A Mobile Clinical DSS based on Augmented Reality and Deep Learning for the home cares of patients afflicted by bedsores

Orciuoli, Francesco; Peduto, Angela

doi:10.1016/j.procs.2020.07.028

Cited by 18 publications

(6 citation statements)

References 11 publications

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“…In fact, 14 of the excluded studies focused on the classification of body postures or movements (e.g., [19][20][21][22][23][24][25][26]), which represents a very important subject in the monitoring of PU. Four of the excluded studies (e.g., [27][28][29][30]) addressed wound image analysis to characterize or classify PU. Despite describing interesting works, these studies did not propose any type of actions or consequences related to the postures and movements identified, or the stage (i.e., pressure ulcers classification according to the level of tissue damage), to improve the prevention and treatment of PU.…”

Section: Discussion and Findingsmentioning

confidence: 99%

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

et al. 2021

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Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities.

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Section: Discussion and Findingsmentioning

confidence: 99%

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

et al. 2021

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“…However, since the ICT investment in transformation requires great intention and financial hazard, infrastructure, operational management, digital acceptance, and digital learning, skills, abilities, and competencies, the measuring SMEs' digitalization readiness emerges as a solution in avoiding the various possible failures of digitization. Understanding the degree of digitization and servitization enforces the SMEs addressed to the right digital solutions, optimal business strategies, business models, and effective technology adoption (Paschou et al, 2018).…”

Section: Conceptual Model Developmentmentioning

confidence: 99%

SMEs digitalization readiness: Sharia fintech framework using Quadruple Helix perceives

Mahyarni,

Okfalisa

2024

Serb J Management

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The role of Quadruple Helix in promoting the development of digitizing SMEs highlights the significant contribution of Sharia fintech as a new challenge to sustainably and creatively face the Covid-19 disruption. This paper developed a framework of SME's Digitalization Readiness in Supporting Sharia Fintech to scrutinize the quadruple Helix perceptions and the success of digitalization. Furthermore, a Fuzzy-AHP approach is applied to analyze the significant weight of Quadruple Helix interaction and distinguish between universities, industry, communities, and government. This creates the basis of a sustainable corporate sector through the consideration of Information Technology (IT), economy, and Sharia within business activity, transaction, marketing, management, microenvironment, and macro-environment, circumscribed into 24 sub-indicators. This study found that university academicians view business activity as the most significant indication for gauging the digitization readiness of small and medium-sized enterprises (SMEs) based on Sharia fintech. It is followed by marketing, transaction, management, micro, and macro environment. Meanwhile, the other sectors including industry, communities, and government discerned marketing as the deliberation of the SMEs' Sharia-based success digitalization. This framework stimulates and synergizes an integrated works, innovation, economic growth, productivity, technology, knowledge, and skills of Quadruple Helix roles in facing the competitive advantages of digital era with concerning on the Sharia values. The prioritized analysis and recommendation from this framework admit as a guidance tool for Quadruple Helix in decision making and strategic policies in achieving the success of the digital transformation.

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“…A mobile application-based CDSS was [23] developed, which was leveraged on the deep learner to assist zone providers to estimate bedsores, categorize their condition, forecast their progress together the period and create proper solutions regarding the group of activities to efficiently diagnose them. But, the training data was inadequate, which impacts the accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Ensemble Learning with GAN-based Semi-Supervised Training Algorithm for Medical Decision Support System in Healthcare Applications

2022

IJIES

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Nowadays, clinical decision support system (CDSS) is emerged broadly due to the growth of smart applications. Yet, labeling a massive amount of clinical data is expensive and needs domain expertise. Also, how to design a robust CDSS model in a healthcare platform with a minimal number of annotated clinical data was an essential problem. To tackle this issue, a generative adversarial network (GAN)-based semi-supervised training model was developed, which increases the quantity of annotated data and solves the unbalanced annotated labels. As well, support vector machine (SVM) and K-nearest neighbor (KNN) classifiers were applied as the base learners to predict the label for unannotated data. But, the result from both base learners was not often similar, which impacts the prediction efficiency. So, this article introduces a deep ensemble learner model (DELM) for predicting labels to the unannotated data by stacking multiple machine learning classifiers as base learners. In this model, two levels of prediction: the initial level and the second level. During the initial level, multiple base learners including SVM, KNN, naive bayes (NB) and random forest (RF) learn the dataset with both the annotated and unannotated samples independently to predict the class of the unannotated instances. Then, those prediction outcomes are merged during the second level, which applies the deep neural network (DNN) as an ensemble classifier to get the final predicted class of the unannotated instance. Finally, the experimental results exhibit that the DELM with GAN achieves 86.54 %, 84.83 % and 86.72 % accuracies on SPECT, WDBC and Hallmarks databases, correspondingly, compared to the Fuzzy-AHP+ANN, HTM+LSTM, DBSCAN+SMOTE-ENN+XGBoost and GAN-based semi-supervised models.

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A Mobile Clinical DSS based on Augmented Reality and Deep Learning for the home cares of patients afflicted by bedsores

Cited by 18 publications

References 11 publications

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

SMEs digitalization readiness: Sharia fintech framework using Quadruple Helix perceives

Deep Ensemble Learning with GAN-based Semi-Supervised Training Algorithm for Medical Decision Support System in Healthcare Applications

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