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Article Dans Une Revue Studies in Health Technology and Informatics Année : 2023

Improving Patient Similarity Using Different Modalities of Phenotypes Extracted from Clinical Narratives

Xiaoyi Chen
Carole Faviez
  • Fonction : Auteur
Marc Vincent
  • Fonction : Auteur
Sophie Saunier
  • Fonction : Auteur
Anita Burgun
  • Fonction : Auteur

Résumé

In the context of medical concept extraction, it is critical to determine if clinical signs or symptoms mentioned in the text were present or absent, experienced by the patient or their relatives. Previous studies have focused on the NLP aspect but not on how to leverage this supplemental information for clinical applications. In this paper, we aim to use the patient similarity networks framework to aggregate different phenotyping modalities. NLP techniques were applied to extract phenotypes and predict their modalities from 5470 narrative reports of 148 patients with ciliopathies (a group of rare diseases). Patient similarities were computed using each modality separately for aggregation and clustering. We found that aggregating negated phenotypes improved patient similarity, but further aggregating relatives’ phenotypes worsened the result. We suggest that different modalities of phenotypes can contribute to patient similarity, but they should be aggregated carefully and with appropriate similarity metrics and aggregation models.

Dates et versions

hal-04339484 , version 1 (13-12-2023)

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Xiaoyi Chen, Carole Faviez, Marc Vincent, Sophie Saunier, Nicolas Garcelon, et al.. Improving Patient Similarity Using Different Modalities of Phenotypes Extracted from Clinical Narratives. Studies in Health Technology and Informatics, 2023, Studies in Health Technology and Informatics, ⟨10.3233/SHTI230342⟩. ⟨hal-04339484⟩
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