ArticleSmart health (Amsterdam, Netherlands)2018
Improving Pain Management in Patients with Sickle Cell Disease from Physiological Measures Using Machine Learning Techniques.
Article in Smart health (Amsterdam, Netherlands), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05253781 (Low Dose Aspirin for Preventing Intrauterine Growth Restriction and Preeclampsia in Sickle Cell Pregnancy), which is not on this map. Cited by 16 papers.
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.
Low Dose Aspirin for Preventing Intrauterine Growth Restriction and Preeclampsia in Sickle Cell Pregnancy (PIPSICKLE): a Randomized Controlled Trial
Who cites it
16 citing papers in PubMed.
- Sickle cell disease detection in low-resource conditions using transfer-learning and contrastive-learning coupled with XAI.Scientific reports · 2026Article
- Classifying social and physical pain from multimodal physiological signals using machine learning.Scientific reports · 2025Article
- Mathematical modeling of SCD: a literature review.Journal of sickle cell disease · 2025Review
- Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management.Current pain and headache reports · 2024Review
- Comparing multimodal physiological responses to social and physical pain in healthy participants.Frontiers in public health · 2024Article
- Transforming medicine: artificial intelligence integration in the peripheral nervous system.Frontiers in neurology · 2024Review
- A systematic review of neurophysiological sensing for the assessment of acute pain.NPJ digital medicine · 2023Review
- Multimodal physiological sensing for the assessment of acute pain.Frontiers in pain research (Lausanne, Switzerland) · 2023Article
- Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study.JMIR formative research · 2022Article
- Can subjective pain be inferred from objective physiological data? Evidence from patients with sickle cell disease.PLoS computational biology · 2021Article
- Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits.Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) · 2021Article
- Measuring Pain in Sickle Cell Disease using Clinical Text.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review.Sensors (Basel, Switzerland) · 2020Review
- 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
- Continuous Pain Assessment Using Ensemble Feature Selection from Wearable Sensor Data.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Pain management is a crucial part in Sickle Cell Disease treatment. Accurate pain assessment is the first stage in pain management. However, pain is a subjective response and hard to assess via objective approaches. In this paper, we proposed a system to map objective physiological measures to subjective self-reported pain scores using machine learning techniques. Using Multinomial Logistic Regression and data from 40 patients, we were able to predict patients' pain scores on an 11-point rating scale with an average accuracy of 0.578 at the intra-individual level, and an accuracy of 0.429 at the inter-individual level. With a condensed 4-point rating scale, the accuracy at the inter-individual level was further improved to 0.681. Overall, we presented a preliminary machine learning model that can predict pain scores in SCD patients with promising results. To our knowledge, such a system has not been proposed earlier within the SCD or pain domains by exploiting machine learning concepts within the clinical framework.
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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.