VITAL APP: Development and User Acceptability of an IoT-Based Patient Monitoring Device for Synchronous Measurements of Vital Signs

Garcia, Manuel B.; Pilueta, Nino U.; Jardiniano, Moises F.

doi:10.1109/hnicem48295.2019.9072724

Cited by 35 publications

(3 citation statements)

References 36 publications

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“…This level of expertise and granularity in data labeling is necessary to ensure clinician trust in automated clinical decision support solutions for pain detection. [32][33][34] The goal of this study was to capture and transform NICU nurses' labeling of pain assessment data to train a supervised, accurate, unbiased, and precise pain classification ML model. Although NICU nurses' expertise in pain assessment was leveraged to develop the supervised ML model, we predict that accuracy and precision of the ideal ML model will exceed the abilities of these same nurses.…”

Section: Literature Reviewmentioning

confidence: 99%

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Performance Evaluation of a Supervised Machine Learning Pain Classification Model Developed by Neonatal Nurses

Manworren,

Horner,

Joseph

et al. 2024

Advances in Neonatal Care

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Background: Early-life pain is associated with adverse neurodevelopmental consequences; and current pain assessment practices are discontinuous, inconsistent, and highly dependent on nurses’ availability. Furthermore, facial expressions in commonly used pain assessment tools are not associated with brain-based evidence of pain. Purpose: To develop and validate a machine learning (ML) model to classify pain. Methods: In this retrospective validation study, using a human-centered design for Embedded Machine Learning Solutions approach and the Neonatal Facial Coding System (NFCS), 6 experienced neonatal intensive care unit (NICU) nurses labeled data from randomly assigned iCOPEvid (infant Classification Of Pain Expression video) sequences of 49 neonates undergoing heel lance. NFCS is the only observational pain assessment tool associated with brain-based evidence of pain. A standard 70% training and 30% testing split of the data was used to train and test several ML models. NICU nurses’ interrater reliability was evaluated, and NICU nurses’ area under the receiver operating characteristic curve (AUC) was compared with the ML models’ AUC. Results: Nurses weighted mean interrater reliability was 68% (63%-79%) for NFCS tasks, 77.7% (74%-83%) for pain intensity, and 48.6% (15%-59%) for frame and 78.4% (64%-100%) for video pain classification, with AUC of 0.68. The best performing ML model had 97.7% precision, 98% accuracy, 98.5% recall, and AUC of 0.98. Implications for Practice and Research: The pain classification ML model AUC far exceeded that of NICU nurses for identifying neonatal pain. These findings will inform the development of a continuous, unbiased, brain-based, nurse-in-the-loop Pain Recognition Automated Monitoring System (PRAMS) for neonates and infants.

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Section: Literature Reviewmentioning

confidence: 99%

“…We determined that having a frame-level, nurses-in-the-loop, ground truth to train a neonatal pain detection model would improve model performance. This level of expertise and granularity in data labeling is necessary to ensure clinician trust in automated clinical decision support solutions for pain detection 32-34…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of a Supervised Machine Learning Pain Classification Model Developed by Neonatal Nurses

Manworren,

Horner,

Joseph

et al. 2024

Advances in Neonatal Care

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“…AI as a tool and/or technology is used to analyze and visualize patient data for adequate healthcare administration [ 10 ]. Much of the research on the influence of AI on medical outcomes has been beneficial and encouraging [ 11 ]. For example, health professionals and patients are increasingly utilizing and managing medical applications and medical-based games [ 12 ] not only to remotely monitor patients but also as evidence-based medicine [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

et al. 2022

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In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

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Human-centered IoT-based health monitoring in the Healthcare 5.0 era: literature descriptive analysis and future research guidelines

Rashid,

Nemati

2024

Discov Internet Things

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VITAL APP: Development and User Acceptability of an IoT-Based Patient Monitoring Device for Synchronous Measurements of Vital Signs

Cited by 35 publications

References 36 publications

Performance Evaluation of a Supervised Machine Learning Pain Classification Model Developed by Neonatal Nurses

Performance Evaluation of a Supervised Machine Learning Pain Classification Model Developed by Neonatal Nurses

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Human-centered IoT-based health monitoring in the Healthcare 5.0 era: literature descriptive analysis and future research guidelines

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