@inproceedings{36aa52f254594f89991a38752d4cd589,
title = "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.",
keywords = "Breast cancer, Clustering, Machine learning, NLP",
author = "Adrien Boukobza and Maxime Wack and Antoine Neuraz and Daniela Geromin and C{\'e}cile Badoual and Bats, {Anne Sophie} and Anita Burgun and Meriem Koual and Rosy Tsopra",
note = "Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.",
year = "2022",
month = jan,
day = "1",
doi = "10.3233/SHTI220656",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "45--48",
editor = "John Mantas and Parisis Gallos and Emmanouil Zoulias and Arie Hasman and Househ, {Mowafa S.} and Marianna Diomidous and Joseph Liaskos and Martha Charalampidou",
booktitle = "Advances in Informatics, Management and Technology in Healthcare",
}