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Journal of Medical Systems

, 42:261 | Cite as

Prediction of Incident Delirium Using a Random Forest classifier

  • John P. Corradi
  • Stephen Thompson
  • Jeffrey F. Mather
  • Christine M. Waszynski
  • Robert S. Dicks
Systems-Level Quality Improvement
  • 118 Downloads
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visits to train and test a model predicting delirium arising in hospital. Incident delirium was defined as the first instance of a positive CAM occurring at least 48 h into a hospital stay. A Random Forest machine learning algorithm was used with demographic data, comorbidities, medications, procedures, and physiological measures. The data set was randomly partitioned 80% / 20% for training and validating the predictive model, respectively. Of the 51,240 patients in the training set, 2774 (5.4%) experienced delirium during their hospital stay; and of the 12,798 patients in the validation set, 701 (5.5%) experienced delirium. Under-sampling of the delirium negative population was used to address the class imbalance. The Random Forest predictive model yielded an area under the receiver operating characteristic curve (ROC AUC) of 0.909 (95% CI 0.898 to 0.921). Important variables in the model included previously identified predisposing and precipitating risk factors. This machine learning approach displayed a high degree of accuracy and has the potential to provide a clinically useful predictive model for earlier intervention in those patients at greatest risk of developing delirium.

Keywords

Delirium Prediction Decision support Machine learning Random forest 

Notes

Compliance with Ethical Standards

Conflicts of Interest

The authors do not have any conflicts of interest to declare. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

For this type of study formal consent is not required.

Supplementary material

10916_2018_1109_MOESM1_ESM.xlsx (16 kb)
ESM 1 (XLSX 16 kb)
10916_2018_1109_MOESM2_ESM.pdf (255 kb)
ESM 2 (PDF 255 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research DepartmentHartford HospitalHartfordUSA
  2. 2.Division of Geriatric MedicineHartford HospitalHartfordUSA

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