כתבה
arXiv cs.AI ·
FHRFormer: פרקטיקה עצמאית לשיקוף ולחיזוי של קצב לב העובר
FHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting
מודל חדש לחיזוי תסמינים בקצב לב העובר, על סמך נתוני מעקב רציפים.
תקציר מקורי באנגליתarXiv:2605.29695v2 Announce Type: replace Abstract: Approximately 10% of newborns require assistance to initiate breathing at birth, and around 5% need ventilation support. Fetal heart rate (FHR) monitoring plays a crucial role in assessing fetal well-being during prenatal care, enabling the detection of abnormal patterns and supporting timely obstetric interventions to mitigate fetal risks during labor. Applying artificial intelligence (AI) methods to analyze large datasets of continuous FHR monitoring episodes with diverse outcomes may offer novel insights into predicting the risk of needing breathing assistance or interventions. Recent advances in wearable FHR monitors have enabled continuous fetal monitoring without compromising maternal mobility. However, sensor displacement during ma
קרא במקור המקורי
arxiv.org
פתח כתבה מקורית