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Augmenting Embedding with Domain Knowledge for Oral Disease Diagnosis Prediction

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Book cover Smart Computing and Communication (SmartCom 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11344))

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Abstract

In this paper, we propose to add domain knowledge from the most comprehensive biomedical ontology SNOMED CT to facilitate the embedding of EMR symptoms and diagnoses for oral disease prediction. We first learn embeddings of SNOMED CT concepts by applying the TransE algorithm prevalent for representation learning of knowledge base. Secondly, the mapping from symptoms/diagnoses to biomedical concepts and the corresponding semantic relations defined in SNOMED CT are modeled mathematically. We design a neural network to train embeddings of EMR symptoms and diagnoses and ontological concepts in a coherent way, for the latter the TransE-learned vectors being used as initial values. The evaluation on real-world EMR datasets from Peking University School and Hospital Stomatology demonstrates the prediction performance improvement over embeddings solely based on EMRs. This study contributes as a first attempt to learn distributed representations of EMR symptoms and diagnoses under the constraint of embeddings of biomedical concepts from comprehensive clinical ontology. Incorporating domain knowledge can augment embedding as it reveals intrinsic correlation among symptoms and diagnoses that cannot be discovered by EMR data alone.

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Notes

  1. 1.

    https://www.nlm.nih.gov/healthit/snomedct/international.html.

  2. 2.

    https://github.com/thunlp/OpenKE.

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Acknowledgements

This work has been supported by the National Key Research and Development Program of China under grant 2016YFB1000902, Projects of Beijing Municipal Science & Technology Commission, and the Natural Science Foundation of China grant 61621003.

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Authors and Affiliations

  1. MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China

    Guangkai Li & Songmao Zhang

  2. Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, 100081, China

    Jie Liang & Chuanbin Guo

  3. Information Center, Peking University School and Hospital of Stomatology, Beijing, China

    Zhanqiang Cao

Authors
  1. Guangkai Li
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  2. Songmao Zhang
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  3. Jie Liang
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  4. Zhanqiang Cao
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  5. Chuanbin Guo
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Corresponding author

Correspondence to Songmao Zhang .

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Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

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Li, G., Zhang, S., Liang, J., Cao, Z., Guo, C. (2018). Augmenting Embedding with Domain Knowledge for Oral Disease Diagnosis Prediction. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2018. Lecture Notes in Computer Science(), vol 11344. Springer, Cham. https://doi.org/10.1007/978-3-030-05755-8_24

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  • DOI: https://doi.org/10.1007/978-3-030-05755-8_24

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