Medical Big Data Mining and Processing in e-Healthcare

Kumar, S. Adish; Gayathri, N.; Muthuramalingam, S.; Balamurugan, B.; Ramesh, C.; Nallakaruppan, M. K.

doi:10.1016/b978-0-12-817356-5.00016-4

Cited by 24 publications

(6 citation statements)

References 28 publications

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“…The metric used to evaluate the performance of the four models is Accuracy (Equation 1) which is defined as the ratio of true predicted samples on the total number of samples [48,49]. As cited in [48,50], accuracy is the most used evaluation metric for either binary classification or multi-class classification.…”

Section: Model Validation and Resultsmentioning

confidence: 99%

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Natural Language Processing Model for Managing Maintenance Requests in Buildings

et al. 2020

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In recent years, facility management (FM) has adopted many computer technology solutions for building maintenance, such as building information modelling (BIM) and computerized maintenance management systems (CMMS). However, maintenance requests management in buildings remains a manual and a time-consuming process that depends on human management. In this paper, a machine-learning algorithm based on natural language processing (NLP) is proposed to classify maintenance requests. This algorithm aims to assist the FM teams in managing day-to-day maintenance activities. A healthcare facility is addressed as a case study in this work. Ten-year maintenance records from the facility contributed to the design and development of the algorithm. Multiple NLP methods were used in this study, and the results reveal that the NLP model can classify work requests with an average accuracy of 78%. Furthermore, NLP methods have proven to be effective for managing unstructured text data.

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Section: Model Validation and Resultsmentioning

confidence: 99%

“…The metric used to evaluate the performance of the four models is Accuracy (Equation 1) which is defined as the ratio of true predicted samples on the total number of samples [48,49].…”

Section: Model Validation and Resultsmentioning

confidence: 99%

Natural Language Processing Model for Managing Maintenance Requests in Buildings

et al. 2020

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“…We trained the selected classifiers using a two-step algorithm. We used the following metrics to assess the quality of the trained models: accuracy, precision, recall, and F1measure [46][47][48], training and running time, and the value of log_loss loss function.…”

Section: Algorithmmentioning

confidence: 99%

Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning

et al. 2023

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Since the COVID-19 pandemic, the demand for respiratory rehabilitation has significantly increased. This makes developing home (remote) rehabilitation methods using modern technology essential. New techniques and tools, including wireless sensors and motion capture systems, have been developed to implement remote respiratory rehabilitation. Significant attention during respiratory rehabilitation is paid to the type of human breathing. Remote rehabilitation requires the development of automated methods of breath analysis. Most currently developed methods for analyzing breathing do not work with different types of breathing. These methods are either designed for one type (for example, diaphragmatic) or simply analyze the lungs’ condition. Developing methods of determining the types of human breathing is necessary for conducting remote respiratory rehabilitation efficiently. We propose a method of determining the type of breathing using wireless sensors with the motion capture system. To develop that method, spectral analysis and machine learning methods were used to detect the prevailing spectrum, the marker coordinates, and the prevailing frequency for different types of breathing. An algorithm for determining the type of human breathing is described. It is based on approximating the shape of graphs of distances between markers using sinusoidal waves. Based on the features of the resulting waves, we trained machine learning models to determine the types of breathing. After the first stage of training, we found that the maximum accuracy of machine learning models was below 0.63, which was too low to be reliably used in respiratory rehabilitation. Based on the analysis of the obtained accuracy, the training and running time of the models, and the error function, we choose the strategy of achieving higher accuracy by increasing the training and running time of the model and using a two-stage method, composed of two machine learning models, trained separately. The first model determines whether the breath is of the mixed type; if it does not predict the mixed type of breathing, the second model determines whether breathing is thoracic or abdominal. The highest accuracy achieved by the composite model was 0.81, which surpasses single models and is high enough for use in respiratory rehabilitation. Therefore, using three wireless sensors placed on the patient’s body and a two-stage algorithm using machine learning models, it was possible to determine the type of human breathing with high enough precision to conduct remote respiratory rehabilitation. The developed algorithm can be used in building rehabilitation applications.

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“…The resulting matrix contains zero and one elements that can be treated as a binary pixel classification problem, which can be determined from the classification criteria. The following accuracy criterion is adopted to evaluate the image recognition performance [29]:…”

Section: B Noisy Measurementsmentioning

confidence: 99%

Semantic Communication for Critical 6G Applications Based on Compressed Sensing

Rezaei,

Azmi,

Jorswieck

et al. 2023

Preprint

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Medical Big Data Mining and Processing in e-Healthcare

Cited by 24 publications

References 28 publications

Natural Language Processing Model for Managing Maintenance Requests in Buildings

Natural Language Processing Model for Managing Maintenance Requests in Buildings

Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning

Semantic Communication for Critical 6G Applications Based on Compressed Sensing

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