title: Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.
abstract: 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.
authors.affiliation: Harvard Medical School, Boston, MA.
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Roysden
authors.affiliation: Harvard Medical School, Boston, MA; Brigham and Women's Hospital, Boston, MA.
authors.forename: Adam
authors.initials: A
authors.lastname: Wright
mesh_terms: D003365:Costs and Cost Analysis; D020000:Decision Support Systems, Clinical; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing; D012017:Referral and Consultation