The Use of Machine Learning for Inferencing the Effectiveness of a Rehabilitation Program for Orthopedic and Neurological Patients.

Journal: International journal of environmental research and public health
Published Date:

Abstract

Advance assessment of the potential functional improvement of patients undergoing a rehabilitation program is crucial in developing precision medicine tools and patient-oriented rehabilitation programs, as well as in better allocating resources in hospitals. In this work, we propose a novel approach to this problem using machine learning algorithms focused on assessing the modified Barthel index (mBI) as an indicator of functional ability. We build four tree-based ensemble machine learning models and train them on a private training cohort of orthopedic (OP) and neurological (NP) hospital discharges. Moreover, we evaluate the models using a validation set for each category of patients using root mean squared error (RMSE) as an absolute error indicator between the predicted mBI and the actual values. The best results obtained from the study are an RMSE of 6.58 for OP patients and 8.66 for NP patients, which shows the potential of artificial intelligence in predicting the functional improvement of patients undergoing rehabilitation.

Authors

  • Valter Santilli
    Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy.
  • Massimiliano Mangone
    Physical Medicine and Rehabilitation Unit, Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy. massimiliano.mangone@uniroma1.it.
  • Anxhelo Diko
    Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy.
  • Federica Alviti
    Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy.
  • Andrea Bernetti
    Physical Medicine and Rehabilitation Unit, Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy.
  • Francesco Agostini
    Physical Medicine and Rehabilitation Unit, Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy.
  • Laura Palagi
    Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy.
  • Marila Servidio
    Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy.
  • Marco Paoloni
    Department of Physical Medicine and Rehabilitation, Sapienza University of Rome, 00161 Rome, Italy.
  • Michela Goffredo
    Department of Neurorehabilitation IRCCS San Raffaele Pisana, Rome, Italy.
  • Francesco Infarinato
    Department of NeuroRehabilitation, IRCCS San Raffaele Pisana, Rome, Italy. francesco.infarinato@sanraffaele.it.
  • Sanaz Pournajaf
    Department of Neurorehabilitation IRCCS San Raffaele Pisana, Rome, Italy.
  • Marco Franceschini
  • Massimo Fini
    Department of Neurological and Rehabilitation Science, IRCCS San Raffaele Roma, Via della Pisana 235, 00163 Rome, Italy.
  • Carlo Damiani
    Department of Neurological and Rehabilitation Science, IRCCS San Raffaele Roma, Via della Pisana 235, 00163 Rome, Italy.