A novel approach for classification of mental tasks using multiview ensemble learning (MEL)

2020 ◽  
Vol 417 ◽  
pp. 558-584 ◽  
Author(s):  
A. Gupta ◽  
R.U. Khan ◽  
V.K. Singh ◽  
M. Tanveer ◽  
D. Kumar ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Novel Approach ◽  
Mental Tasks

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

scholarly journals Phenotypic homogeneity in childhood epilepsies evolves in gene-specific patterns across 3251 patient-years of clinical data

2021 ◽  
Author(s):  
David Lewis-Smith ◽  
Shiva Ganesan ◽  
Peter D. Galer ◽  
Katherine L. Helbig ◽  
Sarah E. McKeown ◽  
...  
Keyword(s):  
Clinical Data ◽  
Similarity Analysis ◽  
Genetic Studies ◽  
Human Phenotype ◽  
Phenotypic Similarity ◽  
Complex Phenotypes ◽  
Cognitive Framework ◽  
Novel Approach ◽  
Genetic Epilepsies

AbstractWhile genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies. After mapping clinical data to the Human Phenotype Ontology, we determined the phenotypic similarity of individuals sharing each genetic etiology within each 3-month age interval from birth up to a maximum age of 25 years. 140 of 600 (23%) of all 27 genes and 3-month age intervals with sufficient data for calculation of phenotypic similarity were significantly higher than expect by chance. 11 of 27 genetic etiologies had significant overall phenotypic similarity trajectories. These do not simply reflect strong statistical associations with single phenotypic features but appear to emerge from complex clinical constellations of features that may not be strongly associated individually. As an attempt to reconstruct the cognitive framework of syndrome recognition in clinical practice, longitudinal phenotypic similarity analysis extends the traditional phenotyping approach by utilizing data from electronic medical records at a scale that is far beyond the capabilities of manual phenotyping. Delineation of how the phenotypic homogeneity of genetic epilepsies varies with age could improve the phenotypic classification of these disorders, the accuracy of prognostic counseling, and by providing historical control data, the design and interpretation of precision clinical trials in rare diseases.

scholarly journals A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carlo Donadio ◽  
Massimo Brescia ◽  
Alessia Riccardo ◽  
Giuseppe Angora ◽  
Michele Delli Veneri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Morphological Variations ◽  
Control Factors ◽  
Novel Approach ◽  
Drainage Networks ◽  
Key Characteristics ◽  
Intrinsic Complexity ◽  
Solar System Bodies ◽  
Manual Extraction

AbstractSeveral approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.

Advances in the Biology and Genetics of the Podocytopathies: Implications for Diagnosis and Therapy

2009 ◽  
Vol 133 (2) ◽  
pp. 201-216 ◽  
Author(s):  
Laura Barisoni ◽  
H. William Schnaper ◽  
Jeffrey B. Kopp
Keyword(s):  
Current Knowledge ◽  
Diffuse Mesangial Sclerosis ◽  
Therapeutic Approaches ◽  
Glomerular Diseases ◽  
Podocyte Injury ◽  
Collapsing Glomerulopathy ◽  
Novel Approach ◽  
Genes Encoding ◽  
Glomerular Morphology

AbstractContext.—Etiologic factors and pathways leading to altered podocyte phenotype are clearly numerous and involve the activity of different cellular function.Objective.—To focus on recent discoveries in podocyte biology and genetics and their relevance to these human glomerular diseases, named podocytopathies.Data Sources.—Genetic mutations in genes encoding for proteins in the nucleus, slit diaphragm, podocyte cytoplasm, and cell membrane are responsible for podocyte phenotype and functional abnormalities. Podocyte injury may also derive from secondary stimuli, such as mechanical stress, infections, or use of certain medications. Podocytes can respond to injury in a limited number of ways, which include (1) effacement, (2) apoptosis, (3) arrest of development, and (4) dedifferentiation. Each of these pathways results in a specific glomerular morphology: minimal change nephropathy, focal segmental glomerulosclerosis, diffuse mesangial sclerosis, and collapsing glomerulopathy.Conclusions.—Based on current knowledge of podocyte biology, we organized etiologic factors and morphologic features in a taxonomy of podocytopathies, which provides a novel approach to the classification of these diseases. Current and experimental therapeutic approaches are also discussed.

scholarly journals Ensemble learning based classifier to predict depression caused due to pandemic

2021 ◽  
Vol 2089 (1) ◽  
pp. 012026
Author(s):  
P Vaishali ◽  
P L S Kumari
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Text Classification ◽  
Virus Disease ◽  
Model Accuracy ◽  
Research Article ◽  
The World ◽  
Social Gathering ◽  
Second Wave

Abstract Pandemic caused due to Corona Virus Disease 2019 (COVID-19) affected each and every person life throughout the world. First wave of COVID-19 followed by second wave made situation more panic. Government declared Lockdown imposed strict prohibition on social gathering, unnecessary outing, travelling, and education. During home quarantine, people shared opinion, expressed views, feelings on social media. Home isolation and quarantine affected mental health of people which may lead to depression. Hence in this research article depression is predicted by implementing Neural Network based model. At first level this model implements text classification of COVID-19 based Tweets. Neural network model accuracy is 86.85%. In next level, using same tweet dataset as input, Ensemble learning based model is constructed. This model uses one of the boosting techniques known as Adaboost. Model is executed by varying Train-test-validation ratio. It is observed that accuracy of the model is improved. The model showed accuracy of 99.33 % successfully in every execution. Obtained results are compared with previous work in same area.

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