TY - GEN
T1 - Medical concept embedding with multiple ontological representations
AU - Song, Lihong
AU - Cheong, Chin Wang
AU - Yin, Kejing
AU - CHEUNG, Kwok Wai
AU - Fung, Benjamin C.M.
AU - Poon, Jonathan
N1 - Funding Information:
This research is partially supported by General Research Fund 12202117 from the Research Grants Council of Hong Kong.
PY - 2019/8
Y1 - 2019/8
N2 - Learning representations of medical concepts from the Electronic Health Record (EHR) has been shown effective for predictive analytics in healthcare. Incorporation of medical ontologies has also been explored to further enhance the accuracy and to ensure better alignment with the known medical knowledge. Most of the existing works assume that medical concepts under the same ontological category should share similar representations, which however does not always hold. In particular, the categorizations in medical ontologies were established with various factors being considered. Medical concepts even under the same ontological category may not follow similar occurrence patterns in the EHR data, leading to contradicting objectives for the representation learning. In this paper, we propose a deep learning model called MMORE which alleviates this conflicting objective issue by allowing multiple representations to be inferred for each ontological category via an attention mechanism. We apply MMORE to diagnosis prediction and our experimental results show that the representations obtained by MMORE can achieve better predictive accuracy and result in clinically meaningful sub-categorizations of the existing ontological categories.
AB - Learning representations of medical concepts from the Electronic Health Record (EHR) has been shown effective for predictive analytics in healthcare. Incorporation of medical ontologies has also been explored to further enhance the accuracy and to ensure better alignment with the known medical knowledge. Most of the existing works assume that medical concepts under the same ontological category should share similar representations, which however does not always hold. In particular, the categorizations in medical ontologies were established with various factors being considered. Medical concepts even under the same ontological category may not follow similar occurrence patterns in the EHR data, leading to contradicting objectives for the representation learning. In this paper, we propose a deep learning model called MMORE which alleviates this conflicting objective issue by allowing multiple representations to be inferred for each ontological category via an attention mechanism. We apply MMORE to diagnosis prediction and our experimental results show that the representations obtained by MMORE can achieve better predictive accuracy and result in clinically meaningful sub-categorizations of the existing ontological categories.
UR - https://www.ijcai.org/proceedings/2019/
UR - http://www.scopus.com/inward/record.url?scp=85074916146&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2019/641
DO - 10.24963/ijcai.2019/641
M3 - Conference contribution
AN - SCOPUS:85074916146
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 4613
EP - 4619
BT - Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
A2 - Kraus, Sarit
PB - International Joint Conferences on Artificial Intelligence
T2 - 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Y2 - 10 August 2019 through 16 August 2019
ER -