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Found 38 papers, 12 papers with code
Improving Medical Multi-modal Contrastive Learning with Expert Annotations
We introduce eCLIP, an enhanced version of the CLIP model that integrates expert annotations in the form of radiologist eye-gaze heatmaps.
Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series
Experiments on challenging real-world epidemiological and semi-synthetic data demonstrate the advantages of the M-CHMM: improved data fit, capacity to efficiently handle missing and noisy measurements, improved prediction accuracy, and ability to identify interpretable subsets in the data.
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics
By differentiating treatment components, incorporating their dosages, and sharing statistical information across patients via a hierarchical multi-output Gaussian process, our method improves prediction accuracy over existing approaches, and allows us to interpret the different effects of carbohydrates and fat on the overall glucose response.
Content Reduction, Surprisal and Information Density Estimation for Long Documents
We consider two interesting research questions: 1) how is information distributed over long documents, and 2) how does content reduction, such as token selection and text summarization, affect the information density in long documents.
Identifiable causal inference with noisy treatment and no side information
Building on existing results for measurement error models, we prove that our model's causal effect estimates are identifiable, even without knowledge of the measurement error variance or other side information.
A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease
We chose SCD as a model disease condition due to the presence of diverse erythrocyte morphologies in the blood samples of SCD patients.
SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration
Deep learning has emerged as a strong alternative for classical iterative methods for deformable medical image registration, where the goal is to find a mapping between the coordinate systems of two images.
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection
Adverse drug events (ADEs) are an important aspect of drug safety.
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes
Our model enables the estimation of a treatment policy from observational sequences of treatments and outcomes, and it can predict the interventional and counterfactual progression of the outcome after an intervention on the treatment policy (in contrast with the causal effect of a single treatment).
Deformation equivariant cross-modality image synthesis with paired non-aligned training data
Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications.
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
We present a simple approach to incorporate prior knowledge in BNNs based on external summary information about the predicted classification probabilities for a given dataset.
EEG based Emotion Recognition: A Tutorial and Review
Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic.
Deconfounded Representation Similarity for Comparison of Neural Networks
Similarity metrics such as representational similarity analysis (RSA) and centered kernel alignment (CKA) have been used to compare layer-wise representations between neural networks.
A Unified Review of Deep Learning for Automated Medical Coding
Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents.
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks.
Multitask Balanced and Recalibrated Network for Medical Code Prediction
Nevertheless, automated medical coding is still challenging because of the imbalanced class problem, complex code association, and noise in lengthy documents.
SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models
Despite the proven effectiveness of Transformer neural networks across multiple domains, their performance with Electronic Health Records (EHR) can be nuanced.
Deep Learning for Depression Recognition with Audiovisual Cues: A Review
Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection.
Multitask Recalibrated Aggregation Network for Medical Code Prediction
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement.
Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study
In the clinical application of medical code assignment, diagnosis and procedure codes are inferred from lengthy clinical notes such as hospital discharge summaries.
A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Models
Using deep latent variable models in causal inference has attracted considerable interest recently, but an essential open question is their ability to yield consistent causal estimates.
Medical Code Assignment with Gated Convolution and Note-Code Interaction
However, these methods are still ineffective as they do not fully encode and capture the lengthy and rich semantic information of medical notes nor explicitly exploit the interactions between the notes and codes.
Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text
Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems.
Informative Bayesian Neural Network Priors for Weak Signals
Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals.
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation
Surrogate models can be used to accelerate approximate Bayesian computation (ABC).
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
We propose a numerical method to fully quantify the uncertainty in, for example, ABC posterior moments.
Errors-in-variables Modeling of Personalized Treatment-Response Trajectories
Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications.
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained.
Learning Global Pairwise Interactions with Bayesian Neural Networks
Estimating global pairwise interaction effects, i. e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications.
A Bayesian model of acquisition and clearance of bacterial colonization
Bacterial populations that colonize a host play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations.
ELFI: Engine for Likelihood-Free Inference
The stand-alone ELFI graph can be used with any of the available inference methods without modifications.
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine.
Efficient acquisition rules for model-based approximate Bayesian computation
We propose to compute the uncertainty in the ABC posterior density, which is due to a lack of simulations to estimate this quantity accurately, and define a loss function that measures this uncertainty.
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task.
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
Approximate Bayesian computation (ABC) can be used for model fitting when the likelihood function is intractable but simulating from the model is feasible.
Multiple Output Regression with Latent Noise
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal.
Bayesian Information Sharing Between Noise And Regression Models Improves Prediction of Weak Effects
To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian approach of sharing information between the regression model and the noise model.
