A fusion decision system to identify and grade malnutrition in cancer patients: machine learning reveals feasible workflow from representative real-world data

2021 ◽  
Author(s):  
Liangyu Yin ◽  
Chunhua Song ◽  
Jiuwei Cui ◽  
Xin Lin ◽  
Na Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Real World ◽  
Decision System ◽  
Real World Data ◽  
World Data

scholarly journals Real-World Data Analysis of Confirmatory Biopsy Utilization in Suspected Prostate Cancer Patients

2018 ◽  
Vol 21 ◽  
pp. S161
Author(s):  
J Scott ◽  
R Concepcion ◽  
D Garofalo ◽  
S Verma-Kurvari ◽  
B Xu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Data Analysis ◽  
Cancer Patients ◽  
Real World ◽  
Real World Data ◽  
World Data

scholarly journals 765P Predicting survival in urothelial cancer patients after immunotherapy using real-world data

2020 ◽  
Vol 31 ◽  
pp. S590
Author(s):  
T. Elumalai ◽  
C. Aversa ◽  
B. Buijtenhuijs ◽  
R. Conroy ◽  
W. Croxford ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Real World ◽  
Urothelial Cancer ◽  
Real World Data ◽  
World Data

scholarly journals 1756P Cancer patients infected with COVID-19 at La Princesa Hospital: Real-world data study

2020 ◽  
Vol 31 ◽  
pp. S1023
Author(s):  
P. Toquero Diez ◽  
B. Vera Cea ◽  
A. Garrido Garcia ◽  
E.R. Méndez Carrascosa ◽  
D. Bañón Torres ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Real World ◽  
Real World Data ◽  
World Data

scholarly journals Developing a machine learning environmental allergy prediction model from real world data through a novel decentralized mobile study platform.

2020 ◽  
Author(s):  
Chethan Sarabu ◽  
Sandra Steyaert ◽  
Nirav Shah
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Predictive Models ◽  
Clinical Care ◽  
Sensor Data ◽  
Real World Data ◽  
World Data ◽  
Demographic Groups ◽  
Prediction And Prevention ◽  
Wide Range

Environmental allergies cause significant morbidity across a wide range of demographic groups. This morbidity could be mitigated through individualized predictive models capable of guiding personalized preventive measures. We developed a predictive model by integrating smartphone sensor data with symptom diaries maintained by patients. The machine learning model was found to be highly predictive, with an accuracy of 0.801. Such models based on real-world data can guide clinical care for patients and providers, reduce the economic burden of uncontrolled allergies, and set the stage for subsequent research pursuing allergy prediction and prevention. Moreover, this study offers proof-of-principle regarding the feasibility of building clinically useful predictive models from 'messy,' participant derived real-world data.

scholarly journals An application of machine learning based on real-world data: Mining features of fibrinogen in clinical stages of lung cancer between sexes

2021 ◽  
Vol 9 (8) ◽  
pp. 623-623
Author(s):  
Fangtao Yin ◽  
Hongyu Zhu ◽  
Songlin Hong ◽  
Chen Sun ◽  
Jie Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Clinical Stages
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