A comparison of different Machine Learning algorithms for predicting the length of hospital stay for pediatric patients

Colella, Ylenia; Lauri, Chiara; Ponsiglione, Alfonso Maria; Giglio, Cristiana; Lombardi, Andrea; Borrelli, Anna; Amato, Francesco; Romano, Maria

doi:10.1145/3502060.3503648

Cited by 6 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ability to understand the reasons of prolonged LOS in ED could reasonably support the detection of "bottlenecks" in their organisation. Indeed, the LOS is currently an important indicator for health facilities; other studies have already been focused on the use of such indicators as variables to be predicted for improving the efficiency of the hospital management (13)(14)(15)(16)(17)(18)(19).…”

Section: Discussionmentioning

confidence: 99%

“…As a result, several methodologies have been proposed in the literature to study the factors that influence LOS in healthcare processes. Among them, regression models and artificial intelligence techniques have been widely applied with satisfactory performance to predict the LOS ( 13 – 19 ) and to address healthcare-related problems, such as elaboration and analysis of biomedical data and signals ( 20 – 26 ), development of clinical decision-making support systems ( 27 , 28 ), and quality assessment of medicine services. In fact, LOS has been already employed as a target output in healthcare, and other studies have recently aimed at predicting it in different fields ( 29 , 30 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study

Ricciardi,

Marino,

Trunfio

et al. 2024

Front. Digit. Health

View full text Add to dashboard Cite

BackgroundRecently, crowding in emergency departments (EDs) has become a recognised critical factor impacting global public healthcare, resulting from both the rising supply/demand mismatch in medical services and the paucity of hospital beds available in inpatients units and EDs. The length of stay in the ED (ED-LOS) has been found to be a significant indicator of ED bottlenecks. The time a patient spends in the ED is quantified by measuring the ED-LOS, which can be influenced by inefficient care processes and results in increased mortality and health expenditure. Therefore, it is critical to understand the major factors influencing the ED-LOS through forecasting tools enabling early improvements.MethodsThe purpose of this work is to use a limited set of features impacting ED-LOS, both related to patient characteristics and to ED workflow, to predict it. Different factors were chosen (age, gender, triage level, time of admission, arrival mode) and analysed. Then, machine learning (ML) algorithms were employed to foresee ED-LOS. ML procedures were implemented taking into consideration a dataset of patients obtained from the ED database of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital (Salerno, Italy) from the period 2014–2019.ResultsFor the years considered, 496,172 admissions were evaluated and 143,641 of them (28.9%) revealed a prolonged ED-LOS. Considering the complete data (48.1% female vs. 51.9% male), 51.7% patients with prolonged ED-LOS were male and 47.3% were female. Regarding the age groups, the patients that were most affected by prolonged ED-LOS were over 64 years. The evaluation metrics of Random Forest algorithm proved to be the best; indeed, it achieved the highest accuracy (74.8%), precision (72.8%), and recall (74.8%) in predicting ED-LOS.ConclusionsDifferent variables, referring to patients’ personal and clinical attributes and to the ED process, have a direct impact on the value of ED-LOS. The suggested prediction model has encouraging results; thus, it may be applied to anticipate and manage ED-LOS, preventing crowding and optimising effectiveness and efficiency of the ED.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study

Ricciardi,

Marino,

Trunfio

et al. 2024

Front. Digit. Health

View full text Add to dashboard Cite

show abstract

“…A machine learning classifier then predicted the LoS class for each inpatient. Similarly, another study by Colella et al evaluated the accuracy of multiple machine learning models for LoS prediction for pediatric patients [13]. The researchers used random forest, naïve Bayes, support vector machines, and logistic regression to classify the patients into their LoS categories.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables—Bayesian Models vs. Machine Learning Models

Abdurrab,

Mahmood,

Sheikh

et al. 2024

Healthcare

View full text Add to dashboard Cite

Length of stay (LoS) prediction is deemed important for a medical institution’s operational and logistical efficiency. Sound estimates of a patient’s stay increase clinical preparedness and reduce aberrations. Various statistical methods and techniques are used to quantify and predict the LoS of a patient based on pre-operative clinical features. This study evaluates and compares the results of Bayesian (simple Bayesian regression and hierarchical Bayesian regression) models and machine learning (ML) regression models against multiple evaluation metrics for the problem of LoS prediction of cardiac patients admitted to Tabba Heart Institute, Karachi, Pakistan (THI) between 2015 and 2020. In addition, the study also presents the use of hierarchical Bayesian regression to account for data variability and skewness without homogenizing the data (by removing outliers). LoS estimates from the hierarchical Bayesian regression model resulted in a root mean squared error (RMSE) and mean absolute error (MAE) of 1.49 and 1.16, respectively. Simple Bayesian regression (without hierarchy) achieved an RMSE and MAE of 3.36 and 2.05, respectively. The average RMSE and MAE of ML models remained at 3.36 and 1.98, respectively.

show abstract

“…There are some recent reviews of machine learning and statistical methods for the hospital length of stay estimation. [43,48,51,62,68]. Keegan [18] argued in favour of evidence showing that bed occupancy rate is a reliable key performance indicator for hospitals' capability to provide good quality care to patients.…”

Section: Background and Previous Researchmentioning

confidence: 99%

Characterising Hospital Admission Patterns and Length of Stay in the Emergency Department at Mater Dei Hospital Malta

Garg¹,

Attard²,

Caruana³

et al. 2023

Preprint

View full text Add to dashboard Cite

Healthcare professionals and resource planners can use healthcare delivery process mining to ensure the optimal utilisation of scarce healthcare resources when developing policies. Within hospitals, patients' Length of Stay (LOS) and volume of admitted patients, in terms of number and characteristics (age, gender, and social deter-minants), are significant factors determining daily resource requirements. In this study, we used Coxian phase-type Distribution (C-PHD) based Phase-Type Survival (PTS) trees for analysing how covariates such as admission date, gender, age, district, and admissions source influence the admission rate and LOS distribution. PTS trees. This study used a two-year data set (2011-2012) of patients admitted to the Emergency Department at Mater Dei Hospital to generate models and an independent one-year data set (2013) of patients admitted to the Emergency Department at Mater Dei Hospital to evaluate. The PTS tree effectively clusters patients based on their LOS, considering the prognostic significance of different covariates related to patients' characteristics. Charac-terising these covariates provided meaningful results about LOS. Similarly, the PTS tree was used to effectively cluster patients based on the admission rate, considering the prognostic significance of these covariates.

show abstract

A comparison of different Machine Learning algorithms for predicting the length of hospital stay for pediatric patients

Cited by 6 publications

References 15 publications

Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study

Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study

Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables—Bayesian Models vs. Machine Learning Models

Characterising Hospital Admission Patterns and Length of Stay in the Emergency Department at Mater Dei Hospital Malta

Contact Info

Product

Resources

About