Studying variables affecting the length of stay in patients with lower limb fractures by means of Machine Learning

Colella, Ylenia; Scala, Arianna; Lauri, Chiara; Bruno, Francesco; Cesarelli, Giuseppe; Ferrucci, Giuseppe; Borrelli, Anna

doi:10.1145/3472813.3472821

Cited by 10 publications

(12 citation statements)

References 37 publications

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“…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]. Figure 1 shows the distribution of the variables.…”

Section: Methodssupporting

confidence: 81%

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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: Methodssupporting

confidence: 81%

“…Through the use of predictive models, it is possible to know the LOS a priori and optimize bed management by aiding planning. This study continues a line of research begun in 2021 that involved two hospitals in Southern Italy [22,23].…”

Section: Introductionsupporting

confidence: 64%

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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“…Our experimental evaluation made over a large cohort of patients shows that the RF achieves highest results in accuracy (75.7%) in predicting LOS. So taking into account a larger dataset with more accesses but also with more variables, the ML algorithms returned lower accuracy than the previous work which had an accuracy of 88% 62 . The MLR model with an R-square of 0.80 proves to be a valid decision support for this type of patient.…”

Section: Discussionmentioning

confidence: 65%

“…Then, we discuss the potential of the model obtained as a tool for using hospital management. The present research work is both an extension and an improvement of a previous paper that the same authors presented at a conference 62 . An extension because the dataset considered is much larger both in terms of number of records and variables considered.…”

mentioning

confidence: 77%

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

Scala

Borrelli

Improta

2022

Sci Rep

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The length of stay (LOS) in hospital is one of the main parameters for evaluating the management of a health facility, of its departments in relation to the different specializations. Healthcare costs are in fact closely linked to this parameter as well as the profit margin. In the orthopedic field, the provision of this parameter is increasingly complex and of fundamental importance in order to be able to evaluate the planning of resources, the waiting times for any scheduled interventions and the management of the department and related surgical interventions. The purpose of this work is to predict and evaluate the LOS value using machine learning methods and applying multiple linear regression, starting from clinical data of patients hospitalized with lower limb fractures. The data were collected at the "San Giovanni di Dio e Ruggi d’Aragona" hospital in Salerno (Italy).

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“…The work showed that infection control programs with dedicated hospital epidemiologists and surveillance programs reduced nosocomial infections by 32% compared to facilities without infection control programs [ 8 ]. To design an effective prevention program, it is necessary to consider the impact that SSIs have on the length of hospital stay [ 9 , 10 , 11 , 12 ], which is a performance indicator of the quality of health processes [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. In addition, identifying risk factors associated with SSIs can help reduce the incidence of SSIs [ 11 , 20 ] and add value to HTA studies, which are widely used to support health decision-making [ 21 , 22 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Scala

Loperto

Triassi

et al. 2022

IJERPH

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Background: Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to increase the hospitalization duration, cost, and risk of death, in fact, the SSIs are a leading cause of morbidity and mortality in modern health care. Methods: A study based on statistical test and logistic regression for unveiling the association between SSIs and different risk factors was carried out. Successively, a predictive analysis of SSIs on the basis of risk factors was performed. Results: The obtained data demonstrated that the level of surgery contamination impacts significantly on the infection rate. In addition, data also reveals that the length of postoperative hospital stay increases the rate of surgical infections. Finally, the postoperative length of stay, surgery department and the antibiotic prophylaxis with 2 or more antibiotics are a significant predictor for the development of infection. Conclusions: The data report that the type of surgery department and antibiotic prophylaxis there are a statistically significant predictor of SSIs. Moreover, KNN model better handle the imbalanced dataset (48 infected and 3983 healthy), observing highest accuracy value.

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Studying variables affecting the length of stay in patients with lower limb fractures by means of Machine Learning

Cited by 10 publications

References 37 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

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

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

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