Primer on machine learning: utilization of large data set analyses to individualize pain management
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Summary
In the coming years, machine learning is likely to become a key component of evidence-based medicine, yet will require additional skills and perspectives for its successful and ethical use in research and clinical settings.
- Type
- review
- Published
- 2019-10-01
- Cited by
- 16
- References
- 57
- Access
- Open access
- OpenAlex
- https://openalex.org/W2967501694
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:199550599
Keywords
Medicine, Machine learning, Artificial intelligence, Key (lock), Aside
References
- Machine learning in pain research
- Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
- Use of machine learning theory to predict the need for femoral nerve block following ACL repair.
- Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation
- Prediction Modeling Using EHR Data: Challenges, Strategies, and a Comparison of Machine Learning Approaches
- Comparison of machine classification algorithms for fibromyalgia: Neuroimages versus self-report
- Machine assessment of neonatal facial expressions of acute pain
- Modeling word perception using the Elman network
- Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network.
- Automated Assessment of Children’s Postoperative Pain Using Computer Vision
- Reinforcement Learning: An Introduction
- Use of Machine-learning Classifiers to Predict Requests for Preoperative Acute Pain Service Consultation
- Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain
- Human-level control through deep reinforcement learning
- A non-invasive test for the pre-cancerous breast.
- Representation Learning: A Review and New Perspectives
- Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging
- Rethinking the Inception Architecture for Computer Vision
- Visualizing Data using t-SNE
- Painful Issues in Pain Prediction.
Cited by
- Clinical Information Systems – Seen through the Ethics Lens
- “Precision Medicine in Anesthesiology”
- Artificial intelligence image assisted knee ligament trauma repair efficacy analysis and postoperative femoral nerve block analgesia effect research.
- Revealing the Neural Mechanism Underlying the Effects of Acupuncture on Migraine: A Systematic Review
- Ethics and Facial Recognition Technology: An Integrative Review
- Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review
- The Perioperative Human Digital Twin
- The Analysis of Pain Research through the Lens of Artificial Intelligence and Machine Learning.
- Artificial intelligence and anesthesia: a narrative review
- Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review
- Research trends from 1992 to 2022 of acupuncture anesthesia: a bibliometric analysis
- Signatures of chronic pain in multiple sclerosis: a machine learning approach to investigate trigeminal neuralgia
- Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis
- Postoperative Sore Throat After Tracheal Intubation: An Updated Narrative Review and Call for Action
- Applications of Big Data in Perioperative Outcomes Research and Evidence-Based Clinical Practice.
- Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications
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