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Found 17 papers, 6 papers with code
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures
In this study, we leverage federated learning strategies to address clopidogrel treatment failure detection.
Data augmentation method for modeling health records with applications to clopidogrel treatment failure detection
We present a novel data augmentation method to address the challenge of data scarcity in modeling longitudinal patterns in Electronic Health Records (EHR) of patients using natural language processing (NLP) algorithms.
Multimodal Learning for Materials
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials.
Automatic prediction of mortality in patients with mental illness using electronic health records
We applied a variety of machine learning algorithms to predict 30-day mortality followed by feature importance analysis.
Why Do Students Drop Out? University Dropout Prediction and Associated Factor Analysis Using Machine Learning Techniques
Graduation and dropout rates have always been a serious consideration for educational institutions and students.
Detection and prediction of clopidogrel treatment failures using longitudinal structured electronic health records
We propose machine learning algorithms to automatically detect and predict clopidogrel treatment failure using longitudinal structured electronic health records (EHR).
Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare
The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information.
Predicting Development of Chronic Obstructive Pulmonary Disease and its Risk Factor Analysis
Chronic Obstructive Pulmonary Disease (COPD) is an irreversible airway obstruction with a high societal burden.
Predicting Students' Exam Scores Using Physiological Signals
While acute stress has been shown to have both positive and negative effects on performance, not much is known about the impacts of stress on students grades during examinations.
Deep Learning and Symbolic Regression for Discovering Parametric Equations
Symbolic regression is a machine learning technique that can learn the governing formulas of data and thus has the potential to transform scientific discovery.
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Deep learning techniques have been increasingly applied to the natural sciences, e. g., for property prediction and optimization or material discovery.
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box.
OccamNet: A Fast Neural Model for Symbolic Regression at Scale
Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of finding compact analytic expressions to describe scientific data.
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared to a standard neural network-based architecture, paving the way for deep learning to be applied in scientific exploration and discovery.
Toward estimating personal well-being using voice
Estimating personal well-being draws increasing attention particularly from healthcare and pharmaceutical industries.
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
Our method for discovering interpretable latent parameters in spatiotemporal systems will allow us to better analyze and understand real-world phenomena and datasets, which often have unknown and uncontrolled variables that alter the system dynamics and cause varying behaviors that are difficult to disentangle.
Syntactical Analysis of the Weaknesses of Sentiment Analyzers
We carry out a syntactic analysis of two state-of-the-art sentiment analyzers, Google Cloud Natural Language and Stanford CoreNLP, to assess their classification accuracy on sentences with negative polarity items.
