Interactive Dimensionality Reduction for Improving Patient Adherence in Remote Health Monitoring

Kalatzis, Apostolos; Mortazavi, Bobak J.; Pourhomayoun, Mohammad

doi:10.1109/csci46756.2018.00149

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Artificial Intelligence (AI) has been shown to be an effective tool in predicting medical conditions and adverse events, and help caregivers with medical decision-making [9]- [13]. In this study, we proposed a data-driven predictive analytics algorithm based on Artificial Intelligence (AI) and machine learning to determine the health risk and predict the mortality risk of patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making

Pourhomayoun

Shakibi

2020

Preprint

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In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used documented data of 117,000 patients world-wide with laboratory-confirmed COVID-19. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 93% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.

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Section: Introductionmentioning

confidence: 99%

Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making

Pourhomayoun

Shakibi

2020

Preprint

Self Cite

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“…Unsupervised techniques are exploratory and used to find undefined patterns or clusters which occur within datasets. Unsupervised algorithms categorized into clustering algorithms as hierarchal clustering, k-means, and fuzzy c-means, or dimension reduction algorithms used for compression of information in datasets into fewer features, or dimensions to avoid issues as multiple collinearity or high computational cost [71,72,73].…”

Section: Machine Learningmentioning

confidence: 99%

Requirements for Adaptive Consumer Gateways in Residential Learning Healthcare Systems: Bringing Intelligence to the Edge

Fares,

Sherratt

2024

IEEE Trans. Consumer Electron.

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A gateway is a key component in residential healthcare systems. Enabling adaptability and applying intelligence to the gateway will promote residential healthcare systems to become Learning Healthcare Systems (LHSs) that can perform real-time decision making. This leads to the exciting potential of a new research field in consumer-oriented gateways and consumer products that can adapt to the consumer's healthcare needs, learn about the consumer, and over time can adapt accordingly. While consumer healthcare gateways exist, they have tended to be fixed on specific medical conditions and are not upgradeable or adaptive. To be able to create adaptive consumer gateways for consumer healthcare applications, this paper identifies a set of requirements concerning scalability, energy efficiency, reliability, availability, interoperability, and privacy that need to be fulfilled before any product or service can be created. Intervention in local data processing, local data storage, embedded data mining, security, interoperability, and configurability that serve the development process are also discussed. The goal of this paper is to provide the requirements for the innovation of a one-for-all smart adaptive consumer gateway in residential learning healthcare systems and to influence the consumer healthcare field to consider the benefits of moving to adaptive gateways for future developments.

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“…Among numerous approaches, Artificial intelligence (AI) has emerged as a promising solution and has proved its competence in this regard. AI has been widely employed to predict the count of COVID-19 and thus can be a great aid for the medical professional and associated sensitive medical decisions [8][9][10][11][12][13]. Resultantly, numerous researchers have widely employed AI and its associated disciplines for COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Predicting mortality rate and associated risks in COVID-19 patients

et al. 2021

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The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which was later declared as a global pandemic in view of its exponential rise and spread around the world. Resultantly, the scientific and medical research communities around the globe geared up to curb its spread. In this manuscript, authors claim competence of AI-mediated methods to predict mortality rate. Efficient prediction model enables healthcare professionals to be well prepared to handle this unpredictable situation. The prime focus of the study is to investigate efficient prediction model. In order to determine the most effective prediction model, authors perform comparative analysis of numerous models. The performance of various prediction models is compared using various error metrics viz. Root mean square error, mean absolute error, mean square error and R 2 . During comparative analysis, Auto seasonal auto regressive integrated moving average model proves its competence over comparative models.

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Interactive Dimensionality Reduction for Improving Patient Adherence in Remote Health Monitoring

Cited by 6 publications

References 11 publications

Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making

Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making

Requirements for Adaptive Consumer Gateways in Residential Learning Healthcare Systems: Bringing Intelligence to the Edge

Predicting mortality rate and associated risks in COVID-19 patients

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