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Majeedi A, McAdams RM, Kaur R, Gupta S, Singh H, Li Y. Deep learning to quantify care manipulation activities in neonatal intensive care units. NPJ Digit Med 2024; 7:172. [PMID: 38937643 PMCID: PMC11211355 DOI: 10.1038/s41746-024-01164-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 06/12/2024] [Indexed: 06/29/2024] Open
Abstract
Early-life exposure to stress results in significantly increased risk of neurodevelopmental impairments with potential long-term effects into childhood and even adulthood. As a crucial step towards monitoring neonatal stress in neonatal intensive care units (NICUs), our study aims to quantify the duration, frequency, and physiological responses of care manipulation activities, based on bedside videos and physiological signals. Leveraging 289 h of video recordings and physiological data within 330 sessions collected from 27 neonates in 2 NICUs, we develop and evaluate a deep learning method to detect manipulation activities from the video, to estimate their duration and frequency, and to further integrate physiological signals for assessing their responses. With a 13.8% relative error tolerance for activity duration and frequency, our results were statistically equivalent to human annotations. Further, our method proved effective for estimating short-term physiological responses, for detecting activities with marked physiological deviations, and for quantifying the neonatal infant stressor scale scores.
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Affiliation(s)
- Abrar Majeedi
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Ryan M McAdams
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Ravneet Kaur
- Child Health Imprints (CHIL) USA Inc, Madison, WI, USA
| | - Shubham Gupta
- Child Health Imprints (CHIL) USA Inc, Madison, WI, USA
| | | | - Yin Li
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Computer Sciences, School of Computer, Data and Information Sciences, College of Letters and Science, University of Wisconsin-Madison, Madison, WI, USA.
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Goodwin AJ, Eytan D, Dixon W, Goodfellow SD, Doherty Z, Greer RW, McEwan A, Tracy M, Laussen PC, Assadi A, Mazwi M. Timing errors and temporal uncertainty in clinical databases-A narrative review. Front Digit Health 2022; 4:932599. [PMID: 36060541 PMCID: PMC9433547 DOI: 10.3389/fdgth.2022.932599] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022] Open
Abstract
A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.
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Affiliation(s)
- Andrew J. Goodwin
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- School of Biomedical Engineering, University of Sydney, Sydney, NSW, Australia
| | - Danny Eytan
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
| | - William Dixon
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Sebastian D. Goodfellow
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON, Canada
| | - Zakary Doherty
- Research Fellow, School of Rural Health, Monash University, Melbourne, VIC, Australia
| | - Robert W. Greer
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Alistair McEwan
- School of Biomedical Engineering, University of Sydney, Sydney, NSW, Australia
| | - Mark Tracy
- Neonatal Intensive Care Unit, Westmead Hospital, Sydney, NSW, Australia
- Department of Paediatrics and Child Health, The University of Sydney, Sydney, NSW, Australia
| | - Peter C. Laussen
- Department of Anesthesia, Boston Children's Hospital, Boston, MA, United States
| | - Azadeh Assadi
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Engineering and Applied Sciences, Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Mjaye Mazwi
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
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