Agent-Based Simulators for Empowering Patients in Self-Care Programs Using Mobile Agents with Machine Learning

Kumar, Swarn Avinash; García‐Magariño, Iván; Nasralla, Moustafa M.; Nazir, Shah

doi:10.1155/2021/5909281

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…Afterward, trust increases their online choice of doctors [ 39 ]. A recent study by Kumar et al [ 40 ] proposed certain agent-based simulators (ABSs), which, joining with machine learning, simulate the consequences of self-care mindfulness programs on emotions and heart rate variability; ABSs send these repercussions to an application, guiding patients in various exercises. Following the trial of this approach, patients demonstrated their positive attitudes toward it in terms of usefulness and ease of use.…”

Section: Theoretical Grounds and Literature Reviewmentioning

confidence: 99%

Stimuli Influencing Engagement, Satisfaction, and Intention to Use Telemedicine Services: An Integrative Model

Amin¹,

Hossain

Uddin

et al. 2022

Healthcare

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Telemedicine ensures quality, cost-effective, and equally accessible healthcare services for everyone. Nonetheless, a poor usage rate could curb its progression in developing cultures like Bangladesh. Therefore, this research examines how external stimuli promote the continuous usage intentions of synchronous telemedicine services through engagement and satisfaction by deploying the stimulus-organism-response framework. A final sample of 312 telemedicine users was analyzed using the structural equation modeling in AMOS. The average age of the participants was 26.28 (std. deviation 5.53), and their average use of telemedicine was 2.39 times (std. deviation 1.31) over the last six months. This study empirically endorsed that the stimuli, including performance expectancy, information quality, and contamination avoidance, as well as organismic factors such as engagement and satisfaction, directly impacted the continuance desires for telemedicine use. In addition, the analyses validated the mediation roles of engagement and satisfaction. Furthermore, performance and effort expectancies influenced engagement, which affected satisfaction along with performance expectancy, functionality, and information quality. Accordingly, telemedicine facilitators should integrate these critical attributes into the system to sustain engagement, satisfaction, and usage intentions. This study has pioneered the effects of performance and effort expectancies on continuous usage intentions facilitated by engagement and satisfaction in the telemedicine landscape.

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Section: Theoretical Grounds and Literature Reviewmentioning

confidence: 99%

Stimuli Influencing Engagement, Satisfaction, and Intention to Use Telemedicine Services: An Integrative Model

Amin¹,

Hossain

Uddin

et al. 2022

Healthcare

View full text Add to dashboard Cite

show abstract

“…Pandemic-induced uncertainty and fear among the general population increased stress, anxiety, and depression [ 8 ]. In this context, agent-based simulators (ABSs), as a unique patient-centered tool of e-health, promote self-care mindfulness and have shown promising results [ 9 ]. Combining ABSs with machine learning could significantly improve the wellbeing of patients without direct supervision from physicians.…”

Section: Introductionmentioning

confidence: 99%

Telemedicine in Neurosurgical Trauma during the COVID-19 Pandemic: A Single-Center Experience

Koruga

Rončević

et al. 2022

Diagnostics

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Telemedicine is a rapid tool that reduces the time until treatment for patients, which is especially useful for neurosurgical trauma. The aim of our study was to evaluate the use of telemedicine in neurosurgery during the COVID-19 pandemic compared with the pre-pandemic era. We assessed the utilization of telemedicine at the Department of Neurosurgery at University Hospital Center Osijek in Croatia over a timespan of one year prior to the COVID-19 pandemic and the first year of the pandemic, starting with the date of first lockdown in Croatia. For each time period, the total number of consults and specific clinical inquiries were recorded and adequately grouped as well as comprehensive patient characteristics. There were 336 consults in the pre-pandemic period and 504 in the pandemic period. The number of trauma-related consults during COVID-19 measures was significantly higher than the pre-pandemic era (288 and 138, respectively, p < 0.0001). Neurosurgical trauma patients requiring consults in the pandemic period were significantly older than before the pandemic (64.9 ± 18.5 and 60.6 ± 19.1, respectively, p = 0.03). Significantly, the number of admissions to our center and urgent surgeries did not significantly differ between these periods. Telemedicine is a cost-effective tool in the neurosurgical evaluation of patients, especially for trauma. The COVID-19 pandemic accelerated telemedicine implementation and improved neurosurgical trauma treatments.

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“…However, to follow the target person using various cameras, the individual's identity has to be retrieved from the second camera based on the detailed information acquired from the first camera. Re-identification is relatively correlated with a recognition problem [5]. The target person's probing image is provided, and all of their associated identities in a gallery collection are searched.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on Multi-Level Bottleneck Attention Modules Using Self-Guided Dropblock for Person Re-Identification

et al. 2022

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Person re-identification has inspired a lot of interest due to its significance in intelligent video surveillance. It is a difficult task due to the presence of critical challenges such as changes in appearance, misalignment, occlusion and background noise. Batch drop block layer (BDB) has been used recently in person re-identification by exploiting the feature erasing procedure. However, BDB drops a block of features randomly, resulting in the loss of contextual information, which makes the model difficult to train. Also, due to the random dropping of features, large area of discriminative information may lose during training, resulting in low efficiency and performance. To address this problem and to improve the model representation power, we propose a novel, lightweight, self-adaptive bottleneck attention module with a self-attention branch to improve the model performance by reducing the parameter overhead with negligible computation cost. The proposed approach entails bottleneck attention module (BAM) which is incorporated between ResNet layers to remove the background noise and to nominate the high-level semantic part. Further, dilated convolutions with batch normalization are used to tackle the contextual information loss problem and to avoid overfitting. In addition, an informative global branch is used which captures the global representation of the network, and the attention branch entails the multiscale local salient information. Two types of loss functions including softmax and batch hard triplet are used in the training process for each branch, forcing the network to encapsulate the common attribute within the similar identity and to maintain distance between distinct individuals. Compared with BDB, our network improves the mAp to 88.1% , and Rank-1 gets 96.3% for the market-1501 dataset. The results on Cuhk-03-Detected dataset showed 79.2% mAp score, with 81.4 %, Rank-1, whereas on Cuhk-03-labelled dataset, a mAP score of 81.3% and a Rank-1 score of 83.3% is achieved. Experiments reveal that ResNet model with addition of multiple BAM layers performs consistently over the state-of-the-art datasets using softmax and batch hard triplet loss.

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Agent-Based Simulators for Empowering Patients in Self-Care Programs Using Mobile Agents with Machine Learning

Cited by 10 publications

References 27 publications

Stimuli Influencing Engagement, Satisfaction, and Intention to Use Telemedicine Services: An Integrative Model

Stimuli Influencing Engagement, Satisfaction, and Intention to Use Telemedicine Services: An Integrative Model

Telemedicine in Neurosurgical Trauma during the COVID-19 Pandemic: A Single-Center Experience

A Novel Approach Based on Multi-Level Bottleneck Attention Modules Using Self-Guided Dropblock for Person Re-Identification

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