scholarly journals Analysing Long Short Term Memory Models for Cricket Match Outcome Prediction

2020 ◽  
Vol 8 (11) ◽  
pp. 1-8
Author(s):  
Rahul U Chakwate
Keyword(s):  
Outcome Prediction ◽  
Short Term Memory ◽  
Memory Models ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

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

2020 ◽  
Vol 10 (6) ◽  
pp. 2000 ◽  
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
Hyong Woo Moon ◽  
Ji Youl Lee
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Outcome Prediction ◽  
Short Term Memory ◽  
Castration Resistant Prostate Cancer ◽  
Short Term ◽  
Term Memory ◽  
Castration Resistant ◽  
Serial Data ◽  
Long Short Term Memory

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.

scholarly journals Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection

2019 ◽  
pp. 310-318 ◽  
Author(s):  
Riqiang Gao ◽  
Yuankai Huo ◽  
Shunxing Bao ◽  
Yucheng Tang ◽  
Sanja L. Antic ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Detection ◽  
Short Term Memory ◽  
Memory Models ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lung Cancer Detection

Bayesian Inference for Training of Long Short Term Memory Models in Chaotic Time Series Forecasting

2019 ◽  
pp. 197-208 ◽  
Author(s):  
Cristian Rodríguez Rivero ◽  
Julián Pucheta ◽  
Daniel Patiño ◽  
Jose Luis Puglisi ◽  
Paula Otaño ◽  
...  
Keyword(s):  
Time Series ◽  
Bayesian Inference ◽  
Short Term Memory ◽  
Memory Models ◽  
Time Series Forecasting ◽  
Chaotic Time Series ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Analyzing El Niño-Southern Oscillation Predictability Using Long-Short-Term-Memory Models

2019 ◽  
Vol 6 (2) ◽  
pp. 212-221 ◽  
Author(s):  
Andrew Huang ◽  
Ben Vega-Westhoff ◽  
Ryan L. Sriver
Keyword(s):  
El Niño ◽  
Short Term Memory ◽  
El Nino ◽  
Southern Oscillation ◽  
Memory Models ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory
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