no code implementations • SMM4H (COLING) 2020 • Oguzhan Gencoglu
This paper describes our approach for detecting adverse drug effect mentions on Twitter as part of the Social Media Mining for Health Applications (SMM4H) 2020, Shared Task 2.
1 code implementation • 2 Aug 2020 • Oguzhan Gencoglu
Quantifying the characteristics of public attention is an essential prerequisite for appropriate crisis management during severe events such as pandemics.
2 code implementations • 16 May 2020 • Oguzhan Gencoglu, Mathias Gruber
Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events.
1 code implementation • 9 May 2020 • Oguzhan Gencoglu
Cyberbullying is a widespread adverse phenomenon among online social interactions in today's digital society.
no code implementations • 16 Apr 2019 • Oguzhan Gencoglu, Mark van Gils, Esin Guldogan, Chamin Morikawa, Mehmet Süzen, Mathias Gruber, Jussi Leinonen, Heikki Huttunen
Recent advancements in machine learning research, i. e., deep learning, introduced methods that excel conventional algorithms as well as humans in several complex tasks, ranging from detection of objects in images and speech recognition to playing difficult strategic games.
no code implementations • 14 Mar 2019 • Umair Akhtar Hasan Khan, Carolin Stürenberg, Oguzhan Gencoglu, Kevin Sandeman, Timo Heikkinen, Antti Rannikko, Tuomas Mirtti
We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training.
no code implementations • 25 Dec 2018 • Oguzhan Gencoglu
In this work, we propose deep convolutional autoencoders for learning compact representations of health-related tweets, further to be employed in clustering.
no code implementations • 27 Nov 2018 • Oguzhan Gencoglu, Miikka Ermes
Internet-based approaches for surveillance are appealing logistically as well as economically.