Lower back Pain (LBP) is pathological and occurs in about 80% of the population at least once in their life. Physiotherapists personalize manual treatments to heal or relieve pain according to the patient characteristics. The contribution of this research is the description and evaluation of the configuration software associated to a therapy machine that executes back segment mobilizations.
The configuration software uses Case-Based Reasoning (CBR), based on mimicking the human decision making process by reusing previously applied configuration episodes on similar individuals.
The CBR engine can achieve, on average, up to 75% 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).
This project proposes a machine learning methodology and demonstrates its feasibility and cost-effectiveness for the personalization of treatments as it reuses expert knowledge and maximizes effectiveness by taking into account the patient's personal medical record and similar patterns among different patients.