International journal of medical informatics · 2024
Application of a human-centered design for embedded machine learning model to develop data labeling software with nurses: Human-to-Artificial Intelligence (H2AI).
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Kaduwela Naomi A, Horner Susan, Dadar Priyansh, Manworren Renee C B
Аннотация
BACKGROUND: 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.
PURPOSE: To develop an intuitive and efficient data labeling software solution, Human-to-Artificial Intelligence (H2AI).
METHOD: 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.
FINDINGS: 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.
CONCLUSIONS: 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.
PURPOSE: To develop an intuitive and efficient data labeling software solution, Human-to-Artificial Intelligence (H2AI).
METHOD: 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.
FINDINGS: 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.
CONCLUSIONS: 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.