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The effectiveness of CAML is evaluated by comparing its performance with that of the Auto-Sklearn model on five different datasets from the Medical Information Mart for Intensive Care (MIMIC III) database of reports. These datasets vary in size, label set, and related diseases. The results demonstrate that CAML outperforms Auto-Sklearn in terms of Micro F1-score and Weighted F1-score, with an overall improvement ratio of 35.15% and 40.56%, respectively. The CAML approach offers the potential to improve healthcare quality by facilitating more accurate diagnoses and treatment decisions, ultimately enhancing patient outcomes.<\/jats:p>","DOI":"10.1007\/s13042-024-02349-3","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T18:02:30Z","timestamp":1725386550000},"page":"1507-1529","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Clustered Automated Machine Learning (CAML) model for clinical coding multi-label classification"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4090-2597","authenticated-orcid":false,"given":"Akram","family":"Mustafa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7975-3985","authenticated-orcid":false,"given":"Mostafa","family":"Rahimi Azghadi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,3]]},"reference":[{"key":"2349_CR1","doi-asserted-by":"crossref","unstructured":"Huang C, Wang J, Wang S, Zhang Y (2023) A review of deep learning in dentistry. 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