多发性骨髓瘤（MM）患者的响应状态监测的金标准是血清和尿液蛋白电泳测定M峰蛋白；然而，结果的反应时间为3-7天，这会延误治疗决策。我们假设机器学习（ML）可以整合易得的临床和实验室数据，快速准确地预测患者M峰值。通过回顾性病历回顾，使用171名MM患者的匿名化电子病历进行研究。随机森林（RF）分析识别独立变量（N=43）的加权值，将其整合到ML算法中。Pearson和Spearman系数表明，ML预测的M峰值与实验室测得的血清蛋白电泳值高度相关。特征选择的RF建模显示，只有两个变量——第一个滞后的M峰和血清总蛋白——能准确预测M峰。综上所述，我们的结果表明，ML工具结合电子数据进行疾病负担的长期监测具有可行性和预后潜力。ML工具支持平滑、安全地交换患者信息，以加快和个性化临床决策，并克服目前限制弱势群体访问癌症专家的地理、经济和社会障碍，使医疗进展的好处不仅限于少数群体。

The gold standard for monitoring response status in patients with multiple myeloma (MM) is serum and urine protein electrophoresis which quantify M-spike proteins; however, the turnaround time for results is 3-7 days which delays treatment decisions. We hypothesized that machine learning (ML) could integrate readily available clinical and laboratory data to rapidly and accurately predict patient M-spike values.A retrospective chart review was performed using the deidentified, electronic medical records of 171 patients with MM.Random forest (RF) analysis identified the weighted value of each independent variable (N = 43) integrated into the ML algorithm. Pearson and Spearman coefficients indicated that the ML-predicted M-spike values correlated highly with laboratory-measured serum protein electrophoresis values. Feature selected RF modeling revealed that only two variables-the first lagged M-spike and serum total protein-accurately predicted the M-spike.Taken together, our results demonstrate the feasibility and prognostic potential of ML tools that integrate electronic data to longitudinally monitor disease burden. ML tools support the seamless, secure exchange of patient information to expedite and personalize clinical decision making and overcome geographic, financial, and social barriers that currently limit the access of underserved populations to cancer care specialists so that the benefits of medical progress are not limited to selected groups.