ArticleNPJ digital medicine2021
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Integrating phenotypic information of obstructive sleep apnea and deep representation of sleep-event sequences for cardiovascular risk prediction.BMC medical informatics and decision making · 2026Article
- Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- Gated recurrent unit with decay has real-time capability for postoperative ileus surveillance and offers cross-hospital transferability.Communications medicine · 2025Article
- Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.Journal of medical Internet research · 2024Article
- [Research progress on electronic health records multimodal data fusion based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Three-Stage Framework for Accurate Pediatric Chest X-ray Diagnosis Using Self-Supervision and Transfer Learning on Small Datasets.Diagnostics (Basel, Switzerland) · 2024Article
- Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D's Real-Time Capabilities and Cross-Hospital Transferability.medRxiv : the preprint server for health sciences · 2024Article
- Interpreting Stroke-Impaired Electromyography Patterns through Explainable Artificial Intelligence.Sensors (Basel, Switzerland) · 2024Article
- Predicting Hypoxia Using Machine Learning: Systematic Review.JMIR medical informatics · 2024Review
- Pole balancing on the fingertip: model-motivated machine learning forecasting of falls.Frontiers in physiology · 2024Article
- Internet of Things and New Technologies for Tracking Perioperative Patients With an Innovative Model for Operating Room Scheduling: Protocol for a Development and Feasibility Study.JMIR research protocols · 2023Article
- Article
- Predicting patient decompensation from continuous physiologic monitoring in the emergency department.NPJ digital medicine · 2023Article
- COVID-19 detection based on self-supervised transfer learning using chest X-ray images.International journal of computer assisted radiology and surgery · 2023Article
- Prediction of Acid-Base and Potassium Imbalances in Intensive Care Patients Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Transferability and interpretability of the sepsis prediction models in the intensive care unit.BMC medical informatics and decision making · 2022Article
- Artificial intelligence and anesthesia: a narrative review.Annals of translational medicine · 2022Review
- Breaking away from labels: The promise of self-supervised machine learning in intelligent health.Patterns (New York, N.Y.) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Hundreds of millions of surgical procedures take place annually across the world, which generate a prevalent type of electronic health record (EHR) data comprising time series physiological signals. Here, we present a transferable embedding method (i.e., a method to transform time series signals into input features for predictive machine learning models) named PHASE (PHysiologicAl Signal Embeddings) that enables us to more accurately forecast adverse surgical outcomes based on physiological signals. We evaluate PHASE on minute-by-minute EHR data of more than 50,000 surgeries from two operating room (OR) datasets and patient stays in an intensive care unit (ICU) dataset. PHASE outperforms other state-of-the-art approaches, such as long-short term memory networks trained on raw data and gradient boosted trees trained on handcrafted features, in predicting six distinct outcomes: hypoxemia, hypocapnia, hypotension, hypertension, phenylephrine, and epinephrine. In a transfer learning setting where we train embedding models in one dataset then embed signals and predict adverse events in unseen data, PHASE achieves significantly higher prediction accuracy at lower computational cost compared to conventional approaches. Finally, given the importance of understanding models in clinical applications we demonstrate that PHASE is explainable and validate our predictive models using local feature attribution methods.
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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.