A Novel Learning Algorithm to Optimize Deep Neural Networks: Evolved Gradient Direction Optimizer (EVGO)

Gradient-based algorithms have been widely used in optimizing parameters of deep neural networks' (DNNs) architectures. However, the vanishing gradient remains as one of the common issues in the parameter optimization of such networks. To cope with the vanishing gradient problem, in this article, we propose a novel algorithm, evolved gradient direction optimizer (EVGO), updating the weights of DNNs based on the first-order gradient and a novel hyperplane we introduce. We compare the EVGO algorithm with other gradient-based algorithms, such as gradient descent, RMSProp, Adagrad, momentum, and Adam on the well-known Modified National Institute of Standards and Technology (MNIST) data set for handwritten digit recognition by implementing deep convolutional neural networks. Furthermore, we present empirical evaluations of EVGO on the CIFAR-10 and CIFAR-100 data sets by using the well-known AlexNet and ResNet architectures. Finally, we implement an empirical analysis for EVGO and other algorithms to investigate the behavior of the loss functions. The results show that EVGO outperforms all the algorithms in comparison for all experiments. We conclude that EVGO can be used effectively in the optimization of DNNs, and also, the proposed hyperplane may provide a basis for future optimization algorithms.WOS:000616310400018PubMed: 3248122

Gradient boosting for Parkinson's disease diagnosis from voice recordings

Background Parkinson's Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accuracy using non-invasive and inexpensive voice recordings. Method We used Parkinson Dataset with Replicated Acoustic Features Data Set from the UCI-Machine Learning repository. The dataset included 44 speech-test based acoustic features from patients with PD and controls. We analyzed the data using various machine learning algorithms including Light and Extreme Gradient Boosting, Random Forest, Support Vector Machines, K-nearest neighborhood, Least Absolute Shrinkage and Selection Operator Regression, as well as logistic regression. We also implemented a variable importance analysis to identify important variables classifying patients with PD. Results The cohort included a total of 80 subjects: 40 patients with PD (55% men) and 40 controls (67.5% men). Disease duration was 5 years or less for all subjects, with a mean Unified Parkinson's Disease Rating Scale (UPDRS) score of 19.6 (SD 8.1), and none were taking PD medication. The mean age for PD subjects and controls was 69.6 (SD 7.8) and 66.4 (SD 8.4), respectively. Our best-performing model used Light Gradient Boosting to provide an AUC of 0.951 with 95% confidence interval 0.946-0.955 in 4-fold cross validation using only seven acoustic features. Conclusions Machine learning can accurately detect Parkinson's disease using an inexpensive and non-invasive voice recording. Light Gradient Boosting outperformed other machine learning algorithms. Such approaches could be used to inexpensively screen large patient populations for Parkinson's disease.WOS:0005732847000042-s2.0-85091053141PubMed: 3293349

Application of machine learning to the prediction of postoperative sepsis after appendectomy

Background: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these patients. Methods: The National Surgery Quality Improvement Program database was used to identify patients undergoing appendectomy between 2005 and 2017. Logistic regression, support vector machines, random forest decision trees, and extreme gradient boosting machines were used to model the occurrence of postoperative sepsis. Results: In the study, 223,214 appendectomies were identified; 2,143 (0.96%) were indicated as having postoperative sepsis. Logistic regression (area under the curve 0.70; 95% confidence interval, 0.68-0.73), random forest decision trees (area under the curve 0.70; 95% confidence interval, 0.68-0.73), and extreme gradient boosting (area under the curve 0.70; 95% confidence interval, 0.68-0.73) afforded similar performance, while support vector machines (area under the curve 0.51; 95% confidence interval, 0.50-0.52) had worse performance. Variable importance analyses identified preoperative congestive heart failure, transfusion, and acute renal failure as predictors of postoperative sepsis. Conclusion: Machine learning methods can be used to predict the development of sepsis after appendectomy with moderate accuracy. Such predictive modeling has potential to ultimately allow for preoperative recognition of patients at risk for developing postoperative sepsis after appendectomy thus facilitating early intervention and reducing morbidity. (c) 2020 Elsevier Inc. All rights reserved.National Institute of Health T32 NIGMS [5T32GM008750-20]; National Institute of Health T32 NIAAA [5T32AA013527-17]Dr C. Bunn is supported by National Institute of Health T32 NIGMS 5T32GM008750-20. Dr S. Kulshrestha is supported by National Institute of Health T32 NIAAA 5T32AA013527-17.WOS:0006165864000312-s2.0-85091249604PubMed: 3295190

Machine learning analysis on American Gut Project microbiome data to identify subjects with cancer both with and without chemotherapy exposure.

Annual Meeting of the American-Society-of-Clinical-Oncology (ASCO) -- MAY 29-31, 2020 -- ELECTR NETWORK -- Amer Soc Clin Oncol[Abstract Not Available]WOS:00056036830448

Machine Learning to Identify Dialysis Patients at High Death Risk

Introduction: Given the high mortality rate within the first year of dialysis initiation, an accurate estimation of postdialysis mortality could help patients and clinicians in decision making about initiation of dialysis. We aimed to use machine learning (ML) by incorporating complex information from electronic health records to predict patients at risk for postdialysis short-term mortality. Methods: This study was carried out on a contemporary cohort of 27,615 US veterans with incident end-stage renal disease (ESRD). We implemented a random forest method on 49 variables obtained before dialysis transition to predict outcomes of 30-, 90-, 180-, and 365-day all-cause mortality after dialysis initiation. Results: The mean (+/- SD) age of our cohort was 68.7 +/- 11.2 years, 98.1% of patients were men, 29.4% were African American, and 71.4% were diabetic. The final random forest model provided C-statistics (95% confidence intervals) of 0.7185 (0.6994-0.7377), 0.7446 (0.7346-0.7546), 0.7504 (0.7425-0.7583), and 0.7488 (0.7421-0.7554) for predicting risk of death within the 4 different time windows. The models showed good internal validity and replicated well in patients with various demographic and clinical characteristics and provided similar or better performance compared with other ML algorithms. Results may not be generalizable to non-veterans. Use of predictors available in electronic medical records has limited the assessment of number of predictors. Conclusion: We implemented and ML-based method to accurately predict short-term postdialysis mortality in patients with incident ESRD. Our models could aid patients and clinicians in better decision making about the best course of action in patients approaching ESRD.National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [U01-DK102163]; VA Information Resource Center [SDR 02-237, SDR 98-004]; NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASESUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK) [U01DK102163] Funding Source: NIH RePORTERThis study is supported by grant U01-DK102163 from the National Institutes of Health to KKZ and CK and by resources from the US Department of Veterans Affairs (VA). The data reported here have been supplied in part by the US Renal Data System (USRDS). Support for VA/Centers for Medicare and Medicaid Services (CMS) data is provided by the Veterans Health Administration, Office of Research and Development, Health Services Research and Development, and VA Information Resource Center (project numbers SDR 02-237 and 98-004).WOS:0004843840000042-s2.0-85070215161PubMed: 3151714

Fungi form interkingdom microbial communities in the primordial human gut that develop with gestational age

Fungal and bacterial commensal organisms play a complex role in the health of the human host. Expansion of commensal ecology after birth is a critical period in human immune development. However, the initial fungal colonization of the primordial gut remains undescribed. To investigate primordial fungal ecology, we performed amplicon sequencing and culture based techniques of first-pass meconium, which forms in the intestine prior to birth, from a prospective observational cohort of term and preterm newborns. Here, we describe fungal ecologies in the primordial gut that develop complexity with advancing gestational age at birth. Our findings suggest homeostasis of fungal commensals may represent an important aspect of human biology present even before birth. Unlike bacterial communities that gradually develop complexity, the domination of the fungal communities of some preterm infants by Saccromycetes, specifically Candida, may suggest a pathologic association with preterm birth.WOS:0005074616000952-s2.0-85074377411PubMed: 3148090