TY  - JOUR
PY  - 2019//
TI  - Developing machine learning models for behavioral coding
JO  - Journal of pediatric psychology
A1  - Idalski Carcone, April
A1  - Hasan, Mehedi
A1  - Alexander, Gwen L.
A1  - Dong, Ming
A1  - Eggly, Susan
A1  - Brogan Hartlieb, Kathryn
A1  - Naar, Sylvie
A1  - MacDonell, Karen
A1  - Kotov, Alexander
SP  - 289
EP  - 299
VL  - 44
IS  - 3
N2  - 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. <br><br>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. <br><br>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). <br><br>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.<p />  <p>Language: en</p>
LA  - en
SN  - 0146-8693
UR  - http://dx.doi.org/10.1093/jpepsy/jsy113
ID  - ref1
ER  -