Pub Date : 2023-03-29DOI: 10.1109/JTEHM.2023.3280974
Vincenzo Catrambone;Gaetano Valenza
Objective: The central and autonomic nervous systems are deemed complex dynamic systems, wherein each system as a whole shows features that the individual system sub-components do not. They also continuously interact to maintain body homeostasis and appropriate react to endogenous and exogenous stimuli. Such interactions are comprehensively referred to functional brain–heart interplay (BHI). Nevertheless, it remains uncertain whether this interaction also exhibits complex characteristics, that is, whether the dynamics of the entire nervous system inherently demonstrate complex behavior, or if such complexity is solely a trait of the central and autonomic systems. Here, we performed complexity mapping of the BHI dynamics under mental and physical stress conditions. Methods and procedures: Electroencephalographic and heart rate variability series were obtained from 56 healthy individuals performing mental arithmetic or cold-pressure tasks, and physiological series were properly combined to derive directional BHI series, whose complexity was quantified through fuzzy entropy. Results: The experimental results showed that BHI complexity is mainly modulated in the efferent functional direction from the brain to the heart, and mainly targets vagal oscillations during mental stress and sympathovagal oscillations during physical stress. Conclusion: We conclude that the complexity of BHI mapping may provide insightful information on the dynamics of both central and autonomic activity, as well as on their continuous interaction. Clinical impact: This research enhances our comprehension of the reciprocal interactions between central and autonomic systems, potentially paving the way for more accurate diagnoses and targeted treatments of cardiovascular, neurological, and psychiatric disorders.
{"title":"Complex Brain–Heart Mapping in Mental and Physical Stress","authors":"Vincenzo Catrambone;Gaetano Valenza","doi":"10.1109/JTEHM.2023.3280974","DOIUrl":"10.1109/JTEHM.2023.3280974","url":null,"abstract":"Objective: The central and autonomic nervous systems are deemed complex dynamic systems, wherein each system as a whole shows features that the individual system sub-components do not. They also continuously interact to maintain body homeostasis and appropriate react to endogenous and exogenous stimuli. Such interactions are comprehensively referred to functional brain–heart interplay (BHI). Nevertheless, it remains uncertain whether this interaction also exhibits complex characteristics, that is, whether the dynamics of the entire nervous system inherently demonstrate complex behavior, or if such complexity is solely a trait of the central and autonomic systems. Here, we performed complexity mapping of the BHI dynamics under mental and physical stress conditions. Methods and procedures: Electroencephalographic and heart rate variability series were obtained from 56 healthy individuals performing mental arithmetic or cold-pressure tasks, and physiological series were properly combined to derive directional BHI series, whose complexity was quantified through fuzzy entropy. Results: The experimental results showed that BHI complexity is mainly modulated in the efferent functional direction from the brain to the heart, and mainly targets vagal oscillations during mental stress and sympathovagal oscillations during physical stress. Conclusion: We conclude that the complexity of BHI mapping may provide insightful information on the dynamics of both central and autonomic activity, as well as on their continuous interaction. Clinical impact: This research enhances our comprehension of the reciprocal interactions between central and autonomic systems, potentially paving the way for more accurate diagnoses and targeted treatments of cardiovascular, neurological, and psychiatric disorders.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"495-504"},"PeriodicalIF":3.4,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/58/a8/jtehm-catrambone-3280974.PMC10561752.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-29DOI: 10.1109/JTEHM.2023.3262841
Fu Zou;Yuanhua Liu;Zelyu Chen;Karl Zhanghao;Dayong Jin
The accuracy of image segmentation is critical for quantitative analysis. We report a lightweight network FRUNet based on the U-Net, which combines the advantages of Fourier channel attention (FCA Block) and Residual unit to improve the accuracy. FCA Block automatically assigns the weight of the learned frequency information to the spatial domain, paying more attention to the precise high-frequency information of diverse biomedical images. While FCA is widely used in image super-resolution with residual network backbones, its role in semantic segmentation is less explored. Here we study the combination of FCA and U-Net, the skip connection of which can fuse the encoder information with the decoder. Extensive experimental results of FRUNet on three public datasets show that the method outperforms other advanced medical image segmentation methods in terms of using fewer network parameters and improved accuracy. It excels in pathological Section segmentation of nuclei and glands.
{"title":"Fourier Channel Attention Powered Lightweight Network for Image Segmentation","authors":"Fu Zou;Yuanhua Liu;Zelyu Chen;Karl Zhanghao;Dayong Jin","doi":"10.1109/JTEHM.2023.3262841","DOIUrl":"10.1109/JTEHM.2023.3262841","url":null,"abstract":"The accuracy of image segmentation is critical for quantitative analysis. We report a lightweight network FRUNet based on the U-Net, which combines the advantages of Fourier channel attention (FCA Block) and Residual unit to improve the accuracy. FCA Block automatically assigns the weight of the learned frequency information to the spatial domain, paying more attention to the precise high-frequency information of diverse biomedical images. While FCA is widely used in image super-resolution with residual network backbones, its role in semantic segmentation is less explored. Here we study the combination of FCA and U-Net, the skip connection of which can fuse the encoder information with the decoder. Extensive experimental results of FRUNet on three public datasets show that the method outperforms other advanced medical image segmentation methods in terms of using fewer network parameters and improved accuracy. It excels in pathological Section segmentation of nuclei and glands.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"252-260"},"PeriodicalIF":3.4,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10086528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9421977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children. Methods: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices. Results: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective. Conclusions: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals. Clinical and Translational Impact Statement—This study presents Early Research on the adoption of a smart childhood obesity caring solution adopting a multidisciplinary approach; it involves researchers from biomedical engineering, medicine, computer science, ethics and education. The solution has the potential to decrease the obesity rates in children aiming to impact to get a better global health.
{"title":"Promoting Obesity Prevention and Healthy Habits in Childhood: The OCARIoT Experience","authors":"Leire Bastida;Gloria Cea;Ana Moya;Alba Gallego;Eugenio Gaeta;Sara Sillaurren;Paulo Barbosa;Sabrina Souto;Eujessika Rodrigues;Macarena Torrego-Ellacuría;Andreas Triantafyllidis;Anastasios Alexiadis;Konstantinos Votis;Dimitrios Tzovaras;Cleilton Rocha;Lucas Alves;Pedro Maló;Márcio Mateus;Fernando Ferreira;María Teresa Arredondo","doi":"10.1109/JTEHM.2023.3261899","DOIUrl":"10.1109/JTEHM.2023.3261899","url":null,"abstract":"Objective: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children. Methods: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices. Results: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective. Conclusions: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals. Clinical and Translational Impact Statement—This study presents Early Research on the adoption of a smart childhood obesity caring solution adopting a multidisciplinary approach; it involves researchers from biomedical engineering, medicine, computer science, ethics and education. The solution has the potential to decrease the obesity rates in children aiming to impact to get a better global health.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"261-270"},"PeriodicalIF":3.4,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10081348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9360298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-19DOI: 10.1109/JTEHM.2023.3277890
Michel Hackbarth;Jessica Koschate;Sandra Lau;Tania Zieschang
Background: Accidental falls are a major health issue in older people. One significant and potentially modifiable risk factor is reduced gait stability. Clinicians do not have sophisticated kinematic options to measure this risk factor with simple and affordable systems. Depth-imaging with AI-pose estimation can be used for gait analysis in young healthy adults. However, is it applicable for measuring gait in older adults at a risk of falling? Methods: In this methodological comparison 59 older adults with and without a history of falls walked on a treadmill while their gait pattern was recorded with multiple inertial measurement units and with an Azure Kinect depth-camera. Spatiotemporal gait parameters of both systems were compared for convergent validity and with a Bland-Altman plot. Results: Correlation between systems for stride length (r=.992, $text{p} < 0.001$ ) and stride time (r=0.914, $text{p} < 0.001$ ) was high. Bland-Altman plots revealed a moderate agreement in stride length (−0.74 ± 3.68 cm; [−7.96 cm to 6.47 cm]) and stride time (−3.7±54 ms; [−109 ms to 102 ms]). Conclusion: Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect cameras. Affordable and small depth-cameras agree with IMUs for gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact. Clinical Translation Statement— Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect. Affordable and small depth-cameras, developed for various purposes in research and industry, agree with IMUs in clinical gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy to assess function or monitor changes in gait is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact.
{"title":"Depth-Imaging for Gait Analysis on a Treadmill in Older Adults at Risk of Falling","authors":"Michel Hackbarth;Jessica Koschate;Sandra Lau;Tania Zieschang","doi":"10.1109/JTEHM.2023.3277890","DOIUrl":"10.1109/JTEHM.2023.3277890","url":null,"abstract":"Background: Accidental falls are a major health issue in older people. One significant and potentially modifiable risk factor is reduced gait stability. Clinicians do not have sophisticated kinematic options to measure this risk factor with simple and affordable systems. Depth-imaging with AI-pose estimation can be used for gait analysis in young healthy adults. However, is it applicable for measuring gait in older adults at a risk of falling? Methods: In this methodological comparison 59 older adults with and without a history of falls walked on a treadmill while their gait pattern was recorded with multiple inertial measurement units and with an Azure Kinect depth-camera. Spatiotemporal gait parameters of both systems were compared for convergent validity and with a Bland-Altman plot. Results: Correlation between systems for stride length (r=.992, \u0000<inline-formula> <tex-math>$text{p} < 0.001$ </tex-math></inline-formula>\u0000) and stride time (r=0.914, \u0000<inline-formula> <tex-math>$text{p} < 0.001$ </tex-math></inline-formula>\u0000) was high. Bland-Altman plots revealed a moderate agreement in stride length (−0.74 ± 3.68 cm; [−7.96 cm to 6.47 cm]) and stride time (−3.7±54 ms; [−109 ms to 102 ms]). Conclusion: Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect cameras. Affordable and small depth-cameras agree with IMUs for gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact. Clinical Translation Statement— Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect. Affordable and small depth-cameras, developed for various purposes in research and industry, agree with IMUs in clinical gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy to assess function or monitor changes in gait is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"479-486"},"PeriodicalIF":3.4,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10129931","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-17DOI: 10.1109/JTEHM.2023.3276943
Zhaoyang Chen;Lina Siltala-Li;Mikko Lassila;Pekka Malo;Eeva Vilkkumaa;Tarja Saaresranta;Arho Veli Virkki
Background: Obstructive sleep apnea (OSA) is growing increasingly prevalent in many countries as obesity rises. Sufficient, effective treatment of OSA entails high social and financial costs for healthcare. Objective: For treatment purposes, predicting OSA patients’ visit expenses for the coming year is crucial. Reliable estimates enable healthcare decision-makers to perform careful fiscal management and budget well for effective distribution of resources to hospitals. The challenges created by scarcity of high-quality patient data are exacerbated by the fact that just a third of those data from OSA patients can be used to train analytics models: only OSA patients with more than 365 days of follow-up are relevant for predicting a year’s expenditures. Methods and procedures: The authors propose a translational engineering method applying two Transformer models, one for augmenting the input via data from shorter visit histories and the other predicting the costs by considering both the material thus enriched and cases with more than a year’s follow-up. This method effectively adapts state-of-the-art Transformer models to create practical cost prediction solutions that can be implemented in OSA management, potentially enhancing patient care and resource allocation. Results: The two-model solution permits putting the limited body of OSA patient data to productive use. Relative to a single-Transformer solution using only a third of the high-quality patient data, the solution with two models improved the prediction performance’s $R^{2}$ from 88.8% to 97.5%. Even using baseline models with the model-augmented data improved the $R^{2}$ considerably, from 61.6% to 81.9%. Conclusion: The proposed method makes prediction with the most of the available high-quality data by carefully exploiting details, which are not directly relevant for answering the question of the next year’s likely expenditure. Clinical and Translational Impact Statement: Public Health– Lack of high-quality source data hinders data-driven analytics-based research in healthcare. The paper presents a method that couples data augmentation and prediction in cases of scant healthcare data.
{"title":"Predicting Visit Cost of Obstructive Sleep Apnea Using Electronic Healthcare Records With Transformer","authors":"Zhaoyang Chen;Lina Siltala-Li;Mikko Lassila;Pekka Malo;Eeva Vilkkumaa;Tarja Saaresranta;Arho Veli Virkki","doi":"10.1109/JTEHM.2023.3276943","DOIUrl":"10.1109/JTEHM.2023.3276943","url":null,"abstract":"Background: Obstructive sleep apnea (OSA) is growing increasingly prevalent in many countries as obesity rises. Sufficient, effective treatment of OSA entails high social and financial costs for healthcare. Objective: For treatment purposes, predicting OSA patients’ visit expenses for the coming year is crucial. Reliable estimates enable healthcare decision-makers to perform careful fiscal management and budget well for effective distribution of resources to hospitals. The challenges created by scarcity of high-quality patient data are exacerbated by the fact that just a third of those data from OSA patients can be used to train analytics models: only OSA patients with more than 365 days of follow-up are relevant for predicting a year’s expenditures. Methods and procedures: The authors propose a translational engineering method applying two Transformer models, one for augmenting the input via data from shorter visit histories and the other predicting the costs by considering both the material thus enriched and cases with more than a year’s follow-up. This method effectively adapts state-of-the-art Transformer models to create practical cost prediction solutions that can be implemented in OSA management, potentially enhancing patient care and resource allocation. Results: The two-model solution permits putting the limited body of OSA patient data to productive use. Relative to a single-Transformer solution using only a third of the high-quality patient data, the solution with two models improved the prediction performance’s \u0000<inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula>\u0000 from 88.8% to 97.5%. Even using baseline models with the model-augmented data improved the \u0000<inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula>\u0000 considerably, from 61.6% to 81.9%. Conclusion: The proposed method makes prediction with the most of the available high-quality data by carefully exploiting details, which are not directly relevant for answering the question of the next year’s likely expenditure. Clinical and Translational Impact Statement: Public Health– Lack of high-quality source data hinders data-driven analytics-based research in healthcare. The paper presents a method that couples data augmentation and prediction in cases of scant healthcare data.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"306-317"},"PeriodicalIF":3.4,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10128115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9670347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-15DOI: 10.1109/JTEHM.2023.3256893
Rebecca J. Tarbert;Wamis Singhatat
Structured Abstract Falls with major injuries are a devastating occurrence for an older adult with outcomes inclusive of debility, loss of independence and increased mortality. The incidence of falls with major injuries has increased with the growth of the older adult population, and has further risen as a result of reduced physical mobility in recent years due to the Coronavirus pandemic. The standard of care in the effort to reduce major injuries from falling is provided by the CDC through an evidence-based fall risk screening, assessment and intervention initiative (STEADI: Stopping Elderly Accidents and Death Initiative) and is embedded into primary care models throughout residential and institutional settings nationwide. Though the dissemination of this practice has been successfully implemented, recent studies have shown that major injuries from falls have not been reduced. Emerging technology adapted from other industries offers adjunctive intervention in the older adult population at risk of falls and major fall injuries. Technology in the form of a wearable smartbelt that offers automatic airbag deployment to reduce impact forces to the hip region in serious hip-impacting fall scenarios was assessed in a long-term care facility. Device performance was examined in a real-world case series of residents who were identified as being at high-risk of major fall injuries within a long-term care setting. In a timeframe of almost 2 years, 35 residents wore the smartbelt, and 6 falls with airbag deployment occurred with a concomitant reduction in the overall falls with major injury rate.
{"title":"Real World Evidence of Wearable Smartbelt for Mitigation of Fall Impact in Older Adult Care","authors":"Rebecca J. Tarbert;Wamis Singhatat","doi":"10.1109/JTEHM.2023.3256893","DOIUrl":"10.1109/JTEHM.2023.3256893","url":null,"abstract":"Structured Abstract Falls with major injuries are a devastating occurrence for an older adult with outcomes inclusive of debility, loss of independence and increased mortality. The incidence of falls with major injuries has increased with the growth of the older adult population, and has further risen as a result of reduced physical mobility in recent years due to the Coronavirus pandemic. The standard of care in the effort to reduce major injuries from falling is provided by the CDC through an evidence-based fall risk screening, assessment and intervention initiative (STEADI: Stopping Elderly Accidents and Death Initiative) and is embedded into primary care models throughout residential and institutional settings nationwide. Though the dissemination of this practice has been successfully implemented, recent studies have shown that major injuries from falls have not been reduced. Emerging technology adapted from other industries offers adjunctive intervention in the older adult population at risk of falls and major fall injuries. Technology in the form of a wearable smartbelt that offers automatic airbag deployment to reduce impact forces to the hip region in serious hip-impacting fall scenarios was assessed in a long-term care facility. Device performance was examined in a real-world case series of residents who were identified as being at high-risk of major fall injuries within a long-term care setting. In a timeframe of almost 2 years, 35 residents wore the smartbelt, and 6 falls with airbag deployment occurred with a concomitant reduction in the overall falls with major injury rate.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"247-251"},"PeriodicalIF":3.4,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10070834","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9738475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-14DOI: 10.1109/JTEHM.2023.3257179
Yakun Liang;Xuejun Jiang;Bo Zhang
Background and objective: Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear random-effects models are a promising analytical tool for evaluating hospital performance. However, hospital compare data often violate the classical assumptions of normality on random effects and linearity representation on transformed conditional mean structures in these models. Methods: In this article, we proposed and tested the performance of a class of hospital compare models that embraces nonparametric mean structures with semi-nonparametric hospital random effects. Such models were further improved and integrated into a zero-inflated model. $mathtt {SAS}$ programs to implement these newly proposed hospital compare models were thoroughly developed. The $mathtt {SAS}$ programs are freely available via a GitHub ( https:www.GitHub.com ) repository. Results: We demonstrate the robustness of the proposed hospital compare models by conducting intensive empirical studies. Flexible semi-nonparametric random effects and functional fixed-effects mean structure were used to analyze patient mortality in a large-scale intensive care unit data set. After applying the proposed models to assess standardized modality rates and address patient-mix variability across hospitals, we detected those underperforming hospitals with higher mortality rates. Conclusions: Our research findings highlight how constructing advanced assessment tools for hospital performance could support better decision-making at the administrative and public levels. The proposed hospital compare models are comprehensive in their capacity to identify patterns of hospital random effects and to convey the variability in healthcare quality with powerful accuracy and interpretability.
{"title":"Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach","authors":"Yakun Liang;Xuejun Jiang;Bo Zhang","doi":"10.1109/JTEHM.2023.3257179","DOIUrl":"10.1109/JTEHM.2023.3257179","url":null,"abstract":"Background and objective: Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear random-effects models are a promising analytical tool for evaluating hospital performance. However, hospital compare data often violate the classical assumptions of normality on random effects and linearity representation on transformed conditional mean structures in these models. Methods: In this article, we proposed and tested the performance of a class of hospital compare models that embraces nonparametric mean structures with semi-nonparametric hospital random effects. Such models were further improved and integrated into a zero-inflated model. \u0000<inline-formula> <tex-math>$mathtt {SAS}$ </tex-math></inline-formula>\u0000 programs to implement these newly proposed hospital compare models were thoroughly developed. The \u0000<inline-formula> <tex-math>$mathtt {SAS}$ </tex-math></inline-formula>\u0000 programs are freely available via a GitHub (\u0000<uri>https:www.GitHub.com</uri>\u0000) repository. Results: We demonstrate the robustness of the proposed hospital compare models by conducting intensive empirical studies. Flexible semi-nonparametric random effects and functional fixed-effects mean structure were used to analyze patient mortality in a large-scale intensive care unit data set. After applying the proposed models to assess standardized modality rates and address patient-mix variability across hospitals, we detected those underperforming hospitals with higher mortality rates. Conclusions: Our research findings highlight how constructing advanced assessment tools for hospital performance could support better decision-making at the administrative and public levels. The proposed hospital compare models are comprehensive in their capacity to identify patterns of hospital random effects and to convey the variability in healthcare quality with powerful accuracy and interpretability.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"232-246"},"PeriodicalIF":3.4,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10068535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9365163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-14DOI: 10.1109/JTEHM.2023.3256966
Ahmad Chaddad;Lama Hassan;Yousef Katib;Ahmed Bouridane
Objective: Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients. Methods and procedures: We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ( $n$ =44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups. Results: Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19. Conclusion: Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner. Clinical impact: The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient’s chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.
{"title":"Deep Survival Analysis With Clinical Variables for COVID-19","authors":"Ahmad Chaddad;Lama Hassan;Yousef Katib;Ahmed Bouridane","doi":"10.1109/JTEHM.2023.3256966","DOIUrl":"10.1109/JTEHM.2023.3256966","url":null,"abstract":"Objective: Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients. Methods and procedures: We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables (\u0000<inline-formula> <tex-math>$n$ </tex-math></inline-formula>\u0000=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups. Results: Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19. Conclusion: Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner. Clinical impact: The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient’s chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"223-231"},"PeriodicalIF":3.4,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10068502","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9204129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. Methods and procedures: GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for target vessel revascularization (TVR) detection. Results: The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority ( ${p} < 0.01$ ) compared to prior GLP processing. Conclusion: Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise. Clinical impact: Our study effectively transposes patient progression from one cohort to another, surpassing the constraints of episodic observation. The transferability of disease progression contributed to cardiovascular event assessment.
{"title":"Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection","authors":"Li-Chin Chen;Kuo-Hsuan Hung;Yi-Ju Tseng;Hsin-Yao Wang;Tse-Min Lu;Wei-Chieh Huang;Yu Tsao","doi":"10.1109/JTEHM.2023.3307794","DOIUrl":"10.1109/JTEHM.2023.3307794","url":null,"abstract":"Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. Methods and procedures: GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for target vessel revascularization (TVR) detection. Results: The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority ( ${p} < 0.01$ ) compared to prior GLP processing. Conclusion: Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise. Clinical impact: Our study effectively transposes patient progression from one cohort to another, surpassing the constraints of episodic observation. The transferability of disease progression contributed to cardiovascular event assessment.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"43 - 55"},"PeriodicalIF":3.4,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10227304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136298155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-10DOI: 10.1109/JTEHM.2023.3275103
Boyuan Yan;Sheila Nirenberg
Optogenetics is a new approach for controlling neural circuits with numerous applications in both basic and clinical science. In retinal degenerative diseases, the photoreceptors die, but inner retinal cells remain largely intact. By expressing light sensitive proteins in the remaining cells, optogenetics has the potential to offer a novel approach to restoring vision. In the past several years, optogenetics has advanced into an early clinical stage, and promising results have been reported. At the current stage, there is an urgent need to develop hardware and software for clinical training, testing, and rehabilitation in optogenetic therapy, which is beyond the capability of existing ophthalmic equipment. In this paper, we present an engineering platform consisting of hardware and software utilities, which allow clinicians to interactively work with patients to explore and assess their vision in optogenetic treatment, providing the basis for prosthetic design, customization, and prescription. This approach is also applicable to other therapies that utilize light activation of neurons, such as photoswitches.Clinical and Translational Impact Statement–The engineering platform allows clinicians to conduct training, testing, and rehabilitation in optogenetic gene therapy for retinal degenerative diseases, providing the basis for prosthetic design, customization, and prescription.
{"title":"An Engineering Platform for Clinical Application of Optogenetic Therapy in Retinal Degenerative Diseases","authors":"Boyuan Yan;Sheila Nirenberg","doi":"10.1109/JTEHM.2023.3275103","DOIUrl":"10.1109/JTEHM.2023.3275103","url":null,"abstract":"Optogenetics is a new approach for controlling neural circuits with numerous applications in both basic and clinical science. In retinal degenerative diseases, the photoreceptors die, but inner retinal cells remain largely intact. By expressing light sensitive proteins in the remaining cells, optogenetics has the potential to offer a novel approach to restoring vision. In the past several years, optogenetics has advanced into an early clinical stage, and promising results have been reported. At the current stage, there is an urgent need to develop hardware and software for clinical training, testing, and rehabilitation in optogenetic therapy, which is beyond the capability of existing ophthalmic equipment. In this paper, we present an engineering platform consisting of hardware and software utilities, which allow clinicians to interactively work with patients to explore and assess their vision in optogenetic treatment, providing the basis for prosthetic design, customization, and prescription. This approach is also applicable to other therapies that utilize light activation of neurons, such as photoswitches.Clinical and Translational Impact Statement–The engineering platform allows clinicians to conduct training, testing, and rehabilitation in optogenetic gene therapy for retinal degenerative diseases, providing the basis for prosthetic design, customization, and prescription.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"296-305"},"PeriodicalIF":3.4,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2a/75/jtehm-nirenberg-3275103.PMC10217532.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10028170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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