ArticleJournal of clinical medicine2022
Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.Journal of pain and symptom management · 2024Pooled it
- Feasibility Assessment of Telehealth-Based Cancer Pain Management Through Machine Learning: A Prospective Clinical Study.Pain research & management · 2026Article
- The complex task of modelling artificial intelligence workflows for forecasting postoperative risk.Journal of anesthesia, analgesia and critical care · 2025Article
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.Perioperative medicine (London, England) · 2025Review
- Pain Management in Cancer Patients With Artificial Intelligence: Narrative Review.Scientifica · 2025Review
- Envisioning gamification in anesthesia, pain management, and critical care: basic principles, integration of artificial intelligence, and simulation strategies.Journal of anesthesia, analgesia and critical care · 2023Review
- Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management.Journal of anesthesia, analgesia and critical care · 2023Article
- Investigating Functional Impairment in Chronic Low Back Pain: A Qualitative Study from the Patients and Specialists' Perspectives.Journal of personalized medicine · 2023Article
- The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN's Challenge.Healthcare (Basel, Switzerland) · 2023Article
- Fentanyl in cancer pain management: avoiding hasty judgments and discerning its potential benefits.Drugs in context · 2023Review
- Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives.Pain research & management · 2023Review
- High-rate breakthrough cancer pain and tumour characteristics - literature review and case series.Drugs in context · 2023Review
- Multidisciplinary approach, continuous care and opioid management in cancer pain: case series and review of the literature.Drugs in context · 2023Article
- Editorial for the Special Issue: "Advances in Postoperative Pain Management and Chronic Postoperative Pain".Journal of clinical medicine · 2022Article
- Relationship between breakthrough cancer pain, background cancer pain and analgesic treatment - case series and review of the literature.Drugs in context · 2022Review
- Implementation of a Hybrid Care Model for Telemedicine-based Cancer Pain Management at the Cancer Center of Naples, Italy: A Cohort Study.In vivo (Athens, Greece)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained from a single-center program of telemedicine-based cancer pain management. These models included random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN), and the LASSO−RIDGE algorithm. Thirteen demographic, social, clinical, and therapeutic variables were adopted to define the conditions that can affect the number of teleconsultations. After ML validation, the risk analysis for more than one remote consultation was assessed in target individuals. Results: The data from 158 patients were collected. In the training set, the accuracy was about 95% and 98% for ANN and RF, respectively. Nevertheless, the best accuracy on the test set was obtained with RF (70%). The ML-based simulations showed that young age (<55 years), lung cancer, and occurrence of breakthrough cancer pain help to predict the number of remote consultations. Elderly patients (>75 years) with bone metastases may require more telemedicine-based clinical evaluations. Conclusion: ML-based analyses may enable clinicians to identify the best model for predicting the need for more remote consultations. It could be useful for calibrating care interventions and resource allocation.
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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.