A Machine Learning Algorithm to Identify Patients with Tibial Shaft Fractures at Risk for Infection After Operative Treatment

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Operative Treatment ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tibial Shaft ◽  
Shaft Fractures ◽  
Tibial Shaft Fractures

SCREENING OF PATIENTS AT RISK FOR WILD TYPE ATTR-CM USING A COMPUTATIONAL MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 37 (10) ◽  
pp. S65
Author(s):  
C Willis ◽  
K Kawamoto ◽  
A Watanabe ◽  
J Biskupiak ◽  
K Nolen ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Wild Type ◽  
Patients At Risk

IMPLEMENTING MACHINE LEARNING ALGORITHM TO IDENTIFY PATIENTS AT RISK OF DEVELOPING ELEVATED LEFT SIDED CARDIAC FILLING PRESSURES

2018 ◽  
Vol 71 (11) ◽  
pp. A727
Author(s):  
Kinjan Patel ◽  
Marton Tokodi ◽  
Partho P. Sengupta ◽  
Ashok Runkana ◽  
Sirish Shrestha ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Patients At Risk ◽  
Cardiac Filling

scholarly journals SCREENING OF PATIENTS AT RISK FOR WILD TYPE ATTR-CM USING A COMPUTATIONAL MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 77 (18) ◽  
pp. 677
Author(s):  
Connor Willis ◽  
Kensaku Kawamoto ◽  
Alexandre Watanabe ◽  
Joseph Biskupiak ◽  
Kim Nolen ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Wild Type ◽  
Patients At Risk

Machine Learning Algorithm Based Bandwidth Prediction for Internet Usage

2020 ◽  
Vol 17 (8) ◽  
pp. 3749-3753
Author(s):  
J. Rajaram ◽  
M. Nalini ◽  
N. Vadivelan
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Arima Model ◽  
Internet Usage ◽  
Machine Learning Algorithm ◽  
Framework Structure ◽  
The Past ◽  
Basic Activity ◽  
Time Plan

The applicability of framework structure and affiliation arranging recognize a basic activity in the bandwidth prediction. The procedure for predicting the framework use is to see the basic transmission limit with respect to future periods. This prediction helps with utilizing the techniques workplaces in the saint way. Thinking about the fundamental cost of bandwidth, at top hours of a framework traffic we can follow an amazing sort of plan to purchase. In this paper, the past use data of FWDR organize centers is at risk to univariate direct time plan ARIMA model after precise change is used to calculate necessary bandwidth limit concerning future needs. The anticipated data is veered from the obvious data gained from a for all intents and purposes indistinguishable framework and the foreseen data has been viewed as inside ten percent MAPE. This design reduction the MAPE by eleven point seventy-one percentage and fifteen point forty-two percent of self-rulingly when stood separated from the non-able changed ARIMA model at ninety-nine percent CI. The outcome show that the suitably changed ARIMA design has improved show when meandered from non-intentionally changed ARIMA model. Increasingly significant dataset can be passed on with season alterations and thought of expanded length groupings, for dynamically unequivocal and longer term needs.

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