Marburg, Germany; Institute for Medical Informatics at the University Medical Center Göttingen, Göttingen, Germany. Electronic address: jacquelinemichelle.beinecke@med.uni-goettingen.de.
  • Anders P; Department of Nuclear Medicine, University Hospital Marburg, Germany.
  • Schurrat T; Department of Nuclear Medicine, University Hospital Marburg, Germany.
  • Heider D; Department of Mathematics and Computer Science at the Philipps University Marburg, Germany.
  • Luster M; Department of Nuclear Medicine, University Hospital Marburg, Germany.
  • Librizzi D; Department of Nuclear Medicine, University Hospital Marburg, Germany.
  • Hauschild AC; Department of Mathematics and Computer Science at the Philipps University Marburg, Germany; Institute for Medical Informatics at the University Medical Center Göttingen, Göttingen, Germany.
Comput Biol Med ; 143: 105263, 2022 Apr.
Article in En | MEDLINE | ID: mdl-35131608
Key words
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Comput Biol Med Year: 2022 Document type: Article
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Comput Biol Med Year: 2022 Document type: Article