Details

Autor(en) / Beteiligte

• Idalski Carcone, April
• Hasan, Mehedi
• Alexander, Gwen L
• Dong, Ming
• Eggly, Susan
• Brogan Hartlieb, Kathryn
• Naar, Sylvie
• MacDonell, Karen
• Kotov, Alexander

Titel

Developing Machine Learning Models for Behavioral Coding

Ist Teil von

• Journal of pediatric psychology, 2019-04, Vol.44 (3), p.289-299

Ort / Verlag

United States: Oxford University Press

Erscheinungsjahr

2019

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• Abstract Objective The goal of this research is to develop a machine learning supervised classification model to automatically code clinical encounter transcripts using a behavioral code scheme. Methods We first evaluated the efficacy of eight state-of-the-art machine learning classification models to recognize patient–provider communication behaviors operationalized by the motivational interviewing framework. Data were collected during the course of a single weight loss intervention session with 37 African American adolescents and their caregivers. We then tested the transferability of the model to a novel treatment context, 80 patient–provider interactions during routine human immunodeficiency virus (HIV) clinic visits. Results Of the eight models tested, the support vector machine model demonstrated the best performance, achieving a .680 F1-score (a function of model precision and recall) in adolescent and .639 in caregiver sessions. Adding semantic and contextual features improved accuracy with 75.1% of utterances in adolescent and 73.8% in caregiver sessions correctly coded. With no modification, the model correctly classified 72.0% of patient–provider utterances in HIV clinical encounters with reliability comparable to human coders (k = .639). Conclusions The development of a validated approach for automatic behavioral coding offers an efficient alternative to traditional, resource-intensive methods with the potential to dramatically accelerate the pace of outcomes-oriented behavioral research. The knowledge gained from computer-driven behavioral research can inform clinical practice by providing clinicians with empirically supported communication strategies to tailor their conversations with patients. Lastly, automatic behavioral coding is a critical first step toward fully automated eHealth/mHealth (electronic/mobile Health) behavioral interventions.