Roysden Nathaniel, Wright Adam
Harvard Medical School, Boston, MA.
Harvard Medical School, Boston, MA; Brigham and Women's Hospital, Boston, MA.
AMIA Annu Symp Proc. 2015 Nov 5;2015:2063-72. eCollection 2015.
Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient's first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection factor analysis.
心理健康问题是医疗保健利用率增加的独立预测因素。我们创建了随机森林分类器，用于预测患者首次行为健康就诊后的两种结果：利用率有任何程度的降低（曲线下面积为0.74）和超高绝对利用率（曲线下面积为0.88）。这些模型可用于为转诊提供者提供临床决策支持，自动检测可能从转诊中受益的患者，进行成本管理或进行风险/保护因素分析。
