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 prediction of the therapeutic response in a panoramic and feedback way while unveiling relevant medical factors. Panoramic prediction makes it possible to decide whether the treatment will be beneficial without using previous knowledge and without involving unnecessary treatments. Feedback prediction can be more accurate prediction 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 prediction in both approaches. To unveil the medical factors that make the treatment effective for patients, various techniques are applied to the prediction 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 prediction, and up to around 90% when using feedback prediction. Furthermore, the following factors have been found to be relevant when predicting 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.

Keywords

• Multi-target prediction
• classification algorithms
• data mining
• simulated annealing