A data-driven predictive system using Case-Based Reasoning for the configuration of device-assisted back pain therapy

ABSTRACT

Lower back Pain (LBP) is pathological and occurs in about 80% of the population at least once in their life. Physiotherapists personalise manual treatments to heal or relieve pain according to the patient characteristics. The contribution of this methodological paper is the description and evaluation of the configuration software associated to a therapy machine that executes back segment mobilisations. The configuration software uses Case-Based Reasoning (CBR), a successful Machine Learning technique, based on mimicking the human decision making process by reusing previously applied configuration episodes on similar individuals. This paper demonstrates its feasibility and cost-effectiveness for the configuration of treatments as it reuses expert knowledge and maximises effectiveness by taking into account the patient’s personal medical record and similar patterns among different patients. Having a baseline of 31% success rate using a standard solution based on interpolation, the CBR engine can achieve, on average, up to 70% success rate when proposing a machine configuration to the physiotherapist. Regarding clinical results, we run a longitudinal observational study that achieves an average improvement of 31.63% using the pain Visual Analogue Scale (VAS), a 7% according to the Oswestry Disability Index (ODI), and 13% in the 36-Item Short Form Health Survey (SF-36).

KEYWORDS:

• Case Based Reasoning
• Visual Explanations
• Personalisation
• Device-assisted back pain therapy

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. http://gaia.fdi.ucm.es/research/kbs/.

Additional information

Funding

This work was supported by the Complutense University of Madrid (Group 921330) and the Spanish Committee of Economy and Competitiveness (TIN2017-87330-R) Secretaría de Estado de Investigación, Desarrollo e Innovación [TIN2017-87330-R].