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Found 22 papers, 5 papers with code
Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability.
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Scarcity of health care resources could result in the unavoidable consequence of rationing.
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost.
A Comparative Study of Pretrained Language Models for Long Clinical Text
Objective: Clinical knowledge enriched transformer models (e. g., ClinicalBERT) have state-of-the-art results on clinical NLP (natural language processing) tasks.
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
Materials and Methods: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000-2020.
Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets
We analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions of COVID-19 vaccines.
Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning
Most machine learning (ML) models are developed for prediction only; offering no option for causal interpretation of their predictions or parameters/properties.
Multimodal Machine Learning in Precision Health
Attempts to improve prediction and resemble the multimodal nature of clinical expert decision-making this has been met in the computational field of machine learning by a fusion of disparate data.
Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records
The rise of "doubly robust" non-parametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data, offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHR).
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
To overcome this, long sequence transformer models, e. g. Longformer and BigBird, were proposed with the idea of sparse attention mechanism to reduce the memory usage from quadratic to the sequence length to a linear scale.
Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients
On the 5, 783 sepsis patients identified by the Sepsis-3 criteria statistically significant performance decreases for mortality prediction were observed when applying the trained machine learning model on Asian and Hispanic patients.
Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes
We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients.
Transfer Learning in Electronic Health Records through Clinical Concept Embedding
In this study, we aim to (1) train some of the most prominent disease embedding techniques on a comprehensive EHR data from 3. 1 million patients, (2) employ qualitative and quantitative evaluation techniques to assess these embeddings, and (3) provide pre-trained disease embeddings for transfer learning.
Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records
Capturing the whole history of medical encounters is expected to lead to more accurate predictions, but the inclusion of records collected for decades and from multiple resources can inevitably exceed the receptive field of the existing deep learning architectures.
Risk factor identification for incident heart failure using neural network distillation and variable selection
In this study, we propose two methods, namely, model distillation and variable selection, to untangle hidden patterns learned by an established deep learning model (BEHRT) for risk association identification.
An explainable Transformer-based deep learning model for the prediction of incident heart failure
Predicting the incidence of complex chronic conditions such as heart failure is challenging.
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
Joint image-text embedding extracted from medical images and associated contextual reports is the bedrock for most biomedical vision-and-language (V+L) tasks, including medical visual question answering, clinical image-text retrieval, clinical report auto-generation.
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records
In this paper, we merge features of the deep Bayesian learning framework with deep kernel learning to leverage the strengths of both methods for more comprehensive uncertainty estimation.
BEHRT: Transformer for Electronic Health Records
Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness.
Learning Multimorbidity Patterns from Electronic Health Records Using Non-negative Matrix Factorisation
Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population, both in absolute and relative terms.
Performance Measurement for Deep Bayesian Neural Network
Deep Bayesian neural network has aroused a great attention in recent years since it combines the benefits of deep neural network and probability theory.
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
We developed data-driven prediction models to estimate the risk of new AKI onset.
