Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits.
Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits.
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DOI：
10.1007/978-3-030-68790-8_7
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Shah N
中科院分区：
文献类型：
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作者：
Padhee S;Alambo A;Banerjee T;Subramaniam A;Abrams DM;Nave GK Jr;Shah N
镰状细胞病（SCD）的疼痛通常与发病率，死亡率和高医疗费用增加有关。长期以来，预测疼痛的存在、存在和强度的标准方法一直是自我报告。然而，医疗提供者努力根据主观疼痛报告正确地管理患者，并且止痛药通常导致患者沟通的进一步困难，因为它们可能导致镇静和嗜睡。最近的研究表明，客观的生理指标可以使用机器学习（ML）技术预测住院患者的主观自我报告疼痛评分。在这项研究中，我们评估了ML技术在三种类型的医院就诊（即，住院病人、门诊病人和门诊病人评价）。我们比较了五种分类算法在个体内（每个患者内）和个体间（患者间）水平的各种疼痛强度水平。虽然所有测试的分类器都比随机分类器表现得好得多，但决策树（DT）模型在预测11点严重程度量表（从0-10）上的疼痛方面表现最好，在个体间水平上的准确度为0.728，在个体内水平上为0.653。DT的准确性在2点评级量表上显著提高到0.941（即，无/轻度疼痛：0-5，重度疼痛：6-10）。我们的实验结果表明，ML技术可以为所有三种类型的医院就诊提供客观和定量的疼痛强度水平评估。
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 inter-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.