Signal extraction system, system and method for speech restoration, learning method for neural network model, constructing method of neural network model, and signal processing system

2000 ◽  
Vol 108 (1) ◽  
pp. 21
Author(s):  
Masahiko Tateishi ◽  
Shinichi Tamura
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Model ◽  
Neural Network Model ◽  
Processing System ◽  
Signal Extraction ◽  
Extraction System ◽  
Learning Method ◽  
Signal Processing System ◽  
Speech Restoration

scholarly journals Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Baotian Hu ◽  
Adarsha Bajracharya ◽  
Hong Yu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Language Processing ◽  
Neural Network Model ◽  
Objective Assessment ◽  
Processing System ◽  
Clinical Decision ◽  
Medical Assessment ◽  
Clinical Diagnoses ◽  
Electronic Health

BACKGROUND Since its inception, artificial intelligence has aimed to use computers to help make clinical diagnoses. Evidence-based medical reasoning is important for patient care. Inferring clinical diagnoses is a crucial step during the patient encounter. Previous works mainly used expert systems or machine learning–based methods to predict the International Classification of Diseases - Clinical Modification codes based on electronic health records. We report an alternative approach: inference of clinical diagnoses from patients’ reported symptoms and physicians’ clinical observations. OBJECTIVE We aimed to report a natural language processing system for generating medical assessments based on patient information described in the electronic health record (EHR) notes. METHODS We processed EHR notes into the Subjective, Objective, Assessment, and Plan sections. We trained a neural network model for medical assessment generation (N2MAG). Our N2MAG is an innovative deep neural model that uses the Subjective and Objective sections of an EHR note to automatically generate an “expert-like” assessment of the patient. N2MAG can be trained in an end-to-end fashion and does not require feature engineering and external knowledge resources. RESULTS We evaluated N2MAG and the baseline models both quantitatively and qualitatively. Evaluated by both the Recall-Oriented Understudy for Gisting Evaluation metrics and domain experts, our results show that N2MAG outperformed the existing state-of-the-art baseline models. CONCLUSIONS N2MAG could generate a medical assessment from the Subject and Objective section descriptions in EHR notes. Future work will assess its potential for providing clinical decision support.

scholarly journals FAST NEURAL NETWORK FOR GENERALIZED TRIGONOMETRIC TRANSFORMATIONS SYNTHESIS

2014 ◽  
pp. 6-10
Author(s):  
Oleh Liskevych ◽  
Mykhaylo Yatsymirskyy
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Processing System ◽  
Digital Signal ◽  
Signal Processing System ◽  
Digital Signal Processing System ◽  
Hardware Neural Network

The structure of the fast hardware neural network, based on generalized trigonometric transformations algorithm is developed. The network is appointed for optimal by some given criteria transformation selection and synthesis in adaptive digital signal processing system.

scholarly journals Bayesian Stochastic Neural Network Model for Turbomachinery Damage Prediction

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shuhua Yang ◽  
Xiaomo Jiang ◽  
Shengli Xu ◽  
Xiaofang Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Signal Processing ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Data ◽  
Series Data ◽  
Stochastic Neural Network ◽  
Damage Prediction ◽  
Multivariate Correlation

Turbomachinery often suffers various defects such as impeller cracking, resulting in forced outage, increased maintenance costs, and reduced productivity. Condition monitoring and damage prognostics has been widely used as an increasingly important and powerful tool to improve the system availability, reliability, performance, and maintainability, but still very challenging due to multiple sources of data uncertainties and the complexity of analytics modeling. This paper presents an intelligent probabilistic methodology for anomaly prediction of high-fidelity turbomachine, considering multiple data imperfections and multivariate correlation. The proposed method adeptly integrates several advanced state-of-the-art signal processing and artificial intelligence techniques: wavelet multi-resolution decomposition, Bayesian hypothesis testing, probabilistic principal component analysis, and fuzzy stochastic neural network modeling. The advanced signal processing is employed to reduce dimensionality and to address multivariate correlation and data uncertainty for damage prediction. Instead of conventionally using raw time series data, principal components are utilized in the establishment of stochastic neural network model and anomaly prediction. Bayesian interval hypothesis testing metric is then presented to quantitatively compare the predicted and measured data for model validation and anomaly evaluation, thus providing a confidence indicator to judge the model quality and evaluate the equipment status. Bayesian method is developed in this study for denoising the raw data with multiresolution wavelet decomposition, quantifying the model accuracy, and assessing the equipment status. The dynamic stochastic neural network model is established via the nonlinear autoregressive moving average with exogenous inputs approach. It seamlessly integrates the fuzzy clustering and independent Bernoulli random function into radial basis function neural network. A natural gradient method based on Kullback-Leibler distance criterion is employed to maximize the log-likelihood loss function. The effectiveness of the proposed methodology and procedure is demonstrated with the 11-variable time series data and the forced outage event of a real-world centrifugal compressor.

scholarly journals Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation

10.2196/14971 ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. e14971
Author(s):  
Baotian Hu ◽  
Adarsha Bajracharya ◽  
Hong Yu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Language Processing ◽  
Neural Network Model ◽  
Objective Assessment ◽  
Processing System ◽  
Clinical Decision ◽  
Medical Assessment ◽  
Clinical Diagnoses ◽  
Electronic Health

Background Since its inception, artificial intelligence has aimed to use computers to help make clinical diagnoses. Evidence-based medical reasoning is important for patient care. Inferring clinical diagnoses is a crucial step during the patient encounter. Previous works mainly used expert systems or machine learning–based methods to predict the International Classification of Diseases - Clinical Modification codes based on electronic health records. We report an alternative approach: inference of clinical diagnoses from patients’ reported symptoms and physicians’ clinical observations. Objective We aimed to report a natural language processing system for generating medical assessments based on patient information described in the electronic health record (EHR) notes. Methods We processed EHR notes into the Subjective, Objective, Assessment, and Plan sections. We trained a neural network model for medical assessment generation (N2MAG). Our N2MAG is an innovative deep neural model that uses the Subjective and Objective sections of an EHR note to automatically generate an “expert-like” assessment of the patient. N2MAG can be trained in an end-to-end fashion and does not require feature engineering and external knowledge resources. Results We evaluated N2MAG and the baseline models both quantitatively and qualitatively. Evaluated by both the Recall-Oriented Understudy for Gisting Evaluation metrics and domain experts, our results show that N2MAG outperformed the existing state-of-the-art baseline models. Conclusions N2MAG could generate a medical assessment from the Subject and Objective section descriptions in EHR notes. Future work will assess its potential for providing clinical decision support.

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