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SMURF: Systematic Methodology for Unveiling Relevant Factors in Retrospective Data on Chronic Disease Treatments

Abstract:

Deciding on the continuous treatment of chronic diseases is vital in terms of economy, quality of life, and time. We present a holistic data mining approach that addresses the pbkp_rediction of the therapeutic response in a panoramic and feedback way while unveiling relevant medical factors. Panoramic pbkp_rediction makes it possible to decide whether the treatment will be beneficial without using previous knowledge and without involving unnecessary treatments. Feedback pbkp_rediction can be more accurate pbkp_rediction since it considers the results of previous stages of the treatment. A novel label encoding called simulated annealing and rounding (SAR) encoding is also proposed to help improve the accuracy of pbkp_rediction in both approaches. To unveil the medical factors that make the treatment effective for patients, various techniques are applied to the pbkp_rediction models found through the proposed approaches. Finally, this methodology is applied in the realistic scenario of analyzing electronic medical records of migraineurs under BoNT-A treatment. The results show a significant improvement in accuracy due to the use of SAR encoding, from close to 60% (baseline) to 75% with panoramic pbkp_rediction, and up to around 90% when using feedback pbkp_rediction. Furthermore, the following factors have been found to be relevant when pbkp_redicting the migraine treatment responses: migraine time evolution, unilateral pain, analgesic abuse, headache days, and the retroocular component. According to doctors, these factors are also medically relevant and in alignment with the medical literature.