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Found 54 papers, 31 papers with code
Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records
Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain.
Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports
We compare the performance of our model with other representative models in text-to-speech and text-to-image.
Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays
X-ray computed tomography (CT) is one of the most common imaging techniques used to diagnose various diseases in the medical field.
Deep Imbalanced Time-series Forecasting via Local Discrepancy Density
Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states.
Unified Chest X-ray and Radiology Report Generation Model with Multi-view Chest X-rays
We also find that view-specific special tokens can distinguish between different views and properly generate specific views even if they do not exist in the dataset, and utilizing multi-view chest X-rays can faithfully capture the abnormal findings in the additional X-rays.
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
The utterances were collected from 222 hospital staff, including physicians, nurses, insurance review and health records teams, and more.
Significantly Improving Zero-Shot X-ray Pathology Classification via Fine-tuning Pre-trained Image-Text Encoders
However, large-scale and high-quality data to train powerful neural networks are rare in the medical domain as the labeling must be done by qualified experts.
UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge
Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models.
Universal EHR Federated Learning Framework
Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR).
Self-supervised predictive coding and multimodal fusion advance patient deterioration prediction in fine-grained time resolution
Therefore, in this study, we propose an hourly prediction method of critical events in ED, i. e., mortality and vasopressor need.
Specializing Multi-domain NMT via Penalizing Low Mutual Information
Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains.
Do Language Models Understand Measurements?
Recent success of pre-trained language models (PLMs) has stimulated interest in their ability to understand and work with numbers.
Correlation between Alignment-Uniformity and Performance of Dense Contrastive Representations
Recently, dense contrastive learning has shown superior performance on dense prediction tasks compared to instance-level contrastive learning.
Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity
To analyze this, we first train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences.
Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels
Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time.
Task Agnostic and Post-hoc Unseen Distribution Detection
Despite the recent advances in out-of-distribution(OOD) detection, anomaly detection, and uncertainty estimation tasks, there do not exist a task-agnostic and post-hoc approach.
UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge
Experimental results demonstrate that UniHPF is capable of building large-scale EHR models that can process any form of medical data from distinct EHR systems.
Towards the Practical Utility of Federated Learning in the Medical Domain
Federated learning (FL) is an active area of research.
Revisiting the Importance of Amplifying Bias for Debiasing
$f_B$ is trained to focus on bias-aligned samples (i. e., overfitted to the bias) while $f_D$ is mainly trained with bias-conflicting samples by concentrating on samples which $f_B$ fails to learn, leading $f_D$ to be less susceptible to the dataset bias.
Unconditional Image-Text Pair Generation with Multimodal Cross Quantizer
To learn a multimodal semantic correlation in a quantized space, we combine VQ-VAE with a Transformer encoder and apply an input masking strategy.
Graph-Text Multi-Modal Pre-training for Medical Representation Learning
MedGTX uses a novel graph encoder to exploit the graphical nature of structured EHR data, and a text encoder to handle unstructured text, and a cross-modal encoder to learn a joint representation space.
Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram
In this work, we propose an ECG pre-training method that learns both local and global contextual representations for better generalizability and performance on downstream tasks.
Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records
In addition, for a reliable EHR-QA model, we apply the uncertainty decomposition method to measure the ambiguity in the input question.
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals.
Ranked #1 on
Seizure Detection
on TUH EEG Seizure Corpus
Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE
In order to perform unconditional video generation, we must learn the distribution of the real-world videos.
Exploration into Translation-Equivariant Image Quantization
This is an exploratory study that discovers the current image quantization (vector quantization) do not satisfy translation equivariance in the quantized space due to aliasing.
Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture
An intelligent machine that can answer human questions based on electronic health records (EHR-QA) has a great practical value, such as supporting clinical decisions, managing hospital administration, and medical chatbots.
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos-pitals.
Conditional Generation of Periodic Signals with Fourier-Based Decoder
Periodic signals play an important role in daily lives.
Natural Attribute-based Shift Detection
Despite the impressive performance of deep networks in vision, language, and healthcare, unpredictable behaviors on samples from the distribution different than the training distribution cause severe problems in deployment.
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel
To address this, we introduce a new neural stochastic processes, Decoupled Kernel Neural Processes (DKNPs), which explicitly learn a separate mean and kernel function to directly model the covariance between output variables in a data-driven manner.
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
To overcome this problem, we introduce Description-based Embedding, DescEmb, a code-agnostic description-based representation learning framework for predictive modeling on EHR.
Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training
Recently a number of studies demonstrated impressive performance on diverse vision-language multi-modal tasks such as image captioning and visual question answering by extending the BERT architecture with multi-modal pre-training objectives.
Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction
Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation (NMT) systems.
A System for Automated Open-Source Threat Intelligence Gathering and Management
SecurityKG collects OSCTI reports from various sources, uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors, and constructs a security knowledge graph.
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment
We evaluate the out-of-distribution (OOD) detection performance of self-supervised learning (SSL) techniques with a new evaluation framework.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Knowledge Graph-based Question Answering with Electronic Health Records
Question Answering (QA) is a widely-used framework for developing and evaluating an intelligent machine.
Neural Ordinary Differential Equations for Intervention Modeling
By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerged as an effective framework for modeling a system dynamics in the continuous time domain.
Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation
Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
A recent study showed that using the graphical structure underlying EHR data (e. g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction.
Analyzing the Role of Model Uncertainty for Electronic Health Records
We further show that RNNs with only Bayesian embeddings can be a more efficient way to capture model uncertainty compared to ensembles, and we analyze how model uncertainty is impacted across individual input features and patient subgroups.
Clinical Concept Extraction for Document-Level Coding
The text of clinical notes can be a valuable source of patient information and clinical assessments.
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems.
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers.
Compositional Obverter Communication Learning From Raw Visual Input
Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e. g. hand- engineered features).
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
We present MorphNet, an approach to automate the design of neural network structures.
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Access to electronic health record (EHR) data has motivated computational advances in medical research.
Causal Regularization
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable.
GRAM: Graph-based Attention Model for Healthcare Representation Learning
-Interpretation:The representations learned by deep learning methods should align with medical knowledge.
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.
Ranked #2 on
Disease Trajectory Forecasting
on UK CF trust
Multi-layer Representation Learning for Medical Concepts
Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification.
Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure Prediction
Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data.
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses.
