ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2016
Deep neural network architectures for forecasting analgesic response.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- From the Pain Matrix to Functional Networks: A Narrative Review of Chronic Pain Mechanisms Across Adult and Pediatric Populations with Emerging AI Perspectives.Brain sciences · 2026Review
- Using artificial intelligence to improve pain assessment and pain management: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study.JMIR formative research · 2022Article
- Preoperative prediction of postoperative urinary retention in lumbar surgery: a comparison of regression to multilayer neural network.Journal of neurosurgery. Spine · 2022Article
- Intelligent Health Care: Applications of Deep Learning in Computational Medicine.Frontiers in genetics · 2021Review
- Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study.JMIR mHealth and uHealth · 2019Article
- DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning.Scientific reports · 2019Article
- Predictive Modeling of the Total Joint Replacement Surgery Risk: a Deep Learning Based Approach with Claims Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis.IEEE journal of biomedical and health informatics · 2018Review
- Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia.Journal of medical systems · 2018Article
- Machine learning in pain research.Pain · 2018Review
- Article
- A narrative review of AI monitoring in postoperative pain management and functional rehabilitation for spinal cord injury.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Response to prescribed analgesic drugs varies between individuals, and choosing the right drug/dose often involves a lengthy, iterative process of trial and error. Furthermore, a significant portion of patients experience adverse events such as post-operative urinary retention (POUR) during inpatient management of acute postoperative pain. To better forecast analgesic responses, we compared conventional machine learning methods with modern neural network architectures to gauge their effectiveness at forecasting temporal patterns of postoperative pain and analgesic use, as well as predicting the risk of POUR. Our results indicate that simpler machine learning approaches might offer superior results; however, all of these techniques may play a promising role for developing smarter post-operative pain management strategies.
Indexed as
Identifiers
What OpenQuestion holds
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.