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.
CCA features from medical codes predict future surgeries.
problem Predicting future surgeries based on medical codes.
method Canonical correlation analysis applied to sequences of medical codes.
result CCA embeddings capture meaningful relationships among medical codes.
Enhances medical code predictions for multi-morbidity patients using text classification.
problem Improving accuracy in predicting medical codes for patients with multiple illnesses.
method Used machine learning techniques, including multi-label medical text classification, to enhance predictions.
result High dimensional embeddings pre-trained on health data significantly improve multi-label classification performance.
ConCare personalizes healthcare predictions by capturing EMR features.
problem Predicting patient outcomes from EMR data with personalization.
method Captures personal characteristics and time-aware distribution in EMR data.
result Improves healthcare prediction accuracy through personalized health context.
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.
Deep learning detects pneumonia with 36x compression on low-power devices.
problem High accuracy pneumonia detection on low-power embedded devices.
method Structured weight pruning method for compression and maintaining accuracy.
result Up to 36x compression ratio with no accuracy loss.
New embeddings for medical concepts from multimodal data surpass previous methods.
problem Lack of comprehensive embeddings for medical concepts.
method Combining large datasets of medical notes, claims, and journal articles to learn embeddings.
result cui2vec embeddings achieve state-of-the-art performance.
ME2Vec learns medical entity vectors from EHR data.
problem Learning structured information from EHR data.
method Graph-based medical entity embedding using diverse graph embedding techniques.
result ME2Vec outperforms baselines in disease diagnosis prediction.
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.
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.
Somed2Vec learns medical concept embeddings from SNOMED-CT, improving healthcare analytics.
problem Lack of effective vector representations for medical concepts in healthcare analytics.
method Graph-based representation learning using random walks and Poincaré embeddings on SNOMED-CT.
result Concept embeddings from SNOMED-CT significantly outperform state-of-the-art embeddings.
Two binary Sine Cosine Algorithms improve feature selection in medical datasets.
problem Optimizing feature selection from medical datasets to enhance model accuracy.
method Proposed SBSCA and VBSCA algorithms using S-shaped and V-shaped transfer functions.
result SBSCA and VBSCA outperform four other binary optimization algorithms in medical datasets.
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.
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.
Visual analytics system for comparing medical records using sequence embeddings.
problem Challenges in analyzing medical records due to high dimensionality, irregularity, and sparsity.
method Event and sequence embeddings using autoencoder and self-attention mechanism, with sequence alignment for comparison.
result Demonstrated effectiveness with real-world neonatal ICU dataset.
Paper uses NLP to cluster patient visits for diagnosis validation.
problem Validating if similar patients receive similar diagnoses.
method Representation of medical visits using word embeddings, clustering patients' visits.
result Stable and separated segments of visits positively validated against diagnoses.
Deep learning models improved sentence similarity in medical records.
problem Improving sentence similarity in electronic medical records.
method Developed models using traditional machine learning and deep learning approaches, pre-trained sentence embeddings on biomedical corpora.
result Ensembled model achieved a Person correlation coefficient of 0.8528.
Deepr learns features from medical records to predict patient risk.
problem Feature engineering bottleneck in creating predictive systems from medical records.
method Transforms medical records into sequences, uses convolutional neural nets to detect and combine clinical motifs.
result Deepr achieves superior accuracy in predicting patient risk compared to traditional techniques.
Deep learning models struggle with irrelevant features in survival analysis.
problem Deep learning models suffer from performance deficits when dealing with many irrelevant features in survival analysis.
method Developed novel feature selection methods for deep learning models in survival analysis.
result Substantial performance improvements are achievable with feature selection methods.
MiME learns EHR data structure for predictive healthcare tasks.
problem Data insufficiency in EHR for predictive healthcare tasks.
method Leverages multilevel structure of EHR data and learns multilevel embedding.
result MiME outperforms baseline methods in diverse evaluation settings.
Enhanced word embedding creates new consumer-friendly health terms.
problem Laymen's health terms are often jargon and hard to understand.
method Developed an enhanced GloVe word embedding technique to generate new consumer-friendly terms.
result New CHV terms generated from consumer-generated text.
Proposes a deep learning framework for evaluating patient similarities from EHRs.
problem Evaluating clinical similarities between patients for various healthcare applications.
method A deep learning framework with medical concept embedding, preserving temporal information.
result Significant improvement in patient similarity evaluation over baselines.
FEET protocol evaluates foundation models across three scenarios.
problem Lack of standardized evaluation protocols for foundation models.
method Structured evaluation protocol across three use cases: frozen, few-shot, and fine-tuned embeddings.
result Demonstrates comprehensive assessment of foundation models' effectiveness.
Proposes a new method for medical image segmentation.
problem Medical image segmentation challenges.
method Decompose-and-Integrate Learning.
result Improves segmentation performance on multiple datasets.
Text2Node maps medical phrases to a taxonomy, overcoming coding standard limitations.
problem Limited data interchangeability between EHR systems due to different coding standards.
method Text2Node uses word and node embeddings, along with mapping functions, to generalize from limited training data.
result Text2Node achieves high accuracy in mapping phrases to a taxonomy, even for unseen concepts.
LSTMs predict CHF onset 15 months in advance with 91.47% AUC.
problem Predicting congestive heart failure onset 15 months in advance.
method LSTM-based RNN architecture using 216,394 patient data.
result LSTMs achieve AUCROC of 0.9147 for CHF onset prediction.
A reinforcement learning method predicts medical outcomes from masked feature vectors.
problem Predicting medical outcomes from limited feature data.
method Reinforcement learning with iterative feature selection and a guesser network.
result The method outperforms baselines and is highly interpretable.
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.
Framework harmonizes EHR data across institutions for better analysis.
problem Heterogeneity of medical codes and terminologies hinder EHR data analysis.
method MASH (Multi-source Automated Structured Hierarchy) uses neural optimal transport and learned hyperbolic embeddings to align and structure EHR data.
result MASH generates interpretable hierarchical graphs for unstructured local laboratory codes.
Study shows model uncertainty is crucial for medical predictions, especially for individual patients.
problem Uncertainty in medical predictions, especially for individual patients.
method Used RNN ensembles and various Bayesian RNNs to analyze model uncertainty.
result RNNs with only Bayesian embeddings are more efficient for capturing model uncertainty.
The study compares Euclidean and cosine distances in medical drug prescription prediction.
problem Comparing Euclidean and cosine distances in medical drug prescription prediction.
method Established geometric properties and compared distances in real-world medical data.
result Different distances lead to different optimizing nonlinear kernel embedding frameworks.
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.
Paper reviews neurolinguistics and language technologies, emphasizing mutual enrichment.
problem Understanding brain activity during language processing.
method Brain imaging studies and natural language representations.
result Development of brain-aware natural language representations.
CAggNet improves medical image segmentation by fusing coarse and fine features.
problem Medical image segmentation accuracy and efficiency.
method Crossing Aggregation Network with nested skip connections and weighted aggregation.
result CAggNet achieves more accurate and efficient segmentation compared to existing methods.
Deep learning method extracts medical knowledge from YouTube videos.
problem Improving healthcare information dissemination through machine learning.
method Developed a deep learning method to classify YouTube videos by medical knowledge level.
result Preliminary results show satisfactory performance in extracting medical knowledge from videos.
HRTPP improves TPP interpretability and accuracy in medical event modeling.
problem Lack of interpretability in TPPs for medical event sequences.
method Hybrid-Rule Temporal Point Processes (HRTPP) integrating temporal logic rules and numerical features.
result HRTPP outperforms state-of-the-art interpretable TPPs in predictive performance and clinical interpretability.
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.
Novel IRL method identifies suboptimal medical decisions in ICU data.
problem Identifying suboptimal medical decisions in clinical settings.
method Incorporates Inverse Reinforcement Learning with a pruning step to identify and remove suboptimal actions.
result Pruning step effectively identifies clinical priorities and values from suboptimal data.
LSTM model detects voice disorders with high accuracy.
problem Automated detection of voice disorders is challenging due to continuous audio data.
method Used Long Short Term Memory (LSTM) model for feature extraction and classification of voice disorders.
result 22% sensitivity, 97% specificity, 56% unweighted average recall.
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events usi…
COMPASS improves uncertainty quantification for medical segmentation metrics.
problem Uncertainty quantification for medical segmentation metrics is crucial for clinical decision-making.
method COMPASS leverages deep neural network inductive biases to generate efficient, metric-based conformal prediction intervals.
result COMPASS produces significantly tighter intervals than traditional conformal prediction methods on medical image segmentation tasks.
UNet++ improves medical image segmentation with deep supervision.
problem Improving accuracy in medical image segmentation.
method Nested U-Net architecture with deep supervision.
result UNet++ achieves significant improvements in IoU scores.
Model predicts community health outcomes from social media language.
problem Linking social media language to community health outcomes.
method Sentence embeddings, regression model, clustering, without additional data.
result Predicts community-level medical outcomes from social media language.
A new method detects hallucinations in medical image restoration using Fourier Ring Correlation.
problem Detecting hallucinations in DL outputs for medical image restoration.
method sFRC (s(c)can FRC) over small patches of DL outputs and reference images.
result sFRC effectively detects hallucinations in CT and MRI restoration problems.
PLIs improve classifier performance by fine-tuning latent representations.
problem Difficult interpretation of high-dimensional latent representations in neural networks.
method Back-propagation of manual changes to low-dimensional embeddings using t-distributed stochastic neighbourhood embeddings.
result Manual separation of class clusters in latent space enhances classifier performance.
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.
Deep learning improves automatic image segmentation.
problem Automatic object localization and boundary delineation in images and medical scans.
method Proposed and evaluated novel dilated dense encoder-decoder architectures for salient object segmentation and lesion localization in medical images.
result Proposed architectures outperform state-of-the-art models in accuracy and efficiency.