An Acoustical and Lexical Machine-Learning Pipeline to Identify .

Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. To assess the feasibility of automating the identification of a conversational feature, which is associated with important patient outcomes. Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools-a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts.

The Project: Feasibility and Acceptability of a Remotely Delivered Intervention to Alleviate Grief during the COVID-19 Pandemic.

The events surrounding the COVID-19 pandemic have created heightened challenges to coping with loss and grief for family and friends of deceased individuals, as well as clinicians who experience loss of their patients. There is an urgent need for remotely delivered interventions to support those experiencing grief, particularly due to growing numbers of bereaved individuals during the COVID-19 pandemic. To determine the feasibility and acceptability of the brief, remotely delivered StoryListening storytelling intervention for individuals experiencing grief during the COVID pandemic.