Contextualized Graph Embeddings for Adverse Drug Event Detection

Ya Gao; Shaoxiong Ji; Tongxuan Zhang; Prayag Tiwari; Pekka Marttinen

Contextualized Graph Embeddings for Adverse Drug Event Detection

Ya Gao, Shaoxiong Ji, Tongxuan Zhang, Prayag Tiwari, Pekka Marttinen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Original language	English
Title of host publication	Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
Number of pages	16
Publication status	Accepted/In press - 2022
MoE publication type	A4 Article in a conference publication
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Grenoble, France Duration: 19 Sep 2022 → 23 Sep 2022 https://2022.ecmlpkdd.org/

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated title	ECML-PKDD
Country/Territory	France
City	Grenoble
Period	19/09/2022 → 23/09/2022
Internet address	https://2022.ecmlpkdd.org/

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Contextualized Graph Embeddings for Adverse Drug Event DetectionSubmitted manuscript, 682 KB

1 Finished
1 Active

INTERVENE: International consortium for integrative genomics prediction
Kaski, S.
01/01/2021 → 31/12/2025
Project: EU: Framework programmes funding
Intelligent Crop Production: Data-integrative, Multi-task Learning Meets Crop Simulator
Mamitsuka, H., Nariman Zadeh, H., Strahl, J., Guvenc, B., Ji, S., Rissanen, S., Pöllänen, A., Honkamaa, J., Hiremath, S. & Ojala, F.
01/01/2018 → 31/12/2022
Project: Academy of Finland: Other research funding

Science-IT
Mikko Hakala (Manager)
School of Science
Facility/equipment: Facility

Cite this

@inproceedings{9f73c6fe770f4acb85cb757345fef310,

title = "Contextualized Graph Embeddings for Adverse Drug Event Detection",

author = "Ya Gao and Shaoxiong Ji and Tongxuan Zhang and Prayag Tiwari and Pekka Marttinen",

note = "| openaire: EC/H2020/101016775/EU//INTERVENE ; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD ; Conference date: 19-09-2022 Through 23-09-2022",

year = "2022",

language = "English",

booktitle = "Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)",

url = "https://2022.ecmlpkdd.org/",

}

Gao, Y, Ji, S, Zhang, T, Tiwari, P & Marttinen, P 2022, Contextualized Graph Embeddings for Adverse Drug Event Detection. in Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Grenoble, France, 19/09/2022.

Contextualized Graph Embeddings for Adverse Drug Event Detection. / Gao, Ya; Ji, Shaoxiong; Zhang, Tongxuan et al.

Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). 2022.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Contextualized Graph Embeddings for Adverse Drug Event Detection

AU - Gao, Ya

AU - Ji, Shaoxiong

AU - Zhang, Tongxuan

AU - Tiwari, Prayag

AU - Marttinen, Pekka

N1 - | openaire: EC/H2020/101016775/EU//INTERVENE

PY - 2022

Y1 - 2022

M3 - Conference contribution

BT - Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Y2 - 19 September 2022 through 23 September 2022

ER -

Contextualized Graph Embeddings for Adverse Drug Event Detection

Conference

Access to Document

Projects

INTERVENE: International consortium for integrative genomics prediction

Intelligent Crop Production: Data-integrative, Multi-task Learning Meets Crop Simulator

Equipment

Science-IT

Cite this