ArticleBMC medical informatics and decision making2022
Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Predicting Hospital Length of Stay in Orthopedic Trauma Patients Using Fracture-Specific Machine Learning Models: A Multicenter Retrospective Prediction-Modeling Study.Health science reports · 2026Article
- Exploring the Role of Rhizobacteria inMicroorganisms · 2025Article
- Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery.International journal of computer assisted radiology and surgery · 2024Article
- Determinants of public institutional births in India: An analysis using the National Family Health Survey (NFHS-5) factsheet data.Journal of family medicine and primary care · 2024Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS.Bioengineering (Basel, Switzerland) · 2022Article
- Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study.International journal of environmental research and public health · 2022Article
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Authors and funding
7 authors.
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Abstract
backgroundThe rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value.
methodsIn this study, a predictive model was built for the total LOS of patients undergoing laparoscopic appendectomy, one of the most common emergency procedures. Demographic and clinical data of the 357 patients admitted at "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno (Italy) had used as independent variable of the multiple linear regression model.
resultsThe obtained model had an R
conclusionThis work designed an effective and automated strategy for improving the prediction of LOS, that can be useful for enhancing the preoperative pathways. In this way it is possible to characterize the demand and to be able to estimate a priori the occupation of the beds and other related hospital resources.
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