Determining the Set of Items to Include in Breast Operative Reports, Using Clustering Algorithms on Retrospective Data Extracted from Clinical DataWarehouse
In: hal-03738958; PUBMED: 35773802; (2022)
Buch
Zugriff:
International audience ; 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.
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Determining the Set of Items to Include in Breast Operative Reports, Using Clustering Algorithms on Retrospective Data Extracted from Clinical DataWarehouse
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Autor/in / Beteiligte Person: | Boukobza, Adrien ; Wack, Maxime ; Neuraz, Antoine ; Geromin, Daniela ; Badoual, Cécile ; Bats, Anne-Sophie ; Burgun, Anita ; Koual, Meriem ; Tsopra, Rosy ; Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)) ; École Pratique des Hautes Études (EPHE) ; Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité) ; Health data- and model- driven Knowledge Acquisition (HeKA) ; Inria de Paris ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)) ; Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE) ; Hôpital Européen Georges Pompidou APHP (HEGP) ; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO) |
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Quelle: | hal-03738958; PUBMED: 35773802; (2022) |
Veröffentlichung: | HAL CCSD ; IOS Press, 2022 |
Medientyp: | Buch |
DOI: | 10.3233/SHTI220656 |
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