IISE Transactions on Healthcare Systems Engineering最新文献_第6页

Minimally Trained Analysts Can Perform Fast, Objective Assessment of Orthopedic Technical Skill from Fluoroscopic Images. 训练有素的分析师可通过透视图像对骨科技术技能进行快速客观的评估。

IISE Transactions on Healthcare Systems Engineering Pub Date : 2022-01-01 Epub Date: 2022-02-28 DOI: 10.1080/24725579.2022.2035022

Dominik D Mattioli, Geb W Thomas, Steven A Long, Marcus Tatum, Donald D Anderson

{"title":"Minimally Trained Analysts Can Perform Fast, Objective Assessment of Orthopedic Technical Skill from Fluoroscopic Images.","authors":"Dominik D Mattioli, Geb W Thomas, Steven A Long, Marcus Tatum, Donald D Anderson","doi":"10.1080/24725579.2022.2035022","DOIUrl":"10.1080/24725579.2022.2035022","url":null,"abstract":"<p><p>Skill assessment in orthopedics has traditionally relied on subjective impressions from a supervising surgeon. The feedback derived from these tools may be limited by bias and other practical issues. Objective analysis of intraoperative fluoroscopic images offers an inexpensive, repeatable, and precise assessment strategy without bias. Assessors generally refrain from using the scores of images obtained throughout the operation to evaluate skill for practical reasons. A new system was designed to facilitate rapid analysis of this fluoroscopy via minimally trained analysts. Four expert and four novice analysts independently measured one objective metric for skill using both a custom analysis software and a commercial alternative. Analysts were able to measure the objective metric three times faster when using the custom software, and without a practical difference in accuracy in comparison to the expert analysts using the commercial software. These results suggest that a well-designed fluoroscopy analysis system can facilitate inexpensive, reliable, and objective assessment of surgical skills.</p>","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"12 3","pages":"212-220"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9488091/pdf/nihms-1832409.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10797323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic atrial fibrillation detection from short ECG signals: A hybrid deep learning approach 从短ECG信号中自动检测心房颤动：一种混合深度学习方法

IISE Transactions on Healthcare Systems Engineering Pub Date : 2021-11-09 DOI: 10.1080/24725579.2021.1919249

Xiaodan Wu, Z. Sui, Chao-Hsien Chu, Guanjie Huang

{"title":"Automatic atrial fibrillation detection from short ECG signals: A hybrid deep learning approach","authors":"Xiaodan Wu, Z. Sui, Chao-Hsien Chu, Guanjie Huang","doi":"10.1080/24725579.2021.1919249","DOIUrl":"https://doi.org/10.1080/24725579.2021.1919249","url":null,"abstract":"Abstract Atrial fibrillation (AF) is one of the most common arrhythmic complications. Recently, researchers have attempted to use deep learning models, such as convolution neural networks (CNN) and/or Long Short-Term Memory (LSTM) neural networks to alleviate the tedious and time-consuming feature extraction process and achieve good classification results. In this paper we propose a hybrid CNN-LSTM model and use the short ECG signal from the PhysioNet/CinC Challenges 2017 dataset to explore and evaluate the relative performance of four data mining algorithms and three deep learning architectures. The original ECG signal, clinical diagnostic features and 169 features based on time domain, frequency domain and non-linear heart rate variability indicators were used for comparative experiments. The results show that with proper design and tuning, the Hybrid CNN-LSTM model performed much better than other benchmarked algorithms. It achieves 97.42% accuracy, 95.65% sensitivity, 97.14% specificity, 0.99 AUC (Area under the ROC curve) value and 0.98 F1 score. In general, with proper design and configuration, deep learning can be effective for automatic AF detection while data mining methods require domain knowledge and an extensive feature extraction and selection process to get satisfactory results. However, most machine learning algorithms, including deep learning models, perform the task as a black box, making it almost impossible to determine what features in the signal are critical to the analysis.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"12 1","pages":"1 - 19"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46086205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A novel sparse model-based algorithm to cluster categorical data for improved health screening and public health promotion 一种新的基于稀疏模型的分类数据聚类算法，用于改善健康筛查和公共健康促进

IISE Transactions on Healthcare Systems Engineering Pub Date : 2021-10-04 DOI: 10.1080/24725579.2021.1980467

Lan Jiang, Yu Ding, Melissa A. Sutherland, M. K. Hutchinson, Chuheng Zhang, Bing Si

{"title":"A novel sparse model-based algorithm to cluster categorical data for improved health screening and public health promotion","authors":"Lan Jiang, Yu Ding, Melissa A. Sutherland, M. K. Hutchinson, Chuheng Zhang, Bing Si","doi":"10.1080/24725579.2021.1980467","DOIUrl":"https://doi.org/10.1080/24725579.2021.1980467","url":null,"abstract":"Abstract Screening for interpersonal violence is critical to mitigate the consequences of violence and improve women’s health. Current guidelines recommend that health care providers screen all women for experiences of violence. Despite these recommendations, studies have noted a large variation in provider-reported interpersonal violence screening rates ranging from 10% to 90%. Given the disparity in screening rates, identifying variables correlated with providers’ screening practices is an important contribution. A survey of healthcare providers previously collected was utilized for this analysis and consisted of the providers’ socio-demographics, attitudes and beliefs, practice environment characteristics as well as self-reported screening practices. The objective of the study was to stratify healthcare providers into relatively homogeneous clusters based on mixed types of categorical nominal and ordinal variables and correlate the identified clusters with the violence screening rates. This paper proposes a sparse categorical Factor Mixture Model (sc-FMM) to cluster a large number of categorical variables, in which an norm was used for variable selection. An Expectation Maximization framework integrated with Gauss-Hermite approximation was developed for model estimation. Simulation studies show significantly better performance of sc-FMM than competing methods. sc-FMM was applied to identify clusters/subgroups of healthcare providers. The identified clusters were further correlated with interpersonal violence screening rates. The findings reveal how the providers’ screening rate for interpersonal violence are associated with multi-source impacting factors which inform the formation of policy and intervention development to promote the uptake of routine screening for interpersonal violence in women.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"12 1","pages":"137 - 149"},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60128539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The impact of prescriber experience and continuity on adverse drug reactions in hospitalized cancer patients 处方经验和连续性对住院癌症患者药物不良反应的影响

IISE Transactions on Healthcare Systems Engineering Pub Date : 2021-09-24 DOI: 10.1080/24725579.2021.1955777

Yue Tang, Jingui Xie, Aizong Shen, Lin-Lin Liu, Fei Zhai, Changfang Fu

引用次数: 0

A novel self-adaptive convolutional neural network model using spatial pyramid pooling for 3D lung nodule computer-aided diagnosis 一种新的基于空间金字塔池的自适应卷积神经网络模型用于三维肺结节计算机辅助诊断

IISE Transactions on Healthcare Systems Engineering Pub Date : 2021-09-23 DOI: 10.1080/24725579.2021.1953638

Qianqian Zhang, Sangwon Yoon

{"title":"A novel self-adaptive convolutional neural network model using spatial pyramid pooling for 3D lung nodule computer-aided diagnosis","authors":"Qianqian Zhang, Sangwon Yoon","doi":"10.1080/24725579.2021.1953638","DOIUrl":"https://doi.org/10.1080/24725579.2021.1953638","url":null,"abstract":"Abstract This research proposes a novel self-adaptive convolutional neural network (Adap-Net) model for lung nodule diagnosis on 3D computed tomography (CT) images. Lung cancer is one of the most common cancers with a high mortality rate. Therefore, there is an urgent need to diagnose lung nodules to improve the survival rate, which is challenging because of the nodule heterogeneity and the lack of annotated lung nodule images. Prevailing research for lung nodule diagnosis usually ignores the nodule heterogeneity problem and enlarges the model complexity that degrades the lung nodule diagnosis performance given limited annotated training samples. To overcome the challenges, a transverse layer pooling (TLP) algorithm is proposed and the spatial pyramid pooling (SPP) scheme is integrated, which makes it possible to adaptively extract equal-dimensional feature representations from arbitrary-sized 3D lung nodule images. Meanwhile, the TLP algorithm introduces a layer compression architecture that dramatically reduces the model complexity. Moreover, K-means clustering is adopted to assign appropriate input image sizes for each lung nodule, allowing the mini-batch-based model training. The proposed Adap-Net is comprehensively evaluated and compared to other deep learning (DL) models using 3D CT images from a public dataset. Experimental results show that the proposed Adap-Net model improves the lung nodule diagnosis accuracy up to 12.12% with less than 10% of parameters that are involved in other DL models. In practice, the proposed Adap-Net model can offer complementary opinions in computer-aided diagnosis (CAD) systems as a supportive tool for radiologists and physicians in the medical image interpretation, analysis, and diagnosis process.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"12 1","pages":"75 - 88"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43131044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Deep spatio-temporal sparse decomposition for trend prediction and anomaly detection in cardiac electrical conduction 用于心脏电导趋势预测和异常检测的深度时空稀疏分解

IISE Transactions on Healthcare Systems Engineering Pub Date : 2021-09-20 DOI: 10.1080/24725579.2021.1982081

Xinyu Zhao, Hao Yan, Zhiyong Hu, D. Du

{"title":"Deep spatio-temporal sparse decomposition for trend prediction and anomaly detection in cardiac electrical conduction","authors":"Xinyu Zhao, Hao Yan, Zhiyong Hu, D. Du","doi":"10.1080/24725579.2021.1982081","DOIUrl":"https://doi.org/10.1080/24725579.2021.1982081","url":null,"abstract":"Abstract Electrical conduction among cardiac tissue is commonly modeled with partial differential equations, i.e., reaction-diffusion equation, where the reaction term describes cellular stimulation and diffusion term describes electrical propagation. Detecting and identifying of cardiac cells that produce abnormal electrical impulses in such nonlinear dynamic systems are important for efficient treatment and planning. To model the nonlinear dynamics, simulation has been widely used in both cardiac research and clinical study to investigate cardiac disease mechanisms and develop new treatment designs. However, existing cardiac models have a great level of complexity, and the simulation is often time-consuming. We propose a deep spatio-temporal sparse decomposition (DSTSD) approach to bypass the time-consuming cardiac partial differential equations with the deep spatio-temporal model and detect the time and location of the anomaly (i.e., malfunctioning cardiac cells). This approach is validated from the data set generated from the Courtemanche-Ramirez-Nattel (CRN) model, which is widely used to model the propagation of the transmembrane potential across the cross neuron membrane. The proposed DSTSD achieved the best accuracy in terms of spatio-temporal mean trend prediction and anomaly detection.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"12 1","pages":"150 - 164"},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49275603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Patient-centric surgeons’ case mix problem 以病人为中心的外科医生的病例组合问题

IISE Transactions on Healthcare Systems Engineering Pub Date : 2021-09-01 DOI: 10.1080/24725579.2021.1965676

Renato de Matta

引用次数: 0

A modified SIR model equivalent to a generalized logistic model, with standard logistic or log-logistic approximations 等效于广义逻辑模型的修正SIR模型，具有标准逻辑或对数逻辑近似

IISE Transactions on Healthcare Systems Engineering Pub Date : 2021-09-01 DOI: 10.1080/24725579.2021.1968547

D. E. Clark, G. Welch, J. Peck

引用次数: 1

What patients and care partners want in a wearable dialysis device: a mixed-methods study 患者和护理伙伴对可穿戴透析设备的需求：一项混合方法研究

IISE Transactions on Healthcare Systems Engineering Pub Date : 2021-08-06 DOI: 10.1080/24725579.2021.1958273

Auður Anna Jónsdóttir, L. Kessler, S. Rim, Ji-Eun Kim

{"title":"What patients and care partners want in a wearable dialysis device: a mixed-methods study","authors":"Auður Anna Jónsdóttir, L. Kessler, S. Rim, Ji-Eun Kim","doi":"10.1080/24725579.2021.1958273","DOIUrl":"https://doi.org/10.1080/24725579.2021.1958273","url":null,"abstract":"Abstract Few studies have explored the similarities and differences between patients’ and care partners’ perspectives in terms of their ideal dialysis procedure and desired outcomes. As part of a project to improve the quality of life of patients with end-stage renal disease, we elicited unbiased feedback from both patients and care partners regarding the design of a wearable dialysis device tailored to meet users’ clinical needs. We interviewed 24 hemodialysis patients and 12 hemodialysis care partners using a mixed-methods approach of open-ended and rank-order questions. Inductive content analysis showed that both patients and care partners preferred a wearable dialysis device that a patient could carry on their upper body, particularly on their back or shoulder, or wear as a vest. Analysis of responses to the rank-order questions showed a significant preference for a vest design. Operational simplicity and compactness were the attributes most frequently mentioned in response to the open-ended questions, while the accuracy of the device was ranked as significantly more important than ease of attachment, comfort, simplicity, size, or invisibility in response to the rank-order questions. The findings from this study will help to ensure that new wearable dialysis devices are designed in accordance with patients’ and care partners’ preferences.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"12 1","pages":"101 - 110"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46643942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Multidisciplinary efforts in combating nonadherence to medication and health care interventions: Opportunities and challenges for operations researchers 多学科努力在打击不遵守药物和卫生保健干预:机遇和挑战的业务研究人员

IISE Transactions on Healthcare Systems Engineering Pub Date : 2021-07-14 DOI: 10.1080/24725579.2021.1953639

A. Prakash, C. Vega, V. Diaby, Xiang Zhong

{"title":"Multidisciplinary efforts in combating nonadherence to medication and health care interventions: Opportunities and challenges for operations researchers","authors":"A. Prakash, C. Vega, V. Diaby, Xiang Zhong","doi":"10.1080/24725579.2021.1953639","DOIUrl":"https://doi.org/10.1080/24725579.2021.1953639","url":null,"abstract":"Abstract Chronic diseases are a growing concern in public health. Lack of adherence to medications and health care interventions is a major factor behind the worsening of chronic disease outcomes and large medical costs. As a result, there is a critical need to identify structural, systematic, and patient-centric barriers to adherence. Similarly important is the development of models and deployable strategies to ensure effective delivery of care so that clinical and economic outcomes are optimized. This commentary review explores the research on nonadherence of patients with chronic diseases and other non-acute conditions to medications and interventions from different disciplinary perspectives, including medicine/pharmacy, pharmacoeconomics, behavioral economics, operations research (OR), and data analytics (DA). Although a spate of works has been conducted in the medicine and pharmacy fields, the contribution of OR/DA to medication/intervention nonadherence is limited. Given the technological advancement and the increasing understanding of nonadherence, we believe that OR/DA can play an essential role in encapsulating the domain knowledge and catalyzing the development of patient-centric solutions. We discuss the challenges and opportunities presented to OR/DA researchers and conclude the commentary with a discussion on new interdisciplinary research directions that can contribute to solving the nonadherence puzzle.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"11 1","pages":"255 - 270"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42647310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0