scholarly journals Predicting Bipolar Disorder Risk Factors in Distressed Young Adults From Patterns of Brain Activation to Reward: A Machine Learning Approach

2019 ◽  
Vol 4 (8) ◽  
pp. 726-733 ◽  
Author(s):  
Leticia de Oliveira ◽  
Liana C.L. Portugal ◽  
Mirtes Pereira ◽  
Henry W. Chase ◽  
Michele Bertocci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Bipolar Disorder ◽  
Young Adults ◽  
Brain Activation ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Disorder Risk

A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model

2019 ◽  
Vol 1 (1) ◽  
pp. 32-44
Author(s):  
Joseph Simonian ◽  
Chenwei Wu ◽  
Daniel Itano ◽  
Vyshaal Narayanam
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Learning Approach ◽  
Machine Learning Approach

Predicting functional impairment in bipolar disorder: a pilot study with a machine learning approach

2017 ◽  
Vol 27 ◽  
pp. S822-S823
Author(s):  
J.V. Pinto ◽  
D. Librenza-Garcia ◽  
D.P. Baldez ◽  
A.R. Rosa ◽  
F. Kapczinski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Pilot Study ◽  
Functional Impairment ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Assessment of bilateral knee pain from MR imaging using deep neural networks

2018 ◽  
Author(s):  
Gary H. Chang ◽  
David T. Felson ◽  
Shangran Qiu ◽  
Terence D. Capellini ◽  
Vijaya B. Kolachalama
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Neural Networks ◽  
Deep Learning ◽  
Knee Pain ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Mr Images ◽  
Bilateral Knee ◽  
Machine Learning Approach

ABSTRACTBackground and objectiveIt remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors.MethodsWe developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem.ResultsThe deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades.ConclusionThe study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain.SIGNIFICANCE AND INNOVATIONKnee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.

scholarly journals Machine Learning Approach Applied to the Prevalence Analysis of ADHD Symptoms in Young Adults of Barranquilla, Colombia

2020 ◽  
pp. 255-265
Author(s):  
Alexandra Leon-Jacobus ◽  
Paola Patricia Ariza-Colpas ◽  
Ernesto Barcelo-Martínez ◽  
Marlon Alberto Piñeres-Melo ◽  
Roberto Cesar Morales-Ortega ◽  
...  
Keyword(s):  
Machine Learning ◽  
Young Adults ◽  
Learning Approach ◽  
Adhd Symptoms ◽  
Machine Learning Approach

scholarly journals A machine learning approach to predict early outcomes after pituitary adenoma surgery

2018 ◽  
Vol 45 (5) ◽  
pp. E8 ◽  
Author(s):  
Todd C. Hollon ◽  
Adish Parikh ◽  
Balaji Pandian ◽  
Jamaal Tarpeh ◽  
Daniel A. Orringer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Pituitary Adenoma ◽  
Predictive Model ◽  
Odds Ratio ◽  
Postoperative Outcomes ◽  
Learning Approach ◽  
Ratio Analysis ◽  
Machine Learning Approach ◽  
The Mean

OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.METHODSA retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.RESULTSThe study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach

2018 ◽  
Vol 103 ◽  
pp. 237-243 ◽  
Author(s):  
Juliana M. Sartori ◽  
Ramiro Reckziegel ◽  
Ives Cavalcante Passos ◽  
Leticia S. Czepielewski ◽  
Adam Fijtman ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Bipolar Disorder ◽  
Magnetic Resonance ◽  
Brain Magnetic Resonance Imaging ◽  
Learning Approach ◽  
Resonance Imaging ◽  
Machine Learning Approach
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