Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.
Year: 2015
Type: article
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.
Source: PubMed Central
Authors: Nathaniel Roysden, Adam Wright
Institutions: Harvard University, Brigham and Women's Hospital
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2410748543