Scientific article

Real-world patient trajectory prediction from clinical notes using artificial neural networks and UMLS-based extraction of concepts

Published inJournal of healthcare informatics research, vol. 5, no. 4, p. 474-496
Publication date2021-06-05
First online date2021-12

As more data is generated from medical attendances and as Artificial Neural Networks gain momentum in research and industry, computer-aided medical prognosis has become a promising technology. A common approach to perform automated prognoses relies on textual clinical notes extracted from Electronic Health Records (EHRs). Data from EHRs are fed to neural networks that produce a set with the most probable medical problems to which a patient is subject in her/his clinical future, including clinical conditions, mortality, and readmission. Following this research line, we introduce a methodology that takes advantage of the unstructured text found in clinical notes by applying preprocessing, concepts extraction, and fine-tuned neural networks to predict the most probable medical problems to follow in a patient’s clinical trajectory. Different from former works that focus on word embeddings and raw sets of extracted concepts, we generate a refined set of Unified Medical Language System (UMLS) concepts by applying a similarity threshold filter and a list of acceptable concept types. In our prediction experiments, our method demonstrated AUC-ROC performance of 0.91 for diagnosis codes, 0.93 for mortality, and 0.72 for readmission, determining an efficacy that rivals state-of-the-art works. Our findings contribute to the development of automated prognosis systems in hospitals where text is the main source of clinical history.

  • Clinical notes
  • Computer-aided prognosis
  • Patient trajectory prediction
  • QuickUMLS
Affiliation Not a UNIGE publication
Citation (ISO format)
ZAGHIR, Jamil et al. Real-world patient trajectory prediction from clinical notes using artificial neural networks and UMLS-based extraction of concepts. In: Journal of healthcare informatics research, 2021, vol. 5, n° 4, p. 474–496. doi: 10.1007/s41666-021-00100-z
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Article (Published version)
ISSN of the journal2509-498X

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