PMID- 37588696
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230818
IS  - 2624-8212 (Electronic)
IS  - 2624-8212 (Linking)
VI  - 6
DP  - 2023
TI  - Machine learning-based prediction of hospital prolonged length of stay admission 
      at emergency department: a Gradient Boosting algorithm analysis.
PG  - 1179226
LID - 10.3389/frai.2023.1179226 [doi]
LID - 1179226
AB  - OBJECTIVE: This study aims to develop and compare different models to predict the 
      Length of Stay (LoS) and the Prolonged Length of Stay (PLoS) of inpatients 
      admitted through the emergency department (ED) in general patient settings. This 
      aim is not only to promote any specific model but rather to suggest a 
      decision-supporting tool (i.e., a prediction framework). METHODS: We analyzed a 
      dataset of patients admitted through the ED to the "Sant"Orsola Malpighi 
      University Hospital of Bologna, Italy, between January 1 and October 26, 2022. 
      PLoS was defined as any hospitalization with LoS longer than 6 days. We deployed 
      six classification algorithms for predicting PLoS: Random Forest (RF), Support 
      Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors 
      (KNN), and logistic regression (LoR). We evaluated the performance of these 
      models with the Brier score, the area under the ROC curve (AUC), accuracy, 
      sensitivity (recall), specificity, precision, and F1-score. We further developed 
      eight regression models for LoS prediction: Linear Regression (LR), including the 
      penalized linear models Least Absolute Shrinkage and Selection Operator (LASSO), 
      Ridge and Elastic-net regression, Support vector regression, RF regression, KNN, 
      and eXtreme Gradient Boosting (XGBoost) regression. The model performances were 
      measured by their mean square error, mean absolute error, and mean relative 
      error. The dataset was randomly split into a training set (70%) and a validation 
      set (30%). RESULTS: A total of 12,858 eligible patients were included in our 
      study, of whom 60.88% had a PloS. The GB classifier best predicted PloS (accuracy 
      75%, AUC 75.4%, Brier score 0.181), followed by LoR classifier (accuracy 75%, AUC 
      75.2%, Brier score 0.182). These models also showed to be adequately calibrated. 
      Ridge and XGBoost regressions best predicted LoS, with the smallest total 
      prediction error. The overall prediction error is between 6 and 7 days, meaning 
      there is a 6-7 day mean difference between actual and predicted LoS. CONCLUSION: 
      Our results demonstrate the potential of machine learning-based methods to 
      predict LoS and provide valuable insights into the risks behind prolonged 
      hospitalizations. In addition to physicians' clinical expertise, the results of 
      these models can be utilized as input to make informed decisions, such as 
      predicting hospitalizations and enhancing the overall performance of a public 
      healthcare system.
CI  - Copyright (c) 2023 Zeleke, Palumbo, Tubertini, Miglio and Chiari.
FAU - Zeleke, Addisu Jember
AU  - Zeleke AJ
AD  - Department of Electrical, Electronic, and Information Engineering Guglielmo 
      Marconi, University of Bologna, Bologna, Italy.
FAU - Palumbo, Pierpaolo
AU  - Palumbo P
AD  - Department of Electrical, Electronic, and Information Engineering Guglielmo 
      Marconi, University of Bologna, Bologna, Italy.
FAU - Tubertini, Paolo
AU  - Tubertini P
AD  - Enterprise Information Systems for Integrated Care and Research Data Management, 
      Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Azienda 
      Ospedaliero-Universitaria di Bologna, Bologna, Italy.
FAU - Miglio, Rossella
AU  - Miglio R
AD  - Department of Statistical Sciences, University of Bologna, Bologna, Italy.
FAU - Chiari, Lorenzo
AU  - Chiari L
AD  - Department of Electrical, Electronic, and Information Engineering Guglielmo 
      Marconi, University of Bologna, Bologna, Italy.
AD  - Health Sciences and Technologies Interdepartmental Center for Industrial Research 
      (CIRI SDV), University of Bologna, Bologna, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230728
PL  - Switzerland
TA  - Front Artif Intell
JT  - Frontiers in artificial intelligence
JID - 101770551
PMC - PMC10426288
OTO - NOTNLM
OT  - classification
OT  - emergency department
OT  - machine learning
OT  - prediction
OT  - prolonged length of stay
OT  - regression
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/08/17 06:42
MHDA- 2023/08/17 06:43
PMCR- 2023/07/28
CRDT- 2023/08/17 04:25
PHST- 2023/03/06 00:00 [received]
PHST- 2023/07/10 00:00 [accepted]
PHST- 2023/08/17 06:43 [medline]
PHST- 2023/08/17 06:42 [pubmed]
PHST- 2023/08/17 04:25 [entrez]
PHST- 2023/07/28 00:00 [pmc-release]
AID - 10.3389/frai.2023.1179226 [doi]
PST - epublish
SO  - Front Artif Intell. 2023 Jul 28;6:1179226. doi: 10.3389/frai.2023.1179226. 
      eCollection 2023.