Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression

Konrad F. Waschkies, Joram Soch, Margarita Darna, Anni Richter, Slawek Altenstein, Aline Beyle, Frederic Brosseron, Friederike Buchholz, Michaela Butryn, Laura Dobisch, Michael Ewers, Klaus Fliessbach, Tatjana Gabelin, Wenzel Glanz, Doreen Goerss, Daria Gref, Daniel Janowitz, Ingo Kilimann, Andrea Lohse, Matthias H. MunkBoris Stephan Rauchmann, Ayda Rostamzadeh, Nina Roy, Eike Jakob Spruth, Peter Dechent, Michael T. Heneka, Stefan Hetzer, Alfredo Ramirez, Klaus Scheffler, Katharina Buerger, Christoph Laske, Robert Perneczky, Oliver Peters, Josef Priller, Anja Schneider, Annika Spottke, Stefan Teipel, Emrah Düzel, Frank Jessen, Jens Wiltfang, Björn H. Schott, Jasmin M. Kizilirmak

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Fingerprint

Dive into the research topics of 'Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression'. Together they form a unique fingerprint.

Keyphrases

Psychology

Neuroscience

Biochemistry, Genetics and Molecular Biology