使用生命体征评估镰状红细胞病患者疼痛强度
Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits
AI总结：
利用机器学习技术通过生命体征评估镰状红细胞病患者疼痛强度，提高疼痛评估的客观性和准确性。
摘要：
镰状红细胞病（SCD）的疼痛常与发病率、死亡率和高医疗成本相关。预测疼痛的缺席、存在和强度的标准方法长期以来一直是自我报告。然而，医疗人员难以根据主观疼痛报告正确管理患者，而镇痛药物常导致患者沟通困难，因为它们可能引起嗜睡和困倦。最近的研究表明，使用机器学习（ML）技术可以利用客观生理指标预测住院期间的主观自报疼痛评分。在本研究中，我们评估了ML技术在数据收集来自50名患者、跨三种医院访问类型（即住院、门诊和门诊评估）的延长时期内的泛化能力。我们比较了五种分类算法在不同疼痛强度水平上的表现，包括个体内部（每个患者内）和个体间（患者之间）层面。尽管所有测试的分类器都比随机表现更好，但决策树（DT）模型在11点严重程度量表（0-10）上预测疼痛的准确率为0.728（个体间层面）和0.653（个体内部层面）。DT在2点评分量表（即无/轻度疼痛：0-5，重度疼痛：6-10）上的准确率显著提高到0.941（个体内部层面）。我们的实验结果表明，ML技术可以为所有三种医院访问类型提供客观和定量的疼痛强度评估。
英文摘要：
Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from 50 patients over an extended period across three types of hospital visits (i.e., inpatient, outpatient and outpatient evaluation). We compare five classification algorithms for various pain intensity levels at both intra-individual (within each patient) and inter-individual (between patients) level. While all the tested classifiers perform much better than chance, a Decision Tree (DT) model performs best at predicting pain on an 11-point severity scale (from 0-10) with an accuracy of 0.728 at an inter-individual level and 0.653 at an intra-individual level. The accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e., no/mild pain: 0-5, severe pain: 6-10) at an intra-individual level. Our experimental results demonstrate that ML techniques can provide an objective and quantitative evaluation of pain intensity levels for all three types of hospital visits.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
