Application of a human-centered design for embedded machine learning model to develop data labeling software with...

Nurses are essential for assessing and managing acute pain in hospitalized patients, especially those who are unable to self-report pain. Given their role and subject matter expertise (SME), nurses are also essential for the design and development of a supervised machine learning (ML) model for pain detection and clinical decision support software (CDSS) in a pain recognition automated monitoring system (PRAMS). Our first step for developing PRAMS with nurses was to create SME-friendly data labeling software.

To develop an intuitive and efficient data labeling software solution, Human-to-Artificial Intelligence (H2AI).

The Human-centered Design for Embedded Machine Learning Solutions (HCDe-MLS) model was used to engage nurses. In this paper, HCDe-MLS will be explained using H2AI and PRAMS as illustrative cases.

Using HCDe-MLS, H2AI was developed and facilitated labeling of 139 videos (mean = 29.83 min) with 3189 images labeled (mean = 75 s) by 6 nurses. OpenCV was used for video-to-image pre-processing; and MobileFaceNet was used for default landmark placement on images. H2AI randomly assigned videos to nurses for data labeling, tracked labelers' inter-rater reliability, and stored labeled data to train ML models.

Nurses' engagement in CDSS development was critical for ensuring the end-product addressed nurses' priorities, reflected nurses' cognitive and decision-making processes, and garnered nurses' trust for technology adoption.