scholarly journals P09.14 Predictive Analytics in Real-World Data from Peru: The New Models for Personalized Oncology

2021 ◽  
Vol 16 (3) ◽  
pp. S294
Author(s):  
L. Pino ◽  
I. Triana ◽  
J. Mejia ◽  
M. Camelo ◽  
M. Galvez-Nino ◽  
...  
2017 ◽  
Vol 19 (1) ◽  
pp. 127-139 ◽  
Author(s):  
Jun Li ◽  
Eric M. Simmons ◽  
Martin D. Eastgate

A predictive analytics approach to understanding process mass intensity (PMI) is described. This method leverages real-world data to predict probable PMI outcomes for a potential synthetic route and to compare PMI outcomes to the summation of prior experience.


2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  

2020 ◽  
Author(s):  
Jersy Cardenas ◽  
Gomez Nancy Sanchez ◽  
Sierra Poyatos Roberto Miguel ◽  
Luca Bogdana Luiza ◽  
Mostoles Naiara Modroño ◽  
...  

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 209-OR
Author(s):  
SHWETA GOPALAKRISHNAN ◽  
PRATIK AGRAWAL ◽  
MICHAEL STONE ◽  
CATHERINE FOGEL ◽  
SCOTT W. LEE

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 994-P
Author(s):  
PRATIK AGRAWAL ◽  
MICHAEL STONE ◽  
SHWETA GOPALAKRISHNAN ◽  
CATHERINE FOGEL ◽  
SCOTT W. LEE ◽  
...  

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1043-P
Author(s):  
JENNIFER E. LAYNE ◽  
JIALUN HE ◽  
JAY JANTZ ◽  
YIBIN ZHENG ◽  
ERIC BENJAMIN ◽  
...  

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