Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning
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Published on: September 20, 2018
Nathaniel Roysden1, Adam Wright2
1Harvard Medical School, Boston, MA.
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.
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