ArticleNPJ digital medicine2023
Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Missing data handling in pediatric appendectomy research: current practices and National Surgical Quality Improvement Program (NSQIP) analysis.Surgical endoscopy · 2026Pooled it
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- A Critical Methodological Review of Clinical Scores, Machine Learning, and Large Language Models for the Diagnosis and Management of Pediatric Appendicitis.Healthcare (Basel, Switzerland) · 2026Review
- Increasing Use of Wearable Devices for Health or Activity Tracking Among Adults with Chronic Diseases in the US.Journal of the American College of Surgeons · 2026Article
- Latest Developments in Artificial Intelligence and Machine Learning Models in General Pediatric Surgery.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026Review
- Applications of artificial intelligence in pediatric general surgery: a systematic review.Translational pediatrics · 2026Review
- Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis-Diagnosis, severity assessment and evidence-based clinical decision support.PLOS digital health · 2026Article
- Integration of Artificial Intelligence and Wearable Devices in Pediatric Clinical Care: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Integrating a Multimodal Digital Device for Continuous Perioperative Monitoring in Patients With Lung Cancer Undergoing Thoracic Surgery: Development and Usability Study.JMIR mHealth and uHealth · 2025Article
- A roadmap of artificial intelligence applications in pediatric surgery: a comprehensive review of applications, challenges, and ethical considerations.Pediatric surgery international · 2025Review
- Biorhythms derived from consumer wearables predict postoperative complications in children.Science advances · 2025Article
- Review
- A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions.Journal of clinical medicine · 2025Review
- Overcoming biases of individual level shopping history data in health research.NPJ digital medicine · 2024Review
- Machine Learning Models and Applications for Early Detection.Sensors (Basel, Switzerland) · 2024Review
- Improving Early Prediction of Abnormal Recovery after Appendectomy in Children using Real-world Data from Wearables.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review.Updates in surgery · 2024Review
- Feasibility of Leveraging Consumer Wearable Devices with Data Platform Integration for Patient Vital Monitoring in Low-Resource Settings.International journal of telemedicine and applications · 2024Article
- AI in Rehabilitation Medicine: Opportunities and Challenges.Annals of rehabilitation medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.