Evaluation of sarcopenia and sarcopenic obesity in patients affected by oral squamous cell carcinoma: A retrospective single-center study

Bonavolontà, Paola; Improta, Giovanni; Orabona, Giovanni Dell’Aversana; Goglia, Federica; Abbate, Vincenzo; Sorrentino, Alfonso; Piloni, Sara; Salzano, Giovanni; Iaconetta, Giorgio; Califano, Luigi

doi:10.1016/j.jcms.2023.01.014

Cited by 3 publications

(1 citation statement)

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“…Researchers and practitioners are focused on developing models and methodologies for estimating LOS [5,6], and models have been proposed to decrease LOS for other surgeries [7][8][9][10][11][12]. The implementations presented actually adopt all those data analysis strategies already successfully used in the health sector for the analysis of biomedical data [13][14][15] but also from a management point of view for the analysis of hospital resources [16][17][18] and pathways [19,20]. The main aim of the proposed work is to design and develop the most suitable ML model able to estimate the LOS for patients undergoing mastectomy in order to optimize the healthcare management process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hospital Length Stay for Patients Undergoing Mastectomy

Donofrío

D’Amore

Onofaro

et al. 2023

Studies in Health Technology and Informatics

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The prolonged length of stay is an important aspect to be considered for the healthcare management since this affect both the health-related expenditure of the hospital and the quality of the offered service. In the light of these consideration is important for hospitals to be able to predict the LOS of patients and to work on the principal aspect affecting it in order to reduce LOS as much as possible. In this work we focus on patients undergoing mastectomy. The data were collected form 989 patients who underwent mastectomy surgery in the Surgery Department of the AORN “A. Cardarelli” of Naples. Different models have been tested and characterized and the one with the best performance was identified.

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