IISE Transactions on Healthcare Systems Engineering最新文献_第3页

Exchange of health information among doctors using Electronic Health Records: Facilitators & Barriers from a socio-technical perspective 使用电子健康记录的医生之间的健康信息交换:从社会技术角度的促进因素和障碍

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-02-08 DOI: 10.1080/24725579.2023.2177780

Safa Elkefi, Onur Asan, Bradley H. Crotty

{"title":"Exchange of health information among doctors using Electronic Health Records: Facilitators & Barriers from a socio-technical perspective","authors":"Safa Elkefi, Onur Asan, Bradley H. Crotty","doi":"10.1080/24725579.2023.2177780","DOIUrl":"https://doi.org/10.1080/24725579.2023.2177780","url":null,"abstract":"Abstract Electronic health records (EHRs) and other computerized systems facilitate the timely exchange of patients’ health information between providers across different departments and organizations. This study investigates the types and trends of electronic information exchange between doctors and the factors that impact electronic information exchange. We used data from the National Electronic Health Records Survey. Logistic regression models were run to explore the predictors of doctors’ satisfaction with EHRs for information exchange. The models were adjusted to practice type and size and clinical specialty. A total of 1,524 physicians completed cross-sectional questionnaires. Low satisfaction with EHRs was correlated with the inability of providers to exchange information electronically and with difficulty in clinical care documentation. We found that doctors who perceived EHRs to improve their practices’ quality, efficiency, and care coordination were more satisfied with EHRs. They also preferred EHR systems that help them reduce errors (odds ratio (OR) = 2.69, p = 0.008) and prevent duplicate test orderings (OR = 2.33, p = 0.023). Furthermore, factors such as difficulty integrating information into the system, use problems, and time consumption negatively impacted doctors’ perception of EHR use. Doctors’ satisfaction with EHR use was associated with meaningful functionality, namely the ability to enhance care coordination, the ability to improve information availability and quality, work environment and logistic factors, and design characteristics. Improving the design of EHR systems is essential but may not be sufficient for successful and efficient electronic information exchange between doctors. Usability more broadly with data management including external data should be prioritized.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43475627","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

Predicting Colorectal Surgery Readmission Risk: a Surgery-Specific Predictive Model. 预测结直肠手术再入院风险：特定手术预测模型。

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-01-01 Epub Date: 2023-05-02 DOI: 10.1080/24725579.2023.2200210

Thomas Clark Howell, Stephanie Lumpkin, Nicole Chaumont

{"title":"Predicting Colorectal Surgery Readmission Risk: a Surgery-Specific Predictive Model.","authors":"Thomas Clark Howell, Stephanie Lumpkin, Nicole Chaumont","doi":"10.1080/24725579.2023.2200210","DOIUrl":"10.1080/24725579.2023.2200210","url":null,"abstract":"Most current predictive models for risk of readmission were primarily designed from non-surgical patients and often utilize administrative data alone. Models built upon comprehensive data sources specific to colorectal surgery may be key to implementing interventions aimed at reducing readmissions. This study aimed to develop a predictive model for risk of 30-day readmission specific to colorectal surgery patients including administrative, clinical, laboratory, and socioeconomic status (SES) data. Patients admitted to the colorectal surgery service who underwent surgery and were discharged from an academic tertiary hospital between 2017 and 2019 were included. A total of 1549 patients met eligibility criteria for this retrospective split-sample cohort study. The 30-day readmission rate of the cohort was 19.62%. A multivariable logistic regression was developed (C=0.70, 95% CI 0.61-0.73), which outperformed two internationally used readmission risk prediction indices (C=0.58, 95% CI 0.52-0.65) and (C=0.60, 95% CI 0.53-0.66). Tailored surgery-specific readmission models with comprehensive data sources outperform the most used readmission indices in predicting 30-day readmission in colorectal surgery patients. Model performance is improved by using more comprehensive datasets that include administrative and socioeconomic details about a patient, as well as clinical information used for decision-making around the time of discharge.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 3","pages":"175-181"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10426736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10039476","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

A Novel Sparse Linear Mixed Model for Multi-Source Mixed-Frequency Data Fusion in Telemedicine. 用于远程医疗中多源混频数据融合的新型稀疏线性混合模型

IISE Transactions on Healthcare Systems Engineering Pub Date : 2023-01-01 Epub Date: 2023-04-26 DOI: 10.1080/24725579.2023.2202877

Wesam Alramadeen, Yu Ding, Carlos Costa, Bing Si

{"title":"A Novel Sparse Linear Mixed Model for Multi-Source Mixed-Frequency Data Fusion in Telemedicine.","authors":"Wesam Alramadeen, Yu Ding, Carlos Costa, Bing Si","doi":"10.1080/24725579.2023.2202877","DOIUrl":"10.1080/24725579.2023.2202877","url":null,"abstract":"Digital health and telemonitoring have resulted in a wealth of information to be collected to monitor, manage, and improve human health. The multi-source mixed-frequency health data overwhelm the modeling capacity of existing statistical and machine learning models, due to many challenging properties. Although predictive analytics for big health data plays an important role in telemonitoring, there is a lack of rigorous prediction model that can automatically predicts patients' health conditions, e.g., Disease Severity Indicators (DSIs), from multi-source mixed-frequency data. Sleep disorder is a prevalent cardiac syndrome that is characterized by abnormal respiratory patterns during sleep. Although wearable devices are available to administrate sleep studies at home, the manual scoring process to generate the DSI remains a bottleneck in automated monitoring and diagnosis of sleep disorder. To address the multi-fold challenges for precise prediction of the DSI from high-dimensional multi-source mixed-frequency data in sleep disorder, we propose a sparse linear mixed model that combines the modified Cholesky decomposition with group lasso penalties to enable joint group selection of fixed effects and random effects. A novel Expectation Maximization (EM) algorithm integrated with an efficient Majorization Maximization (MM) algorithm is developed for model estimation of the proposed sparse linear mixed model with group variable selection. The proposed method was applied to the SHHS data for telemonitoring and diagnosis of sleep disorder and found that a few significant feature groups that are consistent with prior medical studies on sleep disorder. The proposed method also outperformed a few benchmark methods with the highest prediction accuracy.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 3","pages":"215-225"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10454975/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10174841","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

A Multi-attribute Utility Framework for Patients to Determine Childbirth Method Considering Uncertainties, Patient Preferences, Risk Attitudes, and Pregnancy Complications 考虑不确定性、患者偏好、风险态度和妊娠并发症的患者确定分娩方法的多属性效用框架

IISE Transactions on Healthcare Systems Engineering Pub Date : 2022-11-23 DOI: 10.1080/24725579.2022.2149637

Ridwan Al Aziz, Jun Zhuang

{"title":"A Multi-attribute Utility Framework for Patients to Determine Childbirth Method Considering Uncertainties, Patient Preferences, Risk Attitudes, and Pregnancy Complications","authors":"Ridwan Al Aziz, Jun Zhuang","doi":"10.1080/24725579.2022.2149637","DOIUrl":"https://doi.org/10.1080/24725579.2022.2149637","url":null,"abstract":"Abstract Despite increasing advocacy of shared decision-making (SDM) approaches to maternal healthcare and the significance of including patient preferences in choosing the childbirth method, quantitative research to help the patients to make informed decisions is still lacking. This paper proposes a multi-attribute decision framework to guide the patients toward the best childbirth method considering their preferences, risk attitudes, pregnancy complications, and uncertainties during the delivery process. Valuations of health condition, intrapartum pain, duration of the process, recovery time, and feeling of empowerment constitute the attribute set of our model. Four major pregnancy complications - malpresentation, anemia, eclampsia, and gestational diabetes have been considered to assess the patients’ pregnancy complication state. A deidentified dataset from the maternal delivery unit of the Dhaka Medical College (DMC) Hospital in Bangladesh, which was screened and validated by doctors from three different hospitals, was utilized to present a case study. For mothers with no complications, vaginal delivery (VG) is preferred regardless of the risk attitude. For mothers with single difficulty, as risk attitude changes from risk-averse to risk-seeking, the optimal strategy shifts from cesarean to VG. Cesarean delivery is preferred regardless of the risk attitude for mothers with more than one complication. Sensitivity analyses reveal that valuations of health conditions and intrapartum pain are the most sensitive attributes. The decisions obtained from the model are found consistent with the decision taken at DMC 85.25% times. This paper presents new insights to foster SDM and improve the childbirth experience.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42903575","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

Prediction of Nursing Burnout – a Scoping Review of the Literature from 1970 to 2021 护理倦怠的预测——1970-2021年文献综述

IISE Transactions on Healthcare Systems Engineering Pub Date : 2022-11-22 DOI: 10.1080/24725579.2022.2149638

Carolina Carvalho Manhães Leite, Abigail R. Wooldridge

{"title":"Prediction of Nursing Burnout – a Scoping Review of the Literature from 1970 to 2021","authors":"Carolina Carvalho Manhães Leite, Abigail R. Wooldridge","doi":"10.1080/24725579.2022.2149638","DOIUrl":"https://doi.org/10.1080/24725579.2022.2149638","url":null,"abstract":"Abstract Burnout is an occupational syndrome resulting from chronic workplace stress not appropriately managed. In nursing, burnout has been associated with adverse job characteristics (e.g., high responsibility for others, heavy workload, lack of infrastructure), with negative outcomes for the individual, the organization, and the recipients of care. The objective of this review is to describe the approaches used to predict burnout of practicing nurses to allow health care organizations to proactively address nursing burnout. We searched Scopus and PubMed for publications containing either in their title or abstract the terms “nurs*”, “burnout”, and “predict*” from 1970 to 2021. Our multi-phase screening process resulted in 312 papers. A gap in existing research relates to the primary method all studies but one used to capture data—questionnaires. Burnout is essentially a cumulative condition, and questionnaires identify the damage reactively, after burnout is experienced, by placing an additional demand on the individual, i.e., they further increase workload. Methods, ideally requiring minimal effort, to predict, not detect, burnout are needed so that individuals and organizations can take measures to prevent, reduce, and ultimately eliminate burnout among nurses and other clinicians.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48472780","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

Maximizing Hospital Capacity to Serve Pandemic Patient Surge in Hot Spots via Queueing Theory and Microsimulation 基于排队理论和微模拟的疫情热点地区医院容量最大化

IISE Transactions on Healthcare Systems Engineering Pub Date : 2022-11-18 DOI: 10.1080/24725579.2022.2149936

H. Ghayoomi, Elise Miller-Hooks, Mersedeh Tariverdi, J. Shortle, T. Kirsch

{"title":"Maximizing Hospital Capacity to Serve Pandemic Patient Surge in Hot Spots via Queueing Theory and Microsimulation","authors":"H. Ghayoomi, Elise Miller-Hooks, Mersedeh Tariverdi, J. Shortle, T. Kirsch","doi":"10.1080/24725579.2022.2149936","DOIUrl":"https://doi.org/10.1080/24725579.2022.2149936","url":null,"abstract":"This paper presents a mixed-integer mathematical program with embedded equations developed from concepts of queueing theory and Jackson networks for estimating the steady-state maximum potential hospital capacity for COVID-19 patient care in extreme surge conditions, where a hospital must turn nearly all of its existing resources toward the care of pandemic patients. Estimating the potential maximum hospital capacity for pandemic patient care can aid in assessing regional healthcare capacity during surges in pandemic patient demand, predicting shortfalls, and designing preparedness and response actions. To obtain such estimates and inform action, the program determines a best assignment of a heterogeneous staff of nurses and doctors to key units appropriate for their skills to create the optimal allocation of staffed beds. An alternative trial-and-error approach is offered that decision-makers without optimization or software expertise can use to obtain similar estimates. Under a given assignment of resources, a variety of key performance indicators can be obtained through the direct use of the queueing equations. Results of comparisons to outcomes from a detailed discrete event simulation model of an identical hospital design show the accuracy of the equations to be high despite the added simplifications needed for the use of a closed-form equation-based methodology. KEYWORDS: Micro-simulation;hospital capacity planning;COVID-19;pandemic;queueing theory;surge planning;KPIs. Copyright © 2022 \"IISE\".","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42826474","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

Prevention of Seasonal Influenza Outbreak via Healthcare Insurance 通过医疗保险预防季节性流感爆发

IISE Transactions on Healthcare Systems Engineering Pub Date : 2022-11-15 DOI: 10.1080/24725579.2022.2145393

Ting-Yu Ho, Z. Zabinsky, P. Fishman, Shan Liu

{"title":"Prevention of Seasonal Influenza Outbreak via Healthcare Insurance","authors":"Ting-Yu Ho, Z. Zabinsky, P. Fishman, Shan Liu","doi":"10.1080/24725579.2022.2145393","DOIUrl":"https://doi.org/10.1080/24725579.2022.2145393","url":null,"abstract":"Abstract The outbreak of seasonal flu costs billions of dollars in health care utilization and lost productivity. Despite the effectiveness of vaccination and antiviral medications to prevent serious flu-related complications and slow down the spread of an influenza epidemic, only 52% of the U.S. population aged 6 months and older received flu vaccines in the 2019-20 flu season. In addition, a costly out-of-pocket expense results in fewer patients seeking treatment, leading to potential hospitalizations and even flu-related deaths. In this study, we develop an integrated healthcare insurance mechanism that optimizes two incentive policies, vaccination reward and cost-sharing, to alleviate the medical cost and disease burden while preventing the outbreak of seasonal influenza. We model the dynamic interaction between a single insurer and multiple insureds as a Stackelberg vaccination game; we then embed the game into an agent-based simulation to model the spread of flu in a population under different policies. Finally, we apply machine learning and simulation optimization to optimize healthcare incentive policies in a large-scale flu transmission simulation. Simulation results indicate that the proposed methodology efficiently identifies a set of good incentive policies under different scenarios of flu vaccine efficacy and reproduction numbers.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44669611","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

Readiness for agility adaptability and alignment in healthcare organizations 为医疗保健组织的灵活性、适应性和一致性做好准备

IISE Transactions on Healthcare Systems Engineering Pub Date : 2022-11-08 DOI: 10.1080/24725579.2022.2144966

Albi Thomas, M. Suresh

{"title":"Readiness for agility adaptability and alignment in healthcare organizations","authors":"Albi Thomas, M. Suresh","doi":"10.1080/24725579.2022.2144966","DOIUrl":"https://doi.org/10.1080/24725579.2022.2144966","url":null,"abstract":"Abstract This paper aims to ‘identify,’ ‘analyze,’ and ‘categorize’ Agility, Adaptability, and Alignment (Triple-A) readiness factors in healthcare organizations using Total Interpretive Structural Modeling (TISM). This study identified nine triple-A readiness factors for healthcare organizations. The present research looked at the interrelationships between readiness variables for agility, adaptability, and alignment deployment in healthcare organizations. The findings would assist healthcare practitioners in implementing triple-A in hospitals to enhance service quality. TISM and MICMAC analysis is utilized to determine the importance of each of the elements, allowing organizations to prioritize the most important variables first, followed by the rest of the factors. The identified key factors are organizational leadership, flexible service design, advanced technology and innovativeness, strategy fits. This research will aid key stakeholders and academics in better understanding the readiness factors that influence agility, adaptability, and alignment in a healthcare organization. This study proposes the TISM technique for healthcare, which is a novel attempt in the subject of Triple-A in this sector. This research contributes to the Triple-A theory of knowledge, model conceptualizing, and organizational change.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 1","pages":"161 - 174"},"PeriodicalIF":0.0,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47903511","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

Effectiveness of smart wrist wearables for distinguishing physical and cognitive demands 智能手腕可穿戴设备区分身体和认知需求的有效性

IISE Transactions on Healthcare Systems Engineering Pub Date : 2022-11-01 DOI: 10.1080/24725579.2022.2142867

Jackie S. Cha, Fajar Ausri, L. Mudge, Denny Yu

{"title":"Effectiveness of smart wrist wearables for distinguishing physical and cognitive demands","authors":"Jackie S. Cha, Fajar Ausri, L. Mudge, Denny Yu","doi":"10.1080/24725579.2022.2142867","DOIUrl":"https://doi.org/10.1080/24725579.2022.2142867","url":null,"abstract":"Abstract Wrist-worn wearables, with sensors to measure physiological responses such as heart rate variability (HRV) and electrodermal activity (EDA), have been increasing and have the potential to be used for continuous monitoring. These devices have been used to detect responses in workers’ physical and cognitive demands; however, the accuracy of wrist wearables for distinguishing these demands is unknown, especially since many every day and work activities frequently require motion. The objective of this study was to evaluate the effectiveness of a wrist-worn wearable in measuring physiological changes during different demand conditions. Participants (n = 20 college students) completed a multi-factor laboratory study that considered task (cognitive/physical), difficulty (easy/hard), and motion (motion/no motion). N-back tasks and stationary bike tasks were used to represent cognitive and physical demands, respectively. Metrics of HRV and EDA were measured using reference-standard devices and a validated wrist-wearable. Significant differences between task, motion, and difficulty were observed from HRV measurements from the reference-standard and wrist-worn devices. Wrist wearables are sensitive to detecting workplace demands and may be used as an alternative to reference-standard sensors for continuous health and activity monitoring for worker health and wellness. Findings in this study can provide guidelines on task and conditions that affect the use and interpretation of wrist-worn devices for measuring cognitive and physical demands in healthcare systems. HIGHLIGHTS Applications for noninvasive, wrist-worn sensors can be used for continuous health and exercise monitoring HRV and EDA metrics obtained from wrist-worn device are sensitivity in detecting changes in task, difficulty, and motion HRV metric from wrist-worn device had agreement with reference-standard device Wrist-wearables has potential for ubiquitous health monitoring of individuals","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 1","pages":"150 - 160"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46766685","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

Robust coupled tensor decomposition and feature extraction for multimodal medical data 多模态医学数据的鲁棒耦合张量分解和特征提取

IISE Transactions on Healthcare Systems Engineering Pub Date : 2022-10-31 DOI: 10.1080/24725579.2022.2141929

Meng Zhao, Mostafa Reisi Gahrooei, N. Gaw

{"title":"Robust coupled tensor decomposition and feature extraction for multimodal medical data","authors":"Meng Zhao, Mostafa Reisi Gahrooei, N. Gaw","doi":"10.1080/24725579.2022.2141929","DOIUrl":"https://doi.org/10.1080/24725579.2022.2141929","url":null,"abstract":"Abstract High-dimensional and multimodal data to describe various aspects of a patient’s clinical condition have become increasingly abundant in the medical field across a variety of domains. For example, in neuroimaging applications, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be collected simultaneously (i.e., EEG-fMRI) to provide high spatial and temporal resolution of a patient’s brain function. Additionally, in telemonitoring applications, a smartphone can be used to record various aspects of a patient’s condition using its built-in microphone, accelerometer, touch screen, etc. Coupled CANDECOMP/PARAFAC decomposition (CCPD) is a powerful approach to simultaneously extract common structures and features from multiple tensors and can be applied to these high-dimensional, multi-modal data. However, the existing CCPD models are inadequate to handle outliers, which are highly present in both applications. For EEG-fMRI, outliers are common due to fluctuations in the electromagnetic field resulting from interference between the EEG electrodes and the fMRI machine. For telemonitoring, outliers can result from patients not properly following instructions while performing smartphone-guided exercises at home. This motivates us to propose a robust CCPD (RCCPD) method for robust feature extraction. The proposed method utilizes the Alternating Direction Method of Multipliers (ADMM) to minimize an objective function that simultaneously decomposes a pair of coupled tensors and isolates outliers. We compare the proposed RCCPD method with the classical CP decomposition, the coupled matrix-tensor/tensor-tensor factorization (CMTF/CTTF), and the tensor robust CP decomposition (TRCPD). Experiments on both synthetic and real-world data demonstrate that the proposed RCCPD effectively handles outliers and outperforms the benchmarks in terms of accuracy.","PeriodicalId":37744,"journal":{"name":"IISE Transactions on Healthcare Systems Engineering","volume":"13 1","pages":"117 - 131"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46467940","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