Improves medication name inference for telemedicine and conversational agents.
problem Challenges in mapping user-friendly medication names to standardized ones.
method Entity-boosted two-tower neural network for ranking SMN to DMP.
result State-of-the-art results achieved with improved attention-based ranking.
Automated system extracts medication regimens from medical conversations.
problem Extract relevant medication information from medical conversations.
method QA task approach, combined QA and Information Extraction, data augmentation, public embeddings, pretraining.
result Improved accuracy in extracting dosage and frequency from 54.28 and 37.13 to 89.57 and 45.94.
A GMM-based method generates new 3D structures from medical images.
problem Generating new medical images from limited data and different modalities.
method Gaussian Mixture Model (GMM) for point-cloud generation.
result Generated point-clouds closely match training samples from the same class.
Medical deconfounder uses EHRs to estimate treatment effects without confounders.
problem Bias in assessing treatment effects from EHRs due to unobserved confounders.
method Develops a machine learning algorithm (medical deconfounder) to adjust for confounders.
result Medical deconfounder produces more accurate treatment effect estimates and identifies effective medications.
MedGCN uses graph convolutional networks to recommend medications and estimate lab tests.
problem Automatically recommend medications and estimate lab tests for cost savings and better patient care.
method Integrates heterogeneous graph relations, learns node embeddings with graph convolutional networks, and uses cross regularization for multi-task learning.
result MedGCN outperforms state-of-the-art models in both medication recommendation and lab test imputation on real-world datasets.
Paper detects bias in AI medical models using CART.
problem Ensuring fairness in AI medical decision support systems.
method Uses Classification and Regression Trees (CART) algorithm to identify bias.
result Validated the CART approach in both synthetic and real-world data.
Strong baseline for medical imaging domain adaptation.
problem Improving generalization in medical imaging across different datasets.
method Training on diverse chest X-ray datasets.
result Empirical demonstration of model generalization to out-of-sample domains.
Paper tackles medical question similarity using domain-relevant embeddings.
problem Identifying same-question pairs in medical contexts.
method Semi-supervised pre-training of a neural network on medical question-answer pairs.
result Our model achieves 82.6% accuracy on medical question similarity task.
Transfer learning offers little benefit in medical imaging tasks.
problem Understanding transfer learning's impact on medical imaging tasks.
method Evaluation of transfer learning on two large-scale medical imaging tasks.
result Simple, lightweight models perform comparably to ImageNet architectures.
This paper benchmarks privacy-preserving machine learning on medical images.
problem Ensuring privacy in medical image analysis while maintaining model accuracy.
method Comparing Local-DP and DP-SGD for differential privacy in medical imagery.
result Theoretical privacy guarantees do not fully align with real-world performance.
This study applies neural models to automatically recognize medical entities from natural language.
problem Automated recognition of medical entities from natural language is complex and time-consuming.
method Utilizes deep neural sequence models trained on a large dataset of death certificates.
result Deep neural models can efficiently recognize medical entities from natural language.
Convolutional neural network captures medical features for risk prediction in EHRs.
problem Extracting useful clinical representations from longitudinal EHR data.
method Multi-layer convolutional neural network (CNN) with learned medical feature embedding.
result Effective risk prediction on cohorts of congestive heart failure and diabetes patients.
DiffDenoise preserves fine structures in medical images using conditional diffusion models.
problem Medical image denoising often results in loss of fine structures.
method Conditional diffusion model with stabilized reverse sampling and supervised training.
result DiffDenoise outperforms state-of-the-art methods in medical image denoising.
Novel fuzzy topic model improves health corpus retrieval.
problem Automatic retrieval of health and medical knowledge from text.
method Fuzzy Latent Semantic Analysis (FLSA) for topic discovery.
result FLSA outperforms LDA in estimating topics and improving retrieval.
Paper proposes embedding medical concepts from claims data for better risk adjustment models.
problem Lack of efficient representation of medical histories in risk adjustment models.
method Semantic embeddings of medical concepts from diagnostic, procedure, and prescription codes.
result Embedding-based models outperform commercial risk adjustment models in prospective risk score prediction.
Generative models solve medical imaging inverse problems without needing paired data.
problem Reconstructing medical images from partial measurements.
method Score-based generative models trained on medical images, then sampling to reconstruct images consistent with measurements and physical model.
result Comparable or better performance in CT and MRI tasks, with improved generalization to unknown measurement processes.
Survey of deep learning methods for medical anomaly detection.
problem Medical anomaly detection using machine learning.
method Thorough review of deep learning techniques across various medical domains.
result Comparison and contrast of deep learning models and their limitations.
This paper addresses privacy in federated learning for medical imaging by estimating model uncertainty.
problem Privacy concerns in federated learning for medical imaging.
method Federated Learning (FL) for collaborative model training while preserving patient data privacy.
result Accurate uncertainty estimation in federated learning for medical imaging.
Paper detects biases in medical imaging ML models using counterfactual analysis.
problem Bias in medical imaging ML models negatively impacts generalization performance.
method Counterfactual invariance framework combining conditional latent diffusion models and statistical hypothesis testing.
result The method identifies and quantifies biases without direct access to counterfactual data.
Study uses stacked hourglass networks to improve facial landmark detection for medical diagnosis.
problem Improving accuracy of facial landmark detection for medical diagnosis.
method Conducted a study on landmark localisation methods using stacked hourglass networks.
result State-of-the-art stacked hourglass architecture outperforms traditional methods.
Study compares DL models for medical image segmentation, finds synergistic ensemble strategies improve performance.
problem Improving DL models for specialized medical image segmentation using transfer learning.
method Detailed comparisons of TII and LMI models for binary segmentation of medical images.
result Ensemble strategies improve performance by 10% in certain scenarios.
Deep learning predicts vascular disease from medical history.
problem Predicting high-risk vascular diseases from medical records.
method Medical History-based Prediction using Attention Network (MeHPAN) models.
result MeHPAN models outperform standard classification models.
GANs improve medical image analysis through de-noising, segmentation, and data synthesis.
problem Chronic scarcity of labeled medical images.
method Generative Adversarial Networks (GANs) and their extensions.
result GANs can synthesize realistic medical images to address data scarcity.
Machine learning constructs problem-based medical records from electronic health records.
problem Difficulty in finding relevant medical information for clinical questions.
method Knowledge base completion using machine learning on electronic health records.
result Automatic construction of problem-based groupings of medications, procedures, and lab tests.
MI-GAN generates synthetic medical images for supervised analysis.
problem Lack of large datasets and overfitting in medical image analysis.
method Generative Adversarial Network (GAN) for generating synthetic medical images and masks.
result MI-GAN achieves state-of-the-art performance with dice coefficient of 0.837 on STARE and 0.832 on DRIVE datasets.
Research benchmarks LLMs in medical domain to reduce hallucinations.
problem Hallucinations in medical LLMs can lead to incorrect information.
method Developed Med-HALT dataset and testing methods.
result Significant performance differences among LLMs identified.
Deep learning improves MRI analysis, promising future in medical imaging.
problem Improving accuracy and efficiency in MRI analysis.
method Application of deep artificial neural networks to MRI processing.
result Deep learning outperforms traditional methods in MRI analysis.
This paper tackles label noise in deep learning for medical image analysis.
problem Label noise impacts deep learning models in medical image analysis.
method Review of state-of-the-art techniques and experiments with label noise in medical datasets.
result Developed new methods to combat label noise in deep models for medical image analysis.
Paper proposes a new method to handle missing data in medical records using sequential variational autoencoders.
problem Missing data in medical records due to sensor off-times and uneven data collection.
method Sequential variational autoencoders (VAEs) with a new methodology called Shi-VAE.
result Shi-VAE achieves the best performance in terms of both metrics compared to state-of-the-art methods.
Paper shows how to quantify uncertainty in medical ML models.
problem Uncertainty in opaque ML models can lead to safety risks in medical applications.
method Introduces Uncertainty Wrapper to quantify uncertainty transparently.
result Demonstrates practical utility of Uncertainty Wrapper in flow cytometry.
Neural network model improves sentence classification in medical abstracts.
problem Individual sentence classification misses contextual information.
method Combines ANN effectiveness with structured prediction for joint sentence classification.
result Achieves state-of-the-art results on medical abstract datasets.
Efficient equivariant MobileNetV2 for medical applications on mobile devices.
problem Limited computational resources for deep learning models in medical applications.
method Design and optimize an equivariant version of MobileNetV2 with model quantization.
result Achieved close-to state-of-the-art performance on medical dataset with improved efficiency.
A new metric FRD improves comparing medical images.
problem Comparing medical images for distribution or domain differences.
method Developed a new metric FRD using standardized radiomic features.
result FRD outperforms other metrics in various medical imaging applications.
Develops methods to show model confidence and feature importance in medical imaging.
problem Ensuring safety and understanding model confidence in medical applications.
method Creates a pipeline to visualize uncertainty and saliency maps for deep neural networks.
result Demonstrates how deep neural networks can be made more transparent and safe for medical applications.
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
Hierarchical-CPI improves variable importance measurement for medical data.
problem Limited interpretability of complex medical models.
method Hierarchical-CPI measures conditional variable importance with statistical control, handling correlated data.
result Hierarchical-CPI outperforms existing methods in medical datasets.
Proposes guidelines for developing medical AI products.
problem Lack of clear pathways for regulating medical AI.
method Statistical risk perspective and deep understanding of machine learning methodologies.
result Enhanced development of medical AI products and regulations.
Two large medical dialogue datasets for improving healthcare.
problem Improving healthcare through better dialogue systems.
method Building two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN.
result The datasets are the largest medical dialogue datasets to date.
A new model answers questions about medical images.
problem Lack of transparency in deep learning models for medical imaging.
method A question-centric model that queries image models directly.
result The model achieves equal or higher accuracy than existing methods.
Improves medical note processing by training model on related concepts and global context.
problem Scarce and imbalanced labeled training data limits generalizability of automated abbreviation disambiguation models.
method Data augmentation using related medical concepts and global context information within medical notes.
result Model accuracy improved by almost 14% on CASI dataset and 4% on i2b2 dataset.
MedGraph learns patient visit embeddings from EMRs, capturing both attributes and temporal sequences.
problem Limited EMR embedding methods fail to capture patient demographics, utilisation, and code descriptions.
method MedGraph constructs an attributed bipartite graph and uses a point process to model temporal sequences.
result MedGraph outperforms state-of-the-art methods in medical risk prediction tasks.
Models predict uncertainty to help identify patients needing second opinions.
problem Disagreements among medical experts, especially in diagnosis.
method Direct Uncertainty Prediction (DUP) training to predict uncertainty scores directly from patient features.
result DUP outperforms Uncertainty Via Classification in identifying cases needing second opinions.
TPM improves medical image segmentation by separating foreground and background.
problem Few-shot medical image segmentation challenges due to background variability.
method Tied Prototype Model (TPM) focusing on foreground, adapting thresholds, and using class priors.
result TPM leads to improved segmentation accuracy compared to ADNet.
Neural network classifies breast cancer lesions using global and local image features.
problem Classifying breast cancer lesions in medical images with high resolution and small regions of interest.
method Proposes a neural network that combines global saliency maps and local patches for pixel-level saliency maps.
result Achieves radiologist-level performance in screening mammography interpretation.
Paper proposes a classifier to improve medical image classification with limited data.
problem Limited training data leads to overfitting in medical image classification.
method Uses reinforcement learning to update classifier parameters with generalization feedback from a subset of training data.
result Improves classification performance and demonstrates generalized learning.
This paper combines deep learning with medical imaging models to improve reconstruction speed and reduce radiation.
problem Medical imaging issues like slow MRI, radiation injury in CT and PET.
method Combining deep learning with model-based reconstruction.
result Improves reconstruction speed and reduces radiation in medical imaging.
Proposes a new method for medical diagnosis using network-based representation learning.
problem Improving medical diagnosis accuracy through better data representation.
method Heterogeneous network-based model and modified metapath2vec algorithm for learning latent node representations.
result Significant performance boost in symptom/disease classification and disease prediction tasks.
Improved speech recognition for medical conversations.
problem Transcribing accurate doctor-patient conversations.
method Collected a large dataset, designed realistic tasks, explored alignment and model types (CTC and LAS).
result LAS models were more resilient to noisy data, while CTC required more data cleanup.