Leveraging a Machine Learning Approach to Predict Recurrent Hepatocellular Carcinoma Following Liver Transplantation – A Step in the Right Direction?

2021 ◽  
Author(s):  
Benjamin V. Tran ◽  
Vatche G. Agopian
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Learning Approach ◽  
Recurrent Hepatocellular Carcinoma ◽  
Machine Learning Approach ◽  
The Right

A novel machine learning approach for early detection of hepatocellular carcinoma patients

2019 ◽  
Vol 54 ◽  
pp. 116-127 ◽  
Author(s):  
Wojciech Książek ◽  
Moloud Abdar ◽  
U. Rajendra Acharya ◽  
Paweł Pławiak
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Early Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Classifying Non-Sentential Utterances in Dialogue: A Machine Learning Approach

2007 ◽  
Vol 33 (3) ◽  
pp. 397-427 ◽  
Author(s):  
Raquel Fernández ◽  
Jonathan Ginzburg ◽  
Shalom Lappin
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Full Range ◽  
Learning Approach ◽  
Learning Methods ◽  
Fine Grained ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
The Right

In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focused on one of the NSU classes in the taxonomy—bare wh-phrases or “sluices”—and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.

scholarly journals Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Masaya Sato ◽  
Kentaro Morimoto ◽  
Shigeki Kajihara ◽  
Ryosuke Tateishi ◽  
Shuichiro Shiina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Predictive Model ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Enhanced Classification of Incomplete Pattern Using Fuzzy Systems

2019 ◽  
pp. 204-210
Author(s):  
Yogita Deshmukh ◽  
Pallavi Khawshi ◽  
Priyanka Shinde ◽  
Ruchita Charpe ◽  
Rupali Bopche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Fuzzy Systems ◽  
Learning Approach ◽  
Specific Class ◽  
Course Of Action ◽  
Data Segment ◽  
Machine Learning Approach ◽  
The Right

More often than not values are absent in database, which ought to be managed. Missing qualities are occurred in light of the way that, the data segment individual did not know the right regard or frustration of sensors or leave the space cleanse. The course of action of missing regarded lacking case is a trying errand in machine learning approach. Divided data is not proper for classification handle. Exactly when insufficient cases are masterminded using prototype values, the last class for comparable illustrations may have distinctive results that are variable yields. We can't describe specific class for specific cases. The structure makes a wrong result which also realizes contrasting effects. So, to oversee such kind of lacking data, system executes prototype-based credal classification (PCC) strategy. The PCC procedure is intertwined with Hierarchical clustering and evidential reasoning methodology to give correct, time and memory profitable outcomes. This procedure readies the examples and perceives the class prototype. This will be useful for recognizing the missing qualities. By then in the wake of getting each and every missing worth, credal procedure is use for classification. The trial occurs exhibit that the enhanced type of PCC performs better similar to time and memory viability.

scholarly journals Enhanced Classification of Incomplete Pattern Using Hierarchical Clustering

2019 ◽  
pp. 594-599
Author(s):  
Gaminee Sharnagat ◽  
Pragati Patil
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Data System ◽  
Learning Approach ◽  
Specific Class ◽  
Course Of Action ◽  
Data Segment ◽  
Machine Learning Approach ◽  
The Right

More often than not values are absent in database, which ought to be managed. Missing qualities are occurred in light of the way that, the data segment individual did not know the right regard or frustration of sensors or leave the space cleanse. The course of action of missing regarded lacking case is a trying errand in machine learning approach. Divided data is not proper for classification handle. Exactly when insufficient cases are masterminded using prototype values, the last class for comparable illustrations may have distinctive results that are variable yields. We can't describe specific class for specific cases. The structure makes a wrong result which also realizes contrasting effects. So to oversee such kind of lacking data, system executes prototype-based credal classification (PCC) strategy. The PCC procedure is intertwined with Hierarchical clustering and evidential reasoning methodology to give correct, time and memory profitable outcomes. This procedure readies the examples and perceives the class prototype. This will be useful for recognizing the missing qualities. By then in the wake of getting each and every missing worth, credal procedure is use for classification. The trial occurs exhibit that the enhanced type of PCC performs better similar to time and memory viability.

Outcome of patients with recurrent hepatocellular carcinoma after liver transplantation

2010 ◽  
Vol 48 (05) ◽  
Author(s):  
A Finkenstedt ◽  
I Graziadei ◽  
H Zoller ◽  
K Nachbaur ◽  
W Mark ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Recurrent Hepatocellular Carcinoma
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