ArticleSensors (Basel, Switzerland)2020
Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis.Journal of translational medicine · 2024Pooled it
- Machine learning for predicting acute hypotension: A systematic review.Frontiers in cardiovascular medicine · 2022Pooled it
- Effect of propofol combined with remimazolam besylate on blood pressure during general anesthesia induction in patients undergoing gynecological laparoscopic surgery: single-centre randomized controlled trial.BMC anesthesiology · 2025Trial
- Neural Network-Based Adaptive Resource Allocation for 5G Heterogeneous Ultra-Dense Networks.Sensors (Basel, Switzerland) · 2025Article
- Prediction of postoperative nausea and vomiting in patients undergoing sedated gastrointestinal endoscopy based on machine learning.Annals of medicine · 2025Article
- The Role of Artificial Intelligence in Anesthesia Monitoring and Surveillance.Anesthesiology clinics · 2025Review
- Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.Bioengineering (Basel, Switzerland) · 2025Article
- Impact of societal change on anesthesia practice in Japan.Journal of anesthesia · 2025Article
- Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management.BMC medical informatics and decision making · 2025Article
- Association of Preoperative Frailty and Post-Induction Hypotension in Elderly Patients Undergoing Major Non-Cardiac Surgery: A Prospective Observational Cohort Study.Clinical interventions in aging · 2025Observational
- Artificial intelligence in anesthesiology: a bibliometric analysis.Perioperative medicine (London, England) · 2024Review
- Article
- Enhancing Hypotension Prediction in Real-time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2024Article
- Efficacy and Safety of Remimazolam Versus Etomidate for Induction of General Anesthesia: Protocol for a Systematic Review and Meta-Analysis.JMIR research protocols · 2024Article
- A review of the current status and progress in difficult airway assessment research.European journal of medical research · 2024Review
- American society of anesthesiologists physical status classification significantly affects the performances of machine learning models in intraoperative hypotension inference.Journal of clinical anesthesia · 2024Observational
- Carotid blood flow changes following a simulated end-inspiratory occlusion maneuver measured by ultrasound can predict hypotension after the induction of general anesthesia: an observational study.BMC anesthesiology · 2024Observational
- Article
- Applications of deep learning models in precision prediction of survival rates for heart failure patients.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.
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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.