Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study.
Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study.
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DOI：
10.2196/36998
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发表时间：
2022-06-23
影响因子：
2.2
通讯作者：
Shah, Nirmish
中科院分区：
文献类型：
--
作者：
Padhee, Swati;Nave, Gary K., Jr.;Banerjee, Tanvi;Abrams, Daniel M.;Shah, Nirmish
镰状细胞病（SCD）是影响全世界数百万人的最常见的遗传性血液疾病。大多数SCD患者经历反复的、不可预测的剧烈疼痛发作。这些疼痛发作是SCD患者急诊科就诊的主要原因，可能持续数周。可以说，治疗SCD疼痛发作最具挑战性的方面是评估和解释患者的疼痛强度水平。本研究旨在利用从电子健康记录中收集的客观生理信号来学习主观疼痛轨迹的深度特征表征。这项研究使用了从杜克大学医学中心496名参与者连续5年收集的电子健康记录数据。每个记录包含6个生命体征和患者自我报告的疼痛评分，顺序范围从0（无疼痛）到10（严重和无法忍受的疼痛）。我们还提取了与用药相关的3个特征：用药类型、用药状态（给药或用药、漏药或停药或到期用药）和总用药剂量（mg/mL）。我们使用变分自编码器进行表征学习，并设计了机器学习分类算法来构建疼痛预测模型。我们使用准确性和混淆矩阵评估我们的结果，并将定性数据表示可视化。我们设计了一个分类模型，使用原始数据和深度表征学习来预测主观疼痛评分，2分、4分、6分和11分疼痛评分的平均准确率分别为82.8%、70.6%、49.3%和47.4%。我们观察到，在深度表征特征上训练的随机森林分类模型在所有疼痛评分量表上都优于未表征数据上训练的模型。我们观察到，在不同的李克特量表上，我们的模型在提供药物数据和生命体征数据时表现更好。我们将数据表征可视化，以理解潜在表征，指出具有更高疼痛评分分辨率的相似疼痛评分的邻近表征。我们的研究结果表明，与仅使用生命体征的建模相比，药物信息（药物类型、药物总剂量、是否给药或漏药）可以显著改善主观疼痛预测模型。这项研究显示了数据驱动的估计疼痛评分的前景，除了患者自我报告的疼痛评分外，它还将帮助临床医生获得有关患者病情的额外信息。
Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient’s pain intensity level. This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records. This study used electronic health record data collected from 496 Duke University Medical Center participants over 5 consecutive years. Each record contained measures for 6 vital signs and the patient’s self-reported pain score, with an ordinal range from 0 (no pain) to 10 (severe and unbearable pain). We also extracted 3 features related to medication: medication type, medication status (given or applied, or missed or removed or due), and total medication dosage (mg/mL). We used variational autoencoders for representation learning and designed machine learning classification algorithms to build pain prediction models. We evaluated our results using an accuracy and confusion matrix and visualized the qualitative data representations. We designed a classification model using raw data and deep representational learning to predict subjective pain scores with average accuracies of 82.8%, 70.6%, 49.3%, and 47.4% for 2-point, 4-point, 6-point, and 11-point pain ratings, respectively. We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. We observed that at varying Likert scales, our models performed better when provided with medication data along with vital signs data. We visualized the data representations to understand the underlying latent representations, indicating neighboring representations for similar pain scores with a higher resolution of pain ratings. Our results demonstrate that medication information (the type of medication, total medication dosage, and whether the medication was given or missed) can significantly improve subjective pain prediction modeling compared with modeling with only vital signs. This study shows promise in data-driven estimated pain scores that will help clinicians with additional information about the patient’s condition, in addition to the patient’s self-reported pain scores.
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影响因子：
4.6
作者：
Knowlton SM;Sencan I;Aytar Y;Khoory J;Heeney MM;Ghiran IC;Tasoglu S
通讯作者：
Tasoglu S
影响因子：
--
作者：
Milton, Jacqueline N.;Gordeuk, Victor R.;Sebastiani, Paola
通讯作者：
Sebastiani, Paola
影响因子：
8
作者：
Nazabal, Alfredo;Olmos, Pablo M.;Valera, Isabel
通讯作者：
Valera, Isabel
影响因子：
4.6
作者：
Miotto R;Li L;Kidd BA;Dudley JT
通讯作者：
Dudley JT
DOI：
10.1109/embc.2016.7591352
发表时间：
2016-08
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Nickerson P;Tighe P;Shickel B;Rashidi P
通讯作者：
Rashidi P