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Found 18 papers, 8 papers with code
Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data
AI-driven precision oncology has the transformative potential to reshape cancer treatment by leveraging the power of AI models to analyze the interaction between complex patient characteristics and their corresponding treatment outcomes.
Radiology-Aware Model-Based Evaluation Metric for Report Generation
Furthermore, we demonstrate that one of our checkpoints exhibits a high correlation with human judgment, as assessed using the publicly available annotations of six board-certified radiologists, using a set of 200 reports.
Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes
In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories.
Attention-based Multi-task Learning for Base Editor Outcome Prediction
To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence.
Dynamic Local Attention with Hierarchical Patching for Irregular Clinical Time Series
Irregular multivariate time series data is prevalent in the clinical and healthcare domains.
Attention-based Multi-task Learning for Base Editor Outcome Prediction
To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence.
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies.
Boosting Radiology Report Generation by Infusing Comparison Prior
To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports.
SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification.
Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III
Methods: We present an extensive evaluation of the impact of different federation and differential privacy techniques when training models on the open-source MIMIC-III dataset.
DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm
We use information from the largest drug combination database available (DrugComb), combining drug synergy scores in order to construct high confidence benchmark datasets.
Drug prescription clusters in the UK Biobank: An assessment of drug-drug interactions and patient outcomes in a large patient cohort
In recent decades, there has been an increase in polypharmacy, the concurrent administration of multiple drugs per patient.
Progressive Transformer-Based Generation of Radiology Reports
Inspired by Curriculum Learning, we propose a consecutive (i. e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps.
AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions
Background: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves.
Patient Similarity Analysis with Longitudinal Health Data
Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records.
AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks
Patients increasingly turn to search engines and online content before, or in place of, talking with a health professional.
Neural networks versus Logistic regression for 30 days all-cause readmission prediction
Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0. 642 AUC (95% CI, 0. 640-0. 645).
Publishing without Publishers: a Decentralized Approach to Dissemination, Retrieval, and Archiving of Data
Making available and archiving scientific results is for the most part still considered the task of classical publishing companies, despite the fact that classical forms of publishing centered around printed narrative articles no longer seem well-suited in the digital age.
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