Predictive analysis of lower limb fractures in the orthopedic complex operative unit using artificial intelligence: the case study of AOU Ruggi

Scala, Arianna; Borrelli, Anna; Improta, Giovanni

doi:10.1038/s41598-022-26667-0

Cited by 24 publications

(9 citation statements)

References 59 publications

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“…The information for this study was extracted from the information system of the Evangelical Hospital "Betania" in Naples, Italy. Using hospital discharge records, through the procedure code reported in the literature [19] and the year of discharge, medical records of interest were filtered. From the extracted variables, after the preprocessing stage, the independent variables used in the study (Age, Gender (M/F), Comorbidities (1/0), Complication during surgery (1/0), Pre-Op LOS) were obtained, in agreement with previous studies [22,23].…”

Section: Methodsmentioning

confidence: 99%

“…Fuzzy logic [8,9], statistical analysis [10][11][12], mathematical models [13][14][15] or management approaches such as Lean Six Sigma [16][17][18] are just a few examples. LOS, which is recognized as a parameter of effectiveness, also lends itself well to the use of these techniques in both orthopedic [19] and other settings [20] with excellent results in Italy. In fact, in this study, we want to use Machine Learning algorithms to predict the LOS of patients undergoing lower limb surgery.…”

Section: Introductionmentioning

confidence: 91%

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The Classification Algorithms Applied to the Inpatient Stay for Lower Limb Surgery

Marino

Bottino²,

Negri³

et al. 2023

Studies in Health Technology and Informatics

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The revolutions of recent years in health care have involved several areas ranging from patient treatment to resource management. Therefore, several strategies have been put in place to increase patient value while trying to reduce spending. Several indicators have arisen to evaluate the performance of healthcare processes. The main one is Length of Stay (LOS). In this study, classification algorithms were used to predict the LOS of patients undergoing lower extremity surgery, an increasingly common condition given the progressive aging of the population. The context is the Evangelical Hospital “Betania” in Naples (Italy) in 2019–2020, which augments a multicenter study conducted by the same research team on several hospitals in southern Italy. All selected algorithms show an Accuracy above 90% but among them, the best is Logistic Regression with a value reaching 94%.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

The Classification Algorithms Applied to the Inpatient Stay for Lower Limb Surgery

Marino

Bottino²,

Negri³

et al. 2023

Studies in Health Technology and Informatics

View full text Add to dashboard Cite

show abstract

“…While ML and simulation, taken separately, have been widely investigated in the literature (e.g. to forecast complications and adverse events to achieve timely and more accurate clinical decisions [14][15][16][17][18] or with the aim of improving the management of healthcare emergency processes [19][20][21] and reduce the length of hospital stay (LOS) [20,[22][23][24][25][26][27][28]), there is only a limited subset of studies [14,29] that exploit a hybrid ML-powered simulation methodology in the healthcare field.…”

Section: Introductionmentioning

confidence: 99%

Combining simulation models and machine learning in healthcare management: strategies and applications

Ponsiglione,

Zaffino,

Ricciardi

et al. 2024

Prog. Biomed. Eng.

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Simulation models and artificial intelligence are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and artificial intelligence could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and artificial intelligence approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and artificial intelligence as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed artificial intelligence strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.

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“…Returning to the topic of our work, the study of LOS, several works have been conducted in Italy. Scala et al [ 49 ], for example, use multiple linear regression and classification algorithms to predict the LOS of patients who accessed the hospital for a lower limb fracture, while Olivato et al [ 50 ] use machine learning algorithms to assess the LOS of hospitalized patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study

et al. 2023

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Caesarean section (CS) rate has seen a significant increase in recent years, especially in industrialized countries. There are, in fact, several causes that justify a CS; however, evidence is emerging that non-obstetric factors may contribute to the decision. In reality, CS is not a risk-free procedure. The intra-operative, post-pregnancy risks and risks for children are just a few examples. From a cost point of view, it must be considered that CS requires longer recovery times, and women often stay hospitalized for several days. This study analyzed data from 12,360 women who underwent CS at the “San Giovanni di Dio e Ruggi D’Aragona” University Hospital between 2010 and 2020 by multiple regression algorithms, including multiple linear regression (MLR), Random Forest, Gradient Boosted Tree, XGBoost, and linear regression, classification algorithms and neural network in order to study the variation of the dependent variable (total LOS) as a function of a group of independent variables. We identify the MLR model as the most suitable because it achieves an R-value of 0.845, but the neural network had the best performance (R = 0.944 for the training set). Among the independent variables, Pre-operative LOS, Cardiovascular disease, Respiratory disorders, Hypertension, Diabetes, Haemorrhage, Multiple births, Obesity, Pre-eclampsia, Complicating previous delivery, Urinary and gynaecological disorders, and Complication during surgery were the variables that significantly influence the LOS. Among the classification algorithms, the best is Random Forest, with an accuracy as high as 77%. The simple regression model allowed us to highlight the comorbidities that most influence the total LOS and to show the parameters on which the hospital management must focus for better resource management and cost reduction.

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Predictive analysis of lower limb fractures in the orthopedic complex operative unit using artificial intelligence: the case study of AOU Ruggi

Cited by 24 publications

References 59 publications

The Classification Algorithms Applied to the Inpatient Stay for Lower Limb Surgery

The Classification Algorithms Applied to the Inpatient Stay for Lower Limb Surgery

Combining simulation models and machine learning in healthcare management: strategies and applications

Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study

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