Abstract WMP26: Using Machine Learning Pattern Recognition in Aneurysmal Subarachnoid Hemorrhage Patients to Determine Patterns in Central versus Infectious Fever

Stroke ◽  
2019 ◽  
Vol 50 (Suppl_1) ◽  
Author(s):  
Judy Ch'ang ◽  
Nate Tran ◽  
Catherine DeVoe ◽  
Xiao Hu
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Subarachnoid Hemorrhage ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Learning Pattern

Reaping the benefits of machine learning pattern recognition in nanophotonic component design

2019 ◽  
Author(s):  
Yuri Grinberg ◽  
Daniele Melati ◽  
Mohsen Kamandar Dezfouli ◽  
Siegfried Janz ◽  
Jens Schmid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Pattern ◽  
Component Design

Design of multi-parameter photonic devices using machine learning pattern recognition

2020 ◽  
Author(s):  
Daniele Melati ◽  
Yuri Grinberg ◽  
Mohsen Kamandar Dezfouli ◽  
Jens H. Schmid ◽  
Pavel Cheben ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Photonic Devices ◽  
Learning Pattern

Development of a Complication- and Treatment-Aware Prediction Model for Favorable Functional Outcome in Aneurysmal Subarachnoid Hemorrhage Based on Machine Learning

Neurosurgery ◽  
2020 ◽  
Author(s):  
Nicolai Maldaner ◽  
Anna M Zeitlberger ◽  
Marketa Sosnova ◽  
Johannes Goldberg ◽  
Christian Fung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Subarachnoid Hemorrhage ◽  
Functional Outcome ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Early Model ◽  
Test Dataset ◽  
Wide Range ◽  
Favorable Functional Outcome

Abstract BACKGROUND Current prognostic tools in aneurysmal subarachnoid hemorrhage (aSAH) are constrained by being primarily based on patient and disease characteristics on admission. OBJECTIVE To develop and validate a complication- and treatment-aware outcome prediction tool in aSAH. METHODS This cohort study included data from an ongoing prospective nationwide multicenter registry on all aSAH patients in Switzerland (Swiss SOS [Swiss Study on aSAH]; 2009-2015). We trained supervised machine learning algorithms to predict a binary outcome at discharge (modified Rankin scale [mRS] ≤ 3: favorable; mRS 4-6: unfavorable). Clinical and radiological variables on admission (“Early” Model) as well as additional variables regarding secondary complications and disease management (“Late” Model) were used. Performance of both models was assessed by classification performance metrics on an out-of-sample test dataset. RESULTS Favorable functional outcome at discharge was observed in 1156 (62.0%) of 1866 patients. Both models scored a high accuracy of 75% to 76% on the test set. The “Late” outcome model outperformed the “Early” model with an area under the receiver operator characteristics curve (AUC) of 0.85 vs 0.79, corresponding to a specificity of 0.81 vs 0.70 and a sensitivity of 0.71 vs 0.79, respectively. CONCLUSION Both machine learning models show good discrimination and calibration confirmed on application to an internal test dataset of patients with a wide range of disease severity treated in different institutions within a nationwide registry. Our study indicates that the inclusion of variables reflecting the clinical course of the patient may lead to outcome predictions with superior predictive power compared to a model based on admission data only.

Neuroimaging of bipolar disorder

2020 ◽  
pp. 744-756
Author(s):  
Mary L Phillips ◽  
Wayne C Drevets
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Bipolar Disorder ◽  
Pattern Recognition ◽  
Psychiatric Disorders ◽  
Multimodal Neuroimaging ◽  
Protein Marker ◽  
Pattern Recognition Techniques ◽  
Learning Pattern ◽  
Youth At Risk

This chapter discusses findings from recent major neuroimaging studies of bipolar disorder to provide a better understanding of larger-scale neural circuitry, neurotransmitter concentration, bioenergetic process, and protein marker abnormalities in the disorder. The chapter also reviews findings from newer areas of neuroimaging research, including studies comparing bipolar disorder with other major psychiatric disorders, multimodal neuroimaging studies, studies of youth with, and youth at risk for, the disorder, and studies using machine-learning pattern recognition techniques. These studies are paving the way for identification of robust and objective neural biomarkers of bipolar disorder that can ultimately have clinical utility.

scholarly journals Multivariable and Bayesian Network Analysis of Outcome Predictors in Acute Aneurysmal Subarachnoid Hemorrhage: Review of a Pure Surgical Series in the Post-International Subarachnoid Aneurysm Trial Era

2017 ◽  
Vol 14 (6) ◽  
pp. 603-610 ◽  
Author(s):  
Zsolt Zador ◽  
Wendy Huang ◽  
Matthew Sperrin ◽  
Michael T Lawton
Keyword(s):  
Machine Learning ◽  
Subarachnoid Hemorrhage ◽  
Bayesian Networks ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Case Series ◽  
Variable Importance ◽  
Outcome Predictors ◽  
Surgical Clipping ◽  
International Subarachnoid Aneurysm Trial ◽  
Importance Ranking

AbstractBACKGROUNDFollowing the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping.OBJECTIVETo update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning.METHODSWe reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning.RESULTSImportance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters.CONCLUSIONOur study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.

scholarly journals Robust Procedures for Estimating and Testing in the Framework of Divergence Measures

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 430
Author(s):  
Leandro Pardo ◽  
Nirian Martín
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Pattern ◽  
Divergence Measures ◽  
Robust Procedures

The approach for estimating and testing based on divergence measures has become, in the last 30 years, a very popular technique not only in the field of statistics, but also in other areas, such as machine learning, pattern recognition, etc [...]

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