Spoken words as biomarkers: using machine learning to gain insight into communication as a predictor of anxiety.

Objective: The goal of this study was to explore whether features of recorded and transcribed audio communication data extracted by machine learning algorithms can be used to train a classifier for anxiety.

Materials And Methods: We used a secondary data set generated by a clinical trial examining problem-solving therapy for hospice caregivers consisting of 140 transcripts of multiple, sequential conversations between an interviewer and a family caregiver along with standardized assessments of anxiety prior to each session; 98 of these transcripts (70%) served as the training set, holding the remaining 30% of the data for evaluation.

Results: A classifier for anxiety was developed relying on language-based features.