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

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  • Valter Santilli

    (Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy)

  • Massimiliano Mangone

    (Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy)

  • 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

    (Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy)

  • Francesco Agostini

    (Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy
    Department of Neurological and Rehabilitation Science, IRCCS San Raffaele Roma, Via della Pisana 235, 00163 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 Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy)

  • Michela Goffredo

    (Department of Neurological and Rehabilitation Science, IRCCS San Raffaele Roma, Via della Pisana 235, 00163 Rome, Italy)

  • Francesco Infarinato

    (Department of Neurological and Rehabilitation Science, IRCCS San Raffaele Roma, Via della Pisana 235, 00163 Rome, Italy)

  • Sanaz Pournajaf

    (Department of Neurological and Rehabilitation Science, IRCCS San Raffaele Roma, Via della Pisana 235, 00163 Rome, Italy)

  • Marco Franceschini

    (Department of Neurological and Rehabilitation Science, IRCCS San Raffaele Roma, Via della Pisana 235, 00163 Rome, Italy
    Department of Human Sciences and Promotion of Quality of Life, San Raffaele University, Via di Val Cannuta 247, 00166 Rome, Italy)

  • 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)

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.

Suggested Citation

  • Valter Santilli & Massimiliano Mangone & Anxhelo Diko & Federica Alviti & Andrea Bernetti & Francesco Agostini & Laura Palagi & Marila Servidio & Marco Paoloni & Michela Goffredo & Francesco Infarinat, 2023. "The Use of Machine Learning for Inferencing the Effectiveness of a Rehabilitation Program for Orthopedic and Neurological Patients," IJERPH, MDPI, vol. 20(8), pages 1-16, April.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:8:p:5575-:d:1127089
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    References listed on IDEAS

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    1. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
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    Cited by:

    1. T. Bradley Willingham & Julie Stowell & George Collier & Deborah Backus, 2024. "Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities," IJERPH, MDPI, vol. 21(1), pages 1-28, January.
    2. Massimiliano Mangone & Anxhelo Diko & Luca Giuliani & Francesco Agostini & Marco Paoloni & Andrea Bernetti & Gabriele Santilli & Marco Conti & Alessio Savina & Giovanni Iudicelli & Carlo Ottonello & V, 2023. "A Machine Learning Approach for Knee Injury Detection from Magnetic Resonance Imaging," IJERPH, MDPI, vol. 20(12), pages 1-11, June.

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