scholarly journals Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Luis J. Mena ◽  
Eber E. Orozco ◽  
Vanessa G. Felix ◽  
Rodolfo Ostos ◽  
Jesus Melgarejo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Medical Knowledge ◽  
Learning Approach ◽  
Clinical Parameters ◽  
Machine Learning Method ◽  
Learning Method ◽  
Medical Decisions ◽  
Medical Diagnoses ◽  
Machine Learning Approach

Machine learning has become a powerful tool for analysing medical domains, assessing the importance of clinical parameters, and extracting medical knowledge for outcomes research. In this paper, we present a machine learning method for extracting diagnostic and prognostic thresholds, based on a symbolic classification algorithm called REMED. We evaluated the performance of our method by determining new prognostic thresholds for well-known and potential cardiovascular risk factors that are used to support medical decisions in the prognosis of fatal cardiovascular diseases. Our approach predicted 36% of cardiovascular deaths with 80% specificity and 75% general accuracy. The new method provides an innovative approach that might be useful to support decisions about medical diagnoses and prognoses.

scholarly journals Identification of Microplastics in Soils Using 2D Geometric Shape Descriptors

2021 ◽  
Vol 2 ◽  
pp. 1-6
Author(s):  
Irada Ismayilova ◽  
Tabea Zeyer ◽  
Sabine Timpf
Keyword(s):  
Machine Learning ◽  
Aquatic Ecosystems ◽  
Terrestrial Ecosystems ◽  
Scientific Field ◽  
Learning Approach ◽  
Machine Learning Method ◽  
Shape Descriptors ◽  
Learning Method ◽  
Soil Systems ◽  
Machine Learning Approach

Abstract. Microplastics (MP), until now mostly studied in aquatic ecosystems, are also largely polluting terrestrial ecosystems, especially soil systems. Overall, there is a lack of robust and fast methods to identify, separate and eliminate MPs from soils. This paper is a first attempt to use 2D shape descriptors and Random Forest Machine Learning method in order to discriminate soil and MP particles. The results of this study demonstrate promising potential of the Machine Learning approach and shape descriptors in this relatively new scientific field of determining MPs in soils.

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

scholarly journals Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach

2017 ◽  
Vol 9 (10) ◽  
pp. 2715-2726 ◽  
Author(s):  
Patricia Rojas Sánchez ◽  
Alberto Cobos ◽  
Marisa Navaro ◽  
José Tomas Ramos ◽  
Israel Pagán ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pediatric Patients ◽  
Learning Approach ◽  
Clinical Parameters ◽  
Subtype B ◽  
Machine Learning Approach ◽  
Hiv 1

scholarly journals Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisa Cainelli ◽  
Patrizia S. Bisiacchi ◽  
Paola Cogo ◽  
Massimo Padalino ◽  
Manuela Simonato ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Congenital Heart ◽  
Heart Diseases ◽  
Brain Maturation ◽  
Learning Approach ◽  
Clinical Parameters ◽  
Small Subgroup ◽  
Machine Learning Approach ◽  
High Interindividual Variability

AbstractWe aimed to delineate the neuropsychological and psychopathological profiles of children with congenital heart disease (CHD) and look for associations with clinical parameters. We conducted a prospective observational study in children with CHD who underwent cardiac surgery within five years of age. At least 18 months after cardiac surgery, we performed an extensive neuropsychological (intelligence, language, attention, executive function, memory, social skills) and psychopathological assessment, implementing a machine-learning approach for clustering and influencing variable classification. We examined 74 children (37 with CHD and 37 age-matched controls). Group comparisons have shown differences in many domains: intelligence, language, executive skills, and memory. From CHD questionnaires, we identified two clinical subtypes of psychopathological profiles: a small subgroup with high symptoms of psychopathology and a wider subgroup of patients with ADHD-like profiles. No associations with the considered clinical parameters were found. CHD patients are prone to high interindividual variability in neuropsychological and psychological outcomes, depending on many factors that are difficult to control and study. Unfortunately, these dysfunctions are under-recognized by clinicians. Given that brain maturation continues through childhood, providing a significant window for recovery, there is a need for a lifespan approach to optimize the outcome trajectory for patients with CHD.

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.

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