Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision

@article{Dong2021RareDI,
  title={Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision},
  author={Hang Dong and V'ictor Su'arez-Paniagua and Huayu Zhang and Minhong Wang and Emma Whitfield and Honghan Wu},
  journal={2021 43rd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  year={2021},
  pages={2294-2298}
}
The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules… 

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