ArticlePain medicine (Malden, Mass.)2015
Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain.
Article in Pain medicine (Malden, Mass.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Stable Longitudinal Screening of Latent Physiological Dysregulation from Psychometric Data Using Machine Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Preoperative Beta and Gamma EEG Spectral Power as Independent Predictors of Acute Postoperative Pain in Spinal Surgery: A Prospective Observational Cohort Study.Journal of pain research · 2026Article
- Predicting acute postsurgical pain in the postanesthesia care unit: risk tool development and internal validation.Pain reports · 2025Article
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- A DEEP NEURAL NETWORK TWO-PART MODEL AND FEATURE IMPORTANCE TEST FOR SEMI-CONTINUOUS DATA.The annals of applied statistics · 2025Article
- Machine learning approach to predict postoperative pain after spinal morphine administration during caesarean delivery.Heliyon · 2024Article
- Postoperative Pain at Discharge From the Post-anesthesia Care Unit: A Case-Control Study.Cureus · 2024Article
- Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.Applied clinical informatics · 2024Article
- Postoperative accurate pain assessment of children and artificial intelligence: A medical hypothesis and planned study.World journal of clinical cases · 2024Review
- How to Modulate Peripheral and Central Nervous System to Treat Acute Postoperative Pain and Prevent Pain Persistence.Current neuropharmacology · 2024Review
- [Application possibilities of digital tools in postoperative pain therapy].Schmerz (Berlin, Germany) · 2023Review
- Artificial intelligence algorithms predict the efficacy of analgesic cocktails prescribed after orthopedic surgery.PloS one · 2023Article
- Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study.JMIR formative research · 2022Article
- Fairness in the prediction of acute postoperative pain using machine learning models.Frontiers in digital health · 2022Article
- Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery.World journal of clinical cases · 2021Article
- Big Data in Studying Acute Pain and Regional Anesthesia.Journal of clinical medicine · 2021Review
- Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study.Journal of medical Internet research · 2021Observational
- Intersections of machine learning and epidemiological methods for health services research.International journal of epidemiology · 2021Article
- Profiling Patients by Intensity of Nursing Care: An Operative Approach Using Machine Learning.Journal of personalized medicine · 2020Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
backgroundGiven their ability to process highly dimensional datasets with hundreds of variables, machine learning algorithms may offer one solution to the vexing challenge of predicting postoperative pain.
methodsHere, we report on the application of machine learning algorithms to predict postoperative pain outcomes in a retrospective cohort of 8,071 surgical patients using 796 clinical variables. Five algorithms were compared in terms of their ability to forecast moderate to severe postoperative pain: Least Absolute Shrinkage and Selection Operator (LASSO), gradient-boosted decision tree, support vector machine, neural network, and k-nearest neighbor (k-NN), with logistic regression included for baseline comparison.
resultsIn forecasting moderate to severe postoperative pain for postoperative day (POD) 1, the LASSO algorithm, using all 796 variables, had the highest accuracy with an area under the receiver-operating curve (ROC) of 0.704. Next, the gradient-boosted decision tree had an ROC of 0.665 and the k-NN algorithm had an ROC of 0.643. For POD 3, the LASSO algorithm, using all variables, again had the highest accuracy, with an ROC of 0.727. Logistic regression had a lower ROC of 0.5 for predicting pain outcomes on POD 1 and 3.
conclusionsMachine learning algorithms, when combined with complex and heterogeneous data from electronic medical record systems, can forecast acute postoperative pain outcomes with accuracies similar to methods that rely only on variables specifically collected for pain outcome prediction.
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