Profiling Patients by Intensity of Nursing Care: An Operative Approach Using Machine Learning

Honoria Ocagli; Corrado Lanera; Giulia Lorenzoni; Ilaria Prosepe; Danila Azzolina; Sabrina Bortolotto; Lucia Stivanello; Mario Degan; Dario Gregori

doi:10.3390/jpm10040279

Profiling Patients by Intensity of Nursing Care: An Operative Approach Using Machine Learning

J Pers Med. 2020 Dec 14;10(4):279. doi: 10.3390/jpm10040279.

Authors

Honoria Ocagli¹, Corrado Lanera¹, Giulia Lorenzoni¹, Ilaria Prosepe¹, Danila Azzolina^{1

2}, Sabrina Bortolotto¹, Lucia Stivanello³, Mario Degan³, Dario Gregori¹

Affiliations

¹ Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, via Loredan, 18, 35121 Padova, Italy.
² Department of Translational Medicine, University of Piemonte Orientale, 28100 Novara, Italy.
³ Health Professional Management Service (DPS) of the University Hospital of Padova, 35128 Padova, Italy.

Abstract

Physical function is a patient-oriented indicator and can be considered a proxy for the assignment of healthcare personnel. The study aims to create an algorithm that classifies patients into homogeneous groups according to physical function. A two-step machine-learning algorithm was applied to administrative data recorded between 2015 and 2018 at the University Hospital of Padova. A clustering-large-applications (CLARA) algorithm was used to partition patients into homogeneous groups. Then, machine learning technique (MLT) classifiers were used to categorize the doubtful records. Based on the results of the CLARA algorithm, records were divided into three groups according to the Barthel index: <45, >65, ≥45 and ≤65. The support vector machine was the MLT showing the best performance among doubtful records, reaching an accuracy of 66%. The two-step algorithm, since it splits patients into low and high resource consumption, could be a useful tool for organizing healthcare personnel allocation according to the patients' assistance needs.

Keywords: Barthel index; CLARA; intensity of nursing care; machine learning technique.