Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models

2021 ◽  
pp. 197140092199076
Author(s):  
Sarv Priya ◽  
Amit Agarwal ◽  
Caitlin Ward ◽  
Thomas Locke ◽  
Varun Monga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Texture Analysis ◽  
Diagnostic Performance ◽  
Survival Prediction ◽  
Learning Models ◽  
Post Contrast ◽  
Machine Learning Classification ◽  
First Order ◽  
Machine Learning Models

Objective Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis. Methods We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Results The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4. Conclusions First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.

scholarly journals Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

2019 ◽  
Vol 9 (4) ◽  
pp. 45 ◽  
Author(s):  
Hugo F. Posada-Quintero ◽  
Jeffrey B. Bolkhovsky
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Memory Task ◽  
Autonomic Response ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Classification ◽  
Machine Learning Models

Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS). Electrocardiogram (ECG) (for HRV analysis) and EDA data were collected during the tests. For task-classification, we tested four machine learning classification tools: k-nearest neighbor classifier (KNN), support vector machines (SVM), decision trees, and discriminant analysis (DA). Leave-one-subject-out cross-validation was used to evaluate the performance of the constructed models to prevent overfitting. The most accurate models were the KNN (66%), linear SVM (62%), and linear DA (62%). The results of this study showed that it is possible to identify the task a subject is performing based on the subject’s autonomic reactions (from HRV and EDA). This information can be used to monitor individuals within a larger group to assist in reducing errors caused by uncoordinated or poor performance by allowing for automated tracking of and communication between individuals.

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

Leukemia ◽  
2021 ◽  
Author(s):  
Adrián Mosquera Orgueira ◽  
Marta Sonia González Pérez ◽  
José Ángel Díaz Arias ◽  
Beatriz Antelo Rodríguez ◽  
Natalia Alonso Vence ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Multiple Myeloma ◽  
Gene Expression Data ◽  
Survival Prediction ◽  
Expression Data ◽  
Learning Models ◽  
Treatment Optimization ◽  
Machine Learning Models

scholarly journals Machine Learning Models Correct Systematic Errors in Alchemical Perturbation Density Functional Theory Applications to Catalysis

2020 ◽  
Author(s):  
Charles D. Griego ◽  
Lingyan Zhao ◽  
Karthikeyan Saravanan ◽  
John Keith
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Density Functional ◽  
Binding Energies ◽  
Great Promise ◽  
Support Vector ◽  
Learning Models ◽  
Functional Theory ◽  
First Order ◽  
Machine Learning Models

Alchemical perturbation density functional theory (APDFT) has great promise for enabling rapid and accurate computational screening of hypothetical catalyst sites, but first order approximations are unsatisfactorily inaccurate when alchemical derivatives are large. In this work, we analyze errors in first order APDFT calculation schemes for binding energies of CHx, NHx, OHx, and OOH adsorbates over a range of different coverages on hypothetical alloys based on a Pt(111) reference system. We then construct feature vectors by fingerprinting the dopant locations in the alloy and then use a data set of about 11,100 data points to train three different support vector regression machine learning models that correct systematic APDFT prediction errors for each of the three classes of carbon, nitrogen, and oxygen based adsorbates. While uncorrected first order APDFT alone can approximate reasonably accurate adsorbate binding energies on up to 36 hypothetical alloys based on a single Kohn-Sham DFT calculation on a 3 × 3 unit cell for Pt(111), the machine learning-corrected APDFT in principle extends this number to more than 20,000 and provides a recipe for developing other machine learning models to aid future high throughput screening studies.

scholarly journals Development of a machine-learning model to assess terminal ileum Endoscopic healing in pediatric Crohn's disease from Magnetic Resonance Enterography data

2021 ◽  
Author(s):  
Itai Guez ◽  
Gili Focht ◽  
Mary-Louise C.Greer ◽  
Ruth Cytter-Kuint ◽  
Li-tal Pratt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Linear Regression ◽  
Regression Models ◽  
Magnetic Resonance Enterography ◽  
Linear Regression Models ◽  
Learning Models ◽  
Machine Learning Model ◽  
Relevant Variables ◽  
Machine Learning Models

Background and Aims: Endoscopic healing (EH), is a major treatment goal for Crohn's disease(CD). However, terminal ileum (TI) intubation failure is common, especially in children. We evaluated the added-value of machine-learning models in imputing a TI Simple Endoscopic Score for CD (SES-CD) from Magnetic Resonance Enterography (MRE) data of pediatric CD patients. Methods: This is a sub-study of the prospective ImageKids study. We developed machine-learning and baseline linear-regression models to predict TI SES-CD score from the Magnetic Resonance Index of Activity (MaRIA) and the Pediatric Inflammatory Crohn's MRE Index (PICMI) variables. We assessed TI SES-CD predictions' accuracy for intubated patients with a stratified 2-fold validation experimental setup, repeated 50 times. We determined clinical impact by imputing TI SES-CD in patients with ileal intubation failure during ileocolonscopy. Results: A total of 223 children were included (mean age 14.1+-2.5 years), of whom 132 had all relevant variables (107 with TI intubation and 25 with TI intubation failure). The combination of a machine-learning model with the PICMI variables achieved the lowest SES-CD prediction error compared to a baseline MaRIA-based linear regression model for the intubated patients (N=107, 11.7 (10.5-12.5) vs. 12.1 (11.4-12.9), p<0.05). The PICMI-based models suggested a higher rate of patients with TI disease among the non-intubated patients compared to a baseline MaRIA-based linear regression model (N=25, up to 25/25 (100%) vs. 23/25 (92%)). Conclusions: Machine-learning models with clinically-relevant variables as input are more accurate than linear-regression models in predicting TI SES-CD and EH when using the same MRE-based variables.

Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics

2020 ◽  
Vol 45 (9) ◽  
pp. 2797-2809 ◽  
Author(s):  
Camila Lopes Vendrami ◽  
Robert J. McCarthy ◽  
Carolina Parada Villavicencio ◽  
Frank H. Miller
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Renal Tumors ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Transparent Machine Learning models for Rapid Risk Stratification in the Emergency Department: A multi-center evaluation

2020 ◽  
Author(s):  
William P.T.M. van Doorn ◽  
Floris Helmich ◽  
Paul M.E.L. van Dam ◽  
Leo H.J. Jacobs ◽  
Patricia M. Stassen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Risk Stratification ◽  
Diagnostic Performance ◽  
Medical Center ◽  
Laboratory Data ◽  
Patient Specific ◽  
Learning Models ◽  
Laboratory Results ◽  
Machine Learning Models

AbstractIntroductionRisk stratification of patients presenting to the emergency department (ED) is important for appropriate triage. Using machine learning technology, we can integrate laboratory data from a modern emergency department and present these in relation to clinically relevant endpoints for risk stratification. In this study, we developed and evaluated transparent machine learning models in four large hospitals in the Netherlands.MethodsHistorical laboratory data (2013-2018) available within the first two hours after presentation to the ED of Maastricht University Medical Centre+ (Maastricht), Meander Medical Center (Amersfoort), and Zuyderland (locations Sittard and Heerlen) were used. We used the first five years of data to develop the model and the sixth year to evaluate model performance in each hospital separately. Performance was assessed using area under the receiver-operating-characteristic curve (AUROC), brier scores and calibration curves. The SHapley Additive exPlanations (SHAP) algorithm was used to obtain transparent machine learning models.ResultsWe included 266,327 patients with more than 7 million laboratory results available for analysis. Models possessed high diagnostic performance with AUROCs of 0.94 [0.94-0.95], 0.98 [0.97-0.98], 0.88 [0.87-0.89] and 0.90 [0.89-0.91] for Maastricht, Amersfoort, Sittard and Heerlen, respectively. Using the SHAP algorithm, we visualized patient characteristics and laboratory results that drive patient-specific RISKINDEX predictions. As an illustrative example, we applied our models in a triage system for risk stratification that categorized 94.7% of the patients as low risk with a corresponding NPV of ≥99%.DiscussionDeveloped machine learning models are transparent with excellent diagnostic performance in predicting 31-day mortality in ED patients across four hospitals. Follow up studies will assess whether implementation of these algorithm can improve clinically relevant endpoints.

scholarly journals Abductive Learning with Ground Knowledge Base

2021 ◽  
Author(s):  
Le-Wen Cai ◽  
Wang-Zhou Dai ◽  
Yu-Xuan Huang ◽  
Yu-Feng Li ◽  
Stephen Muggleton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Domain Knowledge ◽  
Ground Truth ◽  
Order Logic ◽  
Abductive Reasoning ◽  
First Order Logic ◽  
Learning Models ◽  
First Order ◽  
Machine Learning Models

Abductive Learning is a framework that combines machine learning with first-order logical reasoning. It allows machine learning models to exploit complex symbolic domain knowledge represented by first-order logic rules. However, it is challenging to obtain or express the ground-truth domain knowledge explicitly as first-order logic rules in many applications. The only accessible knowledge base is implicitly represented by groundings, i.e., propositions or atomic formulas without variables. This paper proposes Grounded Abductive Learning (GABL) to enhance machine learning models with abductive reasoning in a ground domain knowledge base, which offers inexact supervision through a set of logic propositions. We apply GABL on two weakly supervised learning problems and found that the model's initial accuracy plays a crucial role in learning. The results on a real-world OCR task show that GABL can significantly reduce the effort of data labeling than the compared methods.

scholarly journals Machine learning models in breast cancer survival prediction

2016 ◽  
Vol 24 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Mitra Montazeri ◽  
Mohadeseh Montazeri ◽  
Mahdieh Montazeri ◽  
Amin Beigzadeh
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Learning Models ◽  
Machine Learning Models
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