A Machine Learning Approach for Postoperative Outcome Prediction: Surgical Data Science Application in a Thoracic Surgery Setting

2021 ◽  
Vol 45 (5) ◽  
pp. 1585-1594
Author(s):  
Michele Salati ◽  
Lucia Migliorelli ◽  
Sara Moccia ◽  
Marco Andolfi ◽  
Alberto Roncon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thoracic Surgery ◽  
Data Science ◽  
Outcome Prediction ◽  
Postoperative Outcome ◽  
Learning Approach ◽  
Surgical Data Science ◽  
Machine Learning Approach ◽  
Surgical Data ◽  
Science Application

Classification of different skarn deposits based on the compositional variability of associated grandite garnets: a data science and Machine Learning approach

2021 ◽  
Author(s):  
Urmi Ghosh ◽  
Tuhin Chakraborty
Keyword(s):  
Machine Learning ◽  
Trace Element ◽  
Data Science ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Skarn Deposits

<p>Rapid technological improvements made in in-situ analysis techniques, including LA-ICPMS, have transformed the field of analytical geochemistry. This has a far-reaching impact for different petrogenetic and ore-genetic studies where minute major and trace element compositional changes between different mineral zones within a single crystal can now be demarcated. Minerals such as garnet although robust are quite sensitive to the changing P-T and fluid conditions during their formation. These minerals have become powerful tools to characterize mineralization types. Previously, Meinert (1992) has used in-situ major element EPMA analysis results to classify different skarn deposit based on the end-member composition of hydrothermal garnets. Alternatively, Tian et al. (2019) used the garnet trace element composition for the similar purpose. However, these discrimination plots/ classification schemes show major overlap in different skarn deposits, such as Fe, Cu, Zn, and Au. The present study is an attempt to use machine learning approach on available garnet data to found a more potent classification scheme for skarn deposits, thus reaffirming garnet as a faithful indicator for hydrothermal ore deposits. We have meticulously collected major and trace element data of Ca-rich garnets, associated with different skarn deposits worldwide from 40 publications. This collected data is then used to train a model for fingerprinting the skarn deposits. Stratified random sampling method has been used on the dataset with 80% of the samples as test set and the rest 20 % as training dataset. We have used K-nearest neighbour (KNN), Support Vector Machine (SVM) and Random Forest algorithms on the data by using Python as a platform. These ML classification algorithm performs better than the earlier existing models available for classification of ore types based on garnet composition in skarn system. Factor importance is calculated that shows which elements play a pivotal role in classification of the ore type. Our results depict that multiple garnet forming elements taken together can reliably be used to discriminate between different ore formation settings.</p>

scholarly journals O9.7. INDIVIDUALIZED LONG-TERM OUTCOME PREDICTION OF PSYCHOSIS IN AN OBSERVATIONAL STUDY: A MACHINE LEARNING APPROACH

2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S101-S102 ◽  
Author(s):  
Jessica De Nijs ◽  
Daniel P J van Opstal ◽  
Ronald J Janssen ◽  
Wiepke Cahn ◽  
Hugo Schnack ◽  
...  
Keyword(s):  
Machine Learning ◽  
Observational Study ◽  
Outcome Prediction ◽  
Learning Approach ◽  
Term Outcome ◽  
Long Term Outcome ◽  
Machine Learning Approach

scholarly journals Machine Learning Approach using CNN for COVID-19 Pandemic Detection

2021 ◽  
Vol 11 (2) ◽  
pp. 63-67
Author(s):  
Dr. Vikas S ◽  
◽  
Dr. Thimmaraju S N ◽  
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Data Science ◽  
Corrective Action ◽  
Learning Approach ◽  
Data Generation ◽  
X Ray ◽  
Domain Names ◽  
Machine Learning Approach ◽  
Chest X Ray

Data science and machine learning are domain names in which data generation can assist with inside the fight towards the disease. Early caution systems which can are expecting how much a disease might effect society and permit the authorities to take suitable measures without disrupting the economy are extremely important. In the confrontation towards COVID-19 methods for forecasting the future cases primarily based totally on present data are extremely beneficial. The preceding are three strategies of machine learning which are discussed: Two for predicting the wide variety of positive cases in the coming ten days, and one for identifying COVID-19 infection via way of means of analyzing the patient's chest x-ray image. Various algorithms had been tested, and the only that produced the maximum accurate consequences become selected for use on this take a look at to forecast confirmed cases in India. Various government entities can leverage the findings to take corrective action. Now that methods for forecasting infectious disease are available, COVID-19 can be less complicated to combat.

scholarly journals Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e62819 ◽  
Author(s):  
Rubén Armañanzas ◽  
Lidia Alonso-Nanclares ◽  
Jesús DeFelipe-Oroquieta ◽  
Asta Kastanauskaite ◽  
Rafael G. de Sola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Epilepsy Surgery ◽  
Outcome Prediction ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Prediction of Casing Damage: A Data-Driven, Machine Learning Approach

2021 ◽  
Vol 14 ◽  
pp. 1046-1053
Author(s):  
Yanhong Zhao ◽  
Hanqiao Jiang ◽  
Hongqi Li
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Data Driven ◽  
Learning Approach ◽  
Damage Prediction ◽  
Number Of Factors ◽  
Static Data ◽  
Casing Damage ◽  
Machine Learning Approach ◽  
And Performance

Casing damage is the result of a number of factors in the long process of oilfield development, so it must be correctly judged and repaired in time to ensure the normal production of the oil fields. With the development of data science, it has always been an imperative problem remained to be solved. In this paper, we adopt a data-driven and the machine learning approach to casing damage forecasts. Firstly, from the fields of geology, engineering and development, a lot of history data is collected and processed. Then, based on these dynamic and static data samples, the random forest algorithm is used to create the casing damage prediction model. Finally, after the model is tested in two fault blocks, the results indicate that accuracy rates are 91% and 75%, which proves the validity and performance of the mode.

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