beHEALTHIER: A Microservices Platform for Analyzing and Exploiting Healthcare Data

Mavrogiorgou, Argyro; Kleftakis, Spyridon; Mavrogiorgos, Konstantinos; Zafeiropoulos, Nikolaos; Menychtas, Andreas; Kiourtis, Athanasios; Kyriazis, Dimosthenis

doi:10.1109/cbms52027.2021.00078

Cited by 20 publications

(7 citation statements)

References 30 publications

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“…Researchers are exploring the possibility of using big data management mechanisms to provide care services to older adults and help them achieve Successful Aging [ 26 , 84 , 85 ]. Such approaches involve digital equipment and technological solutions at an older person’s home to acquire real-time data for predictive and optimized health service delivery.…”

Section: Discussionmentioning

confidence: 99%

Successful Aging for Community-Dwelling Older Adults: An Experimental Study with a Tablet App

Chaudhry

Dasgupta

Chawla

2022

IJERPH

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Mobile health (mHealth) technologies offer an opportunity to enable the care and support of community-dwelling older adults, however, research examining the use of mHealth in delivering quality of life (QoL) improvements in the older population is limited. We developed a tablet application (eSeniorCare) based on the Successful Aging framework and investigated its feasibility among older adults with low socioeconomic status. Twenty five participants (females = 14, mean age = 65 years) used the app to set and track medication intake reminders and health goals, and to play selected casual mobile games for 24 weeks. The Older person QoL and Short Health (SF12v2) surveys were administered before and after the study. The Wilcoxon rank tests were used to determine differences from baseline, and thematic analysis was used to analyze post-study interview data. The improvements in health-related QoL (HRQoL) scores were statistically significant (V=41.5, p=0.005856) across all participants. The frequent eSeniorCare users experienced statistically significant improvements in their physical health (V=13, p=0.04546) and HRQoL (V=7.5, p=0.0050307) scores. Participants reported that the eSeniorCare app motivated timely medication intake and health goals achievement, whereas tablet games promoted mental stimulation. Participants were willing to use mobile apps to self-manage their medications (70%) and adopt healthy activities (72%), while 92% wanted to recommend eSeniorCare to a friend. This study shows the feasibility and possible impact of an mHealth tool on the health-related QoL in older adults with a low socioeconomic status. mHealth support tools and future research to determine their effects are warranted for this population.

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Section: Discussionmentioning

confidence: 99%

Successful Aging for Community-Dwelling Older Adults: An Experimental Study with a Tablet App

Chaudhry

Dasgupta

Chawla

2022

IJERPH

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“…In addition, our critical analysis of passive multimodal data sources and modality-specific features provides insights to explore the effectiveness of other modality combinations for specific MH disorders. Subsequently, this inspires the development of specific tools that leverage external or multiple data sources to support mental healthcare professionals in their clinical practice (e.g., drawing inspiration from the beHEALTHIER platform [ 319 ] which integrates different types of healthcare data, including health, social care, and clinical signs, to construct effective health policies). We envision engaging with MH professionals through workshops, webinars, or other collaborative efforts to bridge the gap between research and practice.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

Khoo,

Lim,

Chong

et al. 2024

Sensors

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As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.

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“…Looking into the Quantity-rating metric -CV results (Figure 5) it is apparent that the SVD algorithms could not fit the data and as a result they need Hyperparameter Tuning as to provide better results. On the other hand, the BaselineOnly algorithm managed to rank first with a RMSE score around ~28 (27,129). (4)…”

Section: -2-1-cross Validationmentioning

confidence: 99%

“…and the number of 'Hits' each product made. Above all, our further goals include additionally evaluating the derived RS methods deployed in the infrastructure of the Diastema project [26], exploiting high-end hardware specifications (e.g., Solid State Drives (SSD) compared to Hard Disk Drives (HDD), DDR4 memories instead of DDR3 with higher capacities and more efficient CPUs), and integrating the derived RS techniques within the microservices platform of beHEALTHIER [27].…”

Section: Figure 15 Radar Plot Of Lightfm In Comparison With Surprise ...mentioning

confidence: 99%

A Comparative Study of Collaborative Filtering in Product Recommendation

Patoulia

Kiourtis²,

Mavrogiorgou³

et al. 2022

Emerg Sci J

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Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PDF

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beHEALTHIER: A Microservices Platform for Analyzing and Exploiting Healthcare Data

Cited by 20 publications

References 30 publications

Successful Aging for Community-Dwelling Older Adults: An Experimental Study with a Tablet App

Successful Aging for Community-Dwelling Older Adults: An Experimental Study with a Tablet App

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

A Comparative Study of Collaborative Filtering in Product Recommendation

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