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

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 mac hine 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 i dentify 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...
Source: Health and Technology - Category: Information Technology Source Type: research