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Your search keyword '"phased long short-term memory"' showing total 4 results
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4 results on '"phased long short-term memory"'

1. Castration-Resistant Prostate Cancer Outcome Prediction Using Phased Long Short-Term Memory with Irregularly Sampled Serial Data.

Author

Park, Jihwan, Rho, Mi Jung, Moon, Hyong Woo, and Lee, Ji Youl

Subjects
CASTRATION-resistant prostate cancer, SHORT-term memory, FORECASTING, PROSTATE cancer, PREDICTION models
Abstract

It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients' likely outcomes to support physicians' decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) model with a special module called a "time-gate" is used to process the irregularly sampled data sets. A synthetic minority oversampling technique is used to overcome the data imbalance between two patient groups: those with and without CRPC treatment. The phased-LSTM model is able to predict the CRPC outcome with an accuracy of 88.6% (precision-recall: 91.6%) using 120 days of data or 94.8% (precision-recall: 96.9%) using 360 days of data. The validation loss converged slowly with 120 days of data and quickly with 360 days of data. In both cases, the prediction model takes four epochs to build. The overall CPRC outcome prediction model using irregularly sampled serial medical data is accurate and can be used to support physicians' decision-making, which saves time compared to cumbersome serial data reviews. This study can be extended to realize clinically meaningful prediction models. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
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2. Castration-Resistant Prostate Cancer Outcome Prediction Using Phased Long Short-Term Memory with Irregularly Sampled Serial Data

Author

Jihwan Park, Mi Jung Rho, Hyong Woo Moon, and Ji Youl Lee

Subjects
prostate cancer, castration-resistant prostate cancer, deep learning, phased long short-term memory, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients’ likely outcomes to support physicians’ decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) model with a special module called a “time-gate” is used to process the irregularly sampled data sets. A synthetic minority oversampling technique is used to overcome the data imbalance between two patient groups: those with and without CRPC treatment. The phased-LSTM model is able to predict the CRPC outcome with an accuracy of 88.6% (precision-recall: 91.6%) using 120 days of data or 94.8% (precision-recall: 96.9%) using 360 days of data. The validation loss converged slowly with 120 days of data and quickly with 360 days of data. In both cases, the prediction model takes four epochs to build. The overall CPRC outcome prediction model using irregularly sampled serial medical data is accurate and can be used to support physicians’ decision-making, which saves time compared to cumbersome serial data reviews. This study can be extended to realize clinically meaningful prediction models.

Published
2020
Full Text
View/download PDF

3. Medical Informatics and Data Analysis.

Author

Nieminen, Pentti and Nieminen, Pentti

Subjects
Medicine, Active Contour Models, GLM, GVF, ICD coding, Iterative simulation, Long-term care (LTC), Monte Carlo, South Korean female, Type I error, age-dependent stretched exponent, biostatistics, bird swarm algorithm, case study, castration-resistant prostate cancer, cervical injury, characteristic life, compartmental model, data analysis, data anonymization, data mining, data presentation, decision support system, deep belief network, deep learning, diabetes control, electronic healthcare, health, health care, heart disease diagnosis, heuristic approach, hierarchical classification, importance-satisfaction (I-S) model, inertial sensors, mathematical models, maximum lifespan, medical informatics, medicine, mobile assistant, modified stretched exponential function, n/a, neck pain, neck surface accelerometer, performance evaluation matrix (PEM), phased long short-term memory, power simulation, prostate cancer, prostate imaging, publications, reporting, retirement threshold, service quality performance matrix (SQPM), skewed data, snake segmentation, software tool, sparse FCM, statistical computing, statistics, surgery scheduling, t-test, vocal distance dose, vocal fatigue, voice of customer (VOC)
Abstract

Summary: During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role.

4. Castration-Resistant Prostate Cancer Outcome Prediction Using Phased Long Short-Term Memory with Irregularly Sampled Serial Data

Author

Ji Youl Lee, Mi Jung Rho, Hyong Woo Moon, and Jihwan Park

Subjects
medicine.medical_specialty, Serial communication, 030232 urology & nephrology, Castration resistant, urologic and male genital diseases, Data imbalance, lcsh:Technology, lcsh:Chemistry, 03 medical and health sciences, Prostate cancer, Long short term memory, 0302 clinical medicine, phased long short-term memory, medicine, castration-resistant prostate cancer, General Materials Science, lcsh:QH301-705.5, Instrumentation, Fluid Flow and Transfer Processes, lcsh:T, business.industry, Process Chemistry and Technology, General Engineering, deep learning, prostate cancer, medicine.disease, lcsh:QC1-999, Computer Science Applications, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, 030220 oncology & carcinogenesis, Radiology, lcsh:Engineering (General). Civil engineering (General), Outcome prediction, business, lcsh:Physics, Predictive modelling
Abstract

It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients&rsquo, likely outcomes to support physicians&rsquo, decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) model with a special module called a &ldquo, time-gate&rdquo, is used to process the irregularly sampled data sets. A synthetic minority oversampling technique is used to overcome the data imbalance between two patient groups: those with and without CRPC treatment. The phased-LSTM model is able to predict the CRPC outcome with an accuracy of 88.6% (precision-recall: 91.6%) using 120 days of data or 94.8% (precision-recall: 96.9%) using 360 days of data. The validation loss converged slowly with 120 days of data and quickly with 360 days of data. In both cases, the prediction model takes four epochs to build. The overall CPRC outcome prediction model using irregularly sampled serial medical data is accurate and can be used to support physicians&rsquo, decision-making, which saves time compared to cumbersome serial data reviews. This study can be extended to realize clinically meaningful prediction models.

Published
2020

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