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A machine learning model for predicting ICU readmissions and key risk factors: analysis from a longitudinal health records

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Abstract

Due to high costs, resources and managemant associated with readmission into Intensive Care Units (ICU), it has been a center of clinical research. Previous research successfully identified several common risk factors and proposed a variety of frameworks to predict ICU readmissions, whereas, some studies reported that many risk factors were too specific and/or had limited focus. This study aims to investigate and analyze if the relevance of ICU readmission risk factors may have changed overtime. We used MIMIC-III database with 42,307 ICU stays of 31,749 patients from a US hospital, related to medical services provided from 2001 to 2012. The dataset was initially split into two chronological subsets (2001–2008 and 2008–2012), and then split again into train (70%) and test (30%) datasets. The training datasets were rebalanced through undersampling technique. To identify if the most relevant risk factors changes over time, 13 variables (12 features and one class) were selected and a three-step machine learning approach was executed: (i) Numerical Analysis, to identify overall quantitative changes; (ii) Feature Correlation Value Analysis, to rank the most important risk factors in each subset and compare them to identify any significant changes; and (iii) Classifier Performance Analysis, to identify changes in the risk factors prediction capability, based on the three machine learning algorithms - Multilayer Perceptron, Random Forest and Support Vector Machine. When considering readmission rates, some changes were observed for patients using private insurance (variability of +3.0%) and first admitted in ICU through Medical Intensive Care Unit (−3.1%). Regarding the feature analysis, the two most relevant variables were the same in both datasets, having similar correlation value. When applying the machine learning algorithms in test datasets, the model presented similar results for both periods, achieving the best accuracy of 86.4%, and Area Under ROC Curve (AUC) of 0.642. The difference in AUC values between the first and the second periods varied up to 0.05 (better in the first dataset) and in accuracy up to 4% (better in the second period). Overall results indicate that the most relevant risk factors were stable over the years, with some minor changes. Further research is required to incorporate other readmission risk factors, such as social determinants and mental health and well-being.

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Notes

  1. LACE is the acronym for Length of Stay (L), Acute Admission (A), Comorbidity (C) and Visits to Emergency (E)

  2. HOSPITAL is the acronym for Hemoglobin at discharge (H), discharge from an oncology service (O), sodium level at discharge (S), procedure during the index admission (P), index type of admission (IT), number of admissions during the last 12 months (A), and length of stay (L)

  3. PARR-30 is the acronym for “Patients at Risk of Re-admission within 30 days” 10. Billings, J., et al., Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30). BMJ open, 2012. 2(4): p. e001667.

  4. PREADM is the acronym for “Preadmission Readmission Detection Model”

  5. LOS, Number of Services, service MED and service SURG

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Authors and Affiliations

  1. School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand

    Alvaro Ribeiro Botelho Junqueira, Farhaan Mirza & Mirza Mansoor Baig

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  1. Alvaro Ribeiro Botelho Junqueira
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  2. Farhaan Mirza
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  3. Mirza Mansoor Baig
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Correspondence to Mirza Mansoor Baig.

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Junqueira, A.R.B., Mirza, F. & Baig, M.M. A machine learning model for predicting ICU readmissions and key risk factors: analysis from a longitudinal health records. Health Technol. 9, 297–309 (2019). https://doi.org/10.1007/s12553-019-00329-0

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