Automatic Audio-Based Classification of Patient Inhaler Use: A Pharmacy Based Study

McNulty, Johnny; Reilly, Richard B.; Taylor, Terence E.; O'Dwyer, Susan; Costello, Richard W.; Zigel, Yaniv

doi:10.1109/embc.2019.8857132

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The proposed system makes intelligent patient care, monitoring, and management decisions using ensemble machine learning and fuzzy logic to determine the possible con-Figure 18. Comparative analysis of performance parameters with the existing methods, [8,9,12,15,18,25,28,30,32,39].…”

Section: Discussionmentioning

confidence: 99%

“…Patients utilizing a Diskus dry powder inhaler were recorded using the Inhaler Compliance Assessment device [27]. A system with a scalable design that can track thousands of older people, spot falls, and alert caretakers is shown in [28]. The proposed system employed machine learning to choose a medical specialty based on a patient's combined illness symptoms [29].…”

Section: Related Workmentioning

confidence: 99%

“…Figure 18.Comparative analysis of performance parameters with the existing methods,[8,9,12,15,18,25,28,30,32,39] …”

mentioning

confidence: 99%

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A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters

2022

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The tremendous growth of health-related digital information has transformed machine learning algorithms, allowing them to deliver more relevant information while remotely monitoring patients in modern telemedicine. However, patients with epilepsy are likely to die or have post-traumatic difficulties. As a result, early disease detection could be essential for a person’s survival. Hence, early diagnosis of epilepsy based on health parameters is needed. This paper presents a classification of epilepsy disease based on wearable-sensor health parameters that use a hybrid approach with ensemble machine learning and a fuzzy logic inference system. The ensemble machine learning classifiers are used to predict epilepsy events using ensemble bagging and ensemble boosting regression. The experimental results show that compared to the ensemble bagging classifiers and other state-of-the-art methods, the ensemble boosting classifier with the fuzzy inference system outperformed with a 97% accuracy rate.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters

2022

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Max-Fusion of Random Ensemble Subspace Discriminant with Aggregation of MFCCs and High Scalogram Coefficients for Acoustics Classification

Chin

Xiao

2021

2021 IEEE/ACIS 19th International Conference on Computer and Information Science (ICIS)

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Secured Fog Computing Based Framework for Intelligent Health Diagnosis Using Deep Learning and Internet of Things (IoT)

Kadu,

Singh,

Gandam

et al. 2024

Preprint

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With the proliferation of IoT devices and cloud-based data processing technologies, cutting-edge smart, linked healthcare systems may be created. Smart healthcare systems examine patient data collected by the Internet of Things to improve treatment quality and lower healthcare expenditures. The amount of data produced by the billions of IoT devices connected to the Cloud-of-Things (CoT) is a significant problem for these systems. By bridging the gap between Internet of Things (IoT) gadgets and cloud computing, fog computing infrastructure provides a possible answer. It has the potential to provide low-latency, high-efficiency processing and storage for the Internet of Things at a tiny scale. However, privacy-preservation concerns are a major issue for healthcare systems due to the sensitive nature of patient data. In this study, we offer a unique secured computing architecture for intelligent healthcare diagnosis systems. For the sake of in-formation safety, a lightweight and trustworthy user authentication mechanism is made available. Electroencephalogram (EEG) data are used as input to an intelligent diagnosis system that use deep learning techniques to automate the identification of epileptic seizures. Security and performance evaluations, which compare the proposed technique to the current method for seizure prediction, show that it is secure and efficient.

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Automatic Audio-Based Classification of Patient Inhaler Use: A Pharmacy Based Study

Cited by 3 publications

References 15 publications

A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters

A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters

Max-Fusion of Random Ensemble Subspace Discriminant with Aggregation of MFCCs and High Scalogram Coefficients for Acoustics Classification

Secured Fog Computing Based Framework for Intelligent Health Diagnosis Using Deep Learning and Internet of Things (IoT)

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