Real-Time Automated Cardiac Health Monitoring by Combination of Active Learning and Adaptive Feature Selection

doi:10.3837/tiis.2013.01.007

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…In this model, the VCA transform will produce a large number of features, and its input space needs to strictly control the feature dimension. Therefore, we directly set the reservation dimension of the PCA transform [8]. Because different dimensions retain different information, the performance of the model and the experimental results are affected, and this effect is not positively correlated with the retention dimension.…”

Section: Pca Dimension Reduction Stagementioning

confidence: 99%

Medical Data Feature Learning Based on Probability and Depth Learning Mining: Model Development and Validation

Yang

2021

JMIR Med Inform

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Background Big data technology provides unlimited potential for efficient storage, processing, querying, and analysis of medical data. Technologies such as deep learning and machine learning simulate human thinking, assist physicians in diagnosis and treatment, provide personalized health care services, and promote the use of intelligent processes in health care applications. Objective The aim of this paper was to analyze health care data and develop an intelligent application to predict the number of hospital outpatient visits for mass health impact and analyze the characteristics of health care big data. Designing a corresponding data feature learning model will help patients receive more effective treatment and will enable rational use of medical resources. Methods A cascaded depth model was successfully implemented by constructing a cascaded depth learning framework and by studying and analyzing the specific feature transformation, feature selection, and classifier algorithm used in the framework. To develop a medical data feature learning model based on probabilistic and deep learning mining, we mined information from medical big data and developed an intelligent application that studies the differences in medical data for disease risk assessment and enables feature learning of the related multimodal data. Thus, we propose a cascaded data feature learning model. Results The depth model created in this paper is more suitable for forecasting daily outpatient volumes than weekly or monthly volumes. We believe that there are two reasons for this: on the one hand, the training data set in the daily outpatient volume forecast model is larger, so the training parameters of the model more closely fit the actual data relationship. On the other hand, the weekly and monthly outpatient volume is the cumulative daily outpatient volume; therefore, errors caused by the prediction will gradually accumulate, and the greater the interval, the lower the prediction accuracy. Conclusions Several data feature learning models are proposed to extract the relationships between outpatient volume data and obtain the precise predictive value of the outpatient volume, which is very helpful for the rational allocation of medical resources and the promotion of intelligent medical treatment.

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Section: Pca Dimension Reduction Stagementioning

confidence: 99%

Medical Data Feature Learning Based on Probability and Depth Learning Mining: Model Development and Validation

Yang

2021

JMIR Med Inform

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A Hybrid Feature Selection Method to Classification and Its Application in Hypertension Diagnosis

Park

Piao

et al. 2017

Lecture Notes in Computer Science

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The Experimental Study on Concrete Permeability of Wireless Communication Module Embedded in Reinforced Concrete Structures

Park

Yoon

2013

International Journal of Distributed Sensor Networks

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Recently, as the information industry and mobile communication technology develop, their study is conducted on the new concept of intelligent structures and maintenance techniques that apply wireless sensor network, USN (Ubiquitous Sensor Network), to social infrastructures such as civil and architectural structures on the basis of the concept of Ubiquitous Computing, which invisibly provides human life with computing, along with mutually cooperating, compromising, and connecting networks to each other by having computers within all objects around us. The purpose of this study is to investigate the capability of wireless communication of sensor node embedded in reinforced concrete structure with a basic experiment on electric wave permeability of sensor node by fabricating molding with variables of concrete thickness and steel bars that are mostly used in constructing structures to determine the feasibility of application to constructing structures with USN. By installing wireless communication module inside the structures, it is possible to communicate to measure the pitch of steel bars and permeability of concrete, by measuring in both directions horizontally and vertically. The magnitude of an electric wave in the range of used frequencies was measured by using Spectrum Analyzer. This electric wave was numerically analyzed and the effective wavelength of frequencies was analyzed by the properties of a frequency band area. As a result of constructing structures with wireless sensors, Plain concrete showed 45 cm for depth of permeability. Reinforced concretes that has pitches of 5 cm showed 37 cm and pitches of 15 cm showed 45 cm for the depth of permeability. This suggests that if the pitch of steel bars was more than 15 cm, it would not affect wireless communication.

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Real-Time Automated Cardiac Health Monitoring by Combination of Active Learning and Adaptive Feature Selection

Cited by 4 publications

References 36 publications

Medical Data Feature Learning Based on Probability and Depth Learning Mining: Model Development and Validation

Medical Data Feature Learning Based on Probability and Depth Learning Mining: Model Development and Validation

A Hybrid Feature Selection Method to Classification and Its Application in Hypertension Diagnosis

The Experimental Study on Concrete Permeability of Wireless Communication Module Embedded in Reinforced Concrete Structures

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