Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision

Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang, Minhong Wang, Emma Whitfield, Honghan Wu

Research output: Contribution to conferencePaper

Abstract / Description of output

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 and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, and (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a case study, we show that the Text-to-UMLS process can be greatly improved with weak supervision, without any annotated data from domain experts. Our analysis shows that the overall pipeline processing discharge summaries can surface rare disease cases, which are mostly uncaptured in manual ICD codes of the hospital admissions.
Original languageEnglish
Pages2298-2302
Number of pages5
DOIs
Publication statusPublished - 9 Dec 2021
Event2021 43rd Annual International Conference of the IEEE Engineering in Medicine - Mexico
Duration: 1 Nov 20215 Nov 2021

Conference

Conference2021 43rd Annual International Conference of the IEEE Engineering in Medicine
Period1/11/215/11/21

Keywords / Materials (for Non-textual outputs)

  • cs.CL
  • 68T50 (Primary), 68T30 (Secondary)
  • I.2.7; J.3

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