Efeitos dos Canabinoides no Tremor Parkinsoniano e interações medicamentosas: análise por inteligência artificial explicável dos dados Fox Insight

Abstract

Parkinson’s disease constitutes the second most prevalent neurodegenerative condition worldwide, affecting 1-2% of the population over 65 years of age, with tremor present in 70% of cases. Conventional pharmacological treatments present limitations in 30-40% of patients, establishing the need for therapeutic alternatives. This thesis aimed to develop an explainable artificial intelligence methodology for personalizing medical cannabis use in treating parkinsonian tremor, quantifying determinants of individual heterogeneity in therapeutic response. The methodology employed analysis based on SHAP (SHapley Additive exPlanations) values applied to 2,616 records from the Fox Insight platform, structured in three progressive datasets: SINGLE (n=973, isolated cannabis), FULL_NUMERICO (n=931, pharmacological groups), and FULL_GFEM (n=931, detailed individual characteristics). Machine learning models achieved elevated performance metrics: SINGLE (LightGBM) with accuracy 96.31%, precision 96.33%, recall 96.31%, F1-score 96.31%, and specificity 96.03%; FULL_NUMERICO (LightGBM) with accuracy 97.42%, precision 97.47%, recall 97.42%, F1-score 97.42%, and specificity 97.07%; FULL_GFEM (XGBoost) with accuracy 97.42%, precision 97.54%, recall 97.42%, F1-score 97.42%, and specificity 96.91%, demonstrating that interpretability does not compromise predictive performance when architecture is adequately designed. Results quantified eight main findings: (1) pronounced sexual dimorphism, with women presenting mean SHAP values of +1.52 ± 0.48 (improvement rate 94.1%) versus men -0.93 ± 0.37 (rate 38.5%), establishing a magnitude of 2.44x in the probability of favorable response; (2) identification of six distinct profiles through clustering, with 55% of patients presenting elevated improvement rates (89% to 100%) and SHAP values between +1.94 and +2.57; (3) non-linear inverted-U age pattern, with critical group 60-70 years (n=559, 60% of sample) presenting worse response (rate 38.8%) and partial recovery in 70-80 years (rate 76.2%, n=181); (4) hierarchy of administration routes quantified by MASV (Mean Absolute SHAP Value): oil (0.705) > sublingual (0.4959) > food (0.657 Class 1), demonstrating importance superior to dose; (5) bifasic optimal therapeutic windows for THC (1.5-2.5 mg/day, MASV = 0.2888) and CBD (2-4 mg/day, MASV = 0.2654), empirically validating that supra-therapeutic doses reduce efficacy; (6) quantified synergy with MAO-B inhibitors (MASV = 0.4485), superior to interactions with levodopa (MASV = 0.2506) and other dopaminergics (MASV = 0.2454); (7) moderate optimal frequency of 3-5 times/week, superior to continuous daily use; (8) therapeutic learning curve with optimization in 2-3 years and possible tolerance after >3 years. Findings were operationalized in a clinical nomogram that stratifies patients into five predictive categories, with 87.3% concordance with phenotypic clusters. Computational analysis through clustering identified difference in improvement rates between subgroups with elevated response (89-100%, n=513) and general population (66.4%, n=931), suggesting potential for therapeutic personalization based on identifiable clinical characteristics, hypothesis requiring confirmation in future clinical studies. This investigation establishes a new methodological standard for research in precision neurological medicine, providing computational tools and quantitative knowledge for prospective identification of subgroups with high probability of benefit, individualized therapeutic regimen optimization, and establishment of expectations based on measurable patient characteristics.