Determining the Set of Items to Include in Breast Operative Reports, Using Clustering Algorithms on Retrospective Data Extracted from Clinical DataWarehouse - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Book Sections Year : 2022

Determining the Set of Items to Include in Breast Operative Reports, Using Clustering Algorithms on Retrospective Data Extracted from Clinical DataWarehouse

Abstract

Medical reports are key elements to guarantee the quality, and continuity of care but their quality remains an issue. Standardization and structuration of reports can increase their quality, but are usually based on expert opinions. Here, we hypothesize that a structured model of medical reports could be learnt using machine learning on retrospective medical reports extracted from clinical data warehouses (CDW). To investigate our hypothesis, we extracted breast cancer operative reports from our CDW. Each document was preprocessed and split into sentences. Clustering was performed using TFIDF, Paraphrase or Universal Sentence Encoder along with K-Means, DBSCAN, or Hierarchical clustering. The best couple was TFIDF/K-Means, providing a sentence coverage of 89 % on our dataset; and allowing to identify 7 main categories of items to include in breast cancer operative reports. These results are encouraging for a document preset creation task and should then be validated and implemented in real life.

Dates and versions

hal-03738958 , version 1 (26-07-2022)

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Adrien Boukobza, Maxime Wack, Antoine Neuraz, Daniela Geromin, Cécile Badoual, et al.. Determining the Set of Items to Include in Breast Operative Reports, Using Clustering Algorithms on Retrospective Data Extracted from Clinical DataWarehouse. Advances in Informatics, Management and Technology in Healthcare, 295, IOS Press, pp.45-48, 2022, Studies in Health Technology and Informatics, ⟨10.3233/SHTI220656⟩. ⟨hal-03738958⟩
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