Paper proposes a streamlined approach to clinical concept extraction using LSTM-CRF.
problem Automated extraction of concepts from clinical records for clinical research.
method Bidirectional LSTM with CRF decoding initialized with general-purpose word embeddings.
result Experimental results outperform all recent methods and rank closely to the best submission from the original i2b2/VA challenge.
Paper improves relation extraction in clinical texts with limited data.
problem Relation extraction in narrow knowledge domains with scarce annotated data.
method Introduces a bag-of-concepts (BoC) model and compares it with window-bounded co-occurrence (WBC).
result BoC model outperforms baseline and other complex methods on small dataset.
GRNN predicts multiple medical concepts from patient discharge summaries.
problem Understanding medical concepts in patient discharge summaries.
method Grounded Recurrent Neural Network (GRNN) that ties labels to specific dimensions of the hidden state.
result GRNN outperforms baselines in predicting medical concepts from discharge summaries.
MedCAT extracts valuable medical information from unstructured text.
problem Extracting structured information from unstructured biomedical documents.
method Unsupervised machine learning for disambiguation of entities.
result Improved entity detection and linking compared to existing tools.
Natural language processing predicts AKI onset in ICU patients.
problem Early detection of AKI in ICU patients to improve outcomes.
method Clinical notes were processed to generate word and concept embeddings. Five classifiers and a deep learning model were used to predict AKI.
result The best model achieved an AUC of 0.779 for predicting AKI onset.
System suggests clinical concepts in real-time for faster note creation.
problem Efficiently creating structured clinical notes with minimal keystrokes.
method Contextual autocompletion using shallow neural networks.
result Reduces keystrokes by 67% in real hospital environments.
Custom NLP system extracts clinical data for breast cancer analysis.
problem Manual extraction of information from text-based medical records is tedious and requires specialized knowledge.
method Combines standard text mining techniques with advanced synonym detection for global analysis.
result Achieved good extraction accuracy for various concepts of interest without requiring existing corpora or ontologies.
Deep learning outperforms classical methods in patient phenotyping.
problem Classifying patients into medical conditions using clinical notes.
method Comparison of CNNs, n-gram models, and cTAKES-based approaches on 10 phenotyping tasks.
result CNNs achieve an average F1-score of 76, significantly outperforming other methods.
Unified framework for unsupervised concept extraction simplifies guarantees.
problem Establishing guarantees for unsupervised concept extraction.
method Unified theoretical framework, meta-theorem for identifiability.
result Simplifies the task of proving guarantees for concept extraction.
Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts
problem Representing clinical concepts and patients in electronic health records
method Spectral-based unsupervised learning with flexible knowledge transfer
result Outperforms competing approaches in challenging scenarios
Automatically extracts phenotypes from cancer clinical notes for genetic studies.
problem Lack of structured patient representations in EHRs.
method Clustering of medical terms and sentences in clinical notes.
result 341 significant associations between clinical features and somatic mutations.
LLMs help automate extraction of actuarial variables from unstructured claims data.
problem Manual processing of unstructured claims data is time-consuming and inconsistent.
method Two-stage processing architecture using LLMs, modular Python pipeline.
result LLM-based extraction achieved high accuracy and practical actuarial value.
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.
Deep learning improves radiology image analysis.
problem Improving image analysis in radiology.
method Review and survey of deep learning applications in radiology.
result Deep learning algorithms match or exceed human performance in radiology tasks.
Improved negation detection in Dutch clinical texts using machine learning.
problem Extracting negation from clinical text for better model development.
method Comparison of rule-based and machine learning methods (biLSTM, RoBERTa).
result BiLSTM and RoBERTa models outperform rule-based method in F1 score, precision, and recall.
Model detects exercise fatigue with high accuracy.
problem Detecting exercise fatigue in real-time.
method Feature extraction using AHP, machine learning.
result 98.65% accuracy in detecting exercise fatigue.
The paper shows how the timing of prediction impacts model performance in healthcare.
problem The timing of prediction affects model performance in healthcare.
method The paper compares two prediction schemes: outcome-dependent and outcome-independent.
result An outcome-independent scheme outperforms an outcome-dependent scheme.
Extracts decision trees from CNNs to explain concept importance.
problem Understanding how CNNs make decisions about human-understandable concepts.
method Inferring labeled concept data from CNN hidden layer activations and creating a shallow decision tree.
result Extracted decision trees accurately represent CNN classifications.
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.
Study develops models to predict severe COVID-19 progression.
problem Identifying high-risk individuals for severe COVID-19.
method Developed survival models using EHR data, learning clinical concepts to improve accuracy.
result Learned clinical concepts improve model performance, boosting C-index to 0.858.
Framework improves clinical timeline reconstruction from text and tables.
problem Temporal precision and event timing in clinical narratives and EHRs.
method Retrieval-augmented multimodal alignment framework.
result Consistently improves absolute timestamp accuracy and temporal concordance.
System helps engineers with concept recognition for SEVA.
problem Recognizing systems engineering concepts for SEVA.
method Token classification task, domain expert labeling, pre-trained model fine-tuning, essential datasets creation, knowledge graph construction.
result System successfully recognizes systems engineering concepts.
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.
AI predicts medical specialty diagnostic choices from EHR records.
problem Predicting timely medical specialty diagnostic workups for patients.
method Ensemble of feed-forward neural networks trained on EHR data.
result Significantly higher accuracy compared to traditional checklists.
New method extracts biological concepts from cell microscopy images.
problem Extracting meaningful concepts from vision foundation models trained on cell microscopy images.
method Sparse dictionary learning (DL) combined with PCA whitening pre-processing.
result Successfully retrieved biologically meaningful concepts like cell types and genetic perturbations.
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.
Fast and cheaper next generation sequencing technologies will generate unprecedentedly massive and highly-dimensional genomic and epigenomic variation data. In the near future, a routine part of medical record will include the sequenced genomes. A fundamental question is how to efficiently extract genomic and epigenomi…
Machine learning aids in clinical prediction tasks.
problem Improving accuracy in clinical predictions.
method Introduction to machine learning concepts and algorithms, followed by practical application to clinical datasets.
result Demonstrated the application of machine learning models to clinical prediction problems.
DreamNLP extracts important terms from EHRs using a modified Count Sketch algorithm.
problem Efficiently extracting information from large sets of EHRs with limited prior knowledge.
method Modified Count Sketch data streaming algorithm for low memory usage.
result Extracted terms are useful for defining important features for machine learning in precision medicine.
Qwant Research improves clinical case matching and information retrieval.
problem Matching and retrieving relevant clinical cases and discussions.
method Approach based on language models and preprocessings, information extraction system using neural networks and linguistic analysis.
result Very encouraging results in information extraction accuracy.
Transfer learning improves clinical time series prediction with deep RNNs.
problem Training deep neural networks for clinical time series analysis requires large labeled data and expertise.
method Investigated transfer learning scenarios for deep RNNs: domain-adaptation and task-adaptation.
result Pre-trained deep models allow robust, efficient, and data-efficient clinical time series prediction.
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.
Transfer learning improves clinical time series prediction with limited data.
problem Training deep RNNs for clinical tasks requires large labeled data and tuning.
method Transfer learning from pre-trained RNNs on multiple tasks to new tasks.
result Features from pre-trained RNNs improve model performance and robustness.
Extracts salient concepts from CNNs for explaining deep neural networks.
problem Explaining the opaque behavior of deep neural networks in safety-critical domains.
method Uses autoencoders to extract salient concepts and builds a Bayesian causal model.
result Identifies and visualizes features influencing deep neural network classifications.
AdaCare learns health status from biomarkers across multiple time scales.
problem Lack of explicit extraction of historical biomarker variation and adaptability to diverse patient conditions.
method Scale-adaptive feature extraction and recalibration for interpretability.
result AdaCare achieves state-of-the-art prediction accuracy and provides interpretable results.
A new method uses literature constraints to improve phenotyping from EHR data.
problem Improving phenotyping from electronic health records (EHR) data.
method Constrained tensor factorization with literature constraints.
result Improved phenotyping results for hypertensive patients.
Neural networks enhance relation extraction from biomedical literature.
problem Automated extraction of relations between biomedical concepts.
method Use of multichannel architectures in deep neural networks with biomedical ontologies.
result State-of-the-art results in relation extraction tasks.
MIMIC-Extract transforms EHR data for reproducible healthcare machine learning.
problem Lack of accessible, standardized healthcare data for machine learning.
method Open-source pipeline for converting raw EHR data into usable dataframes.
result Demonstrates utility through benchmark tasks and baseline results.
BC-LLM uses LLMs to find concepts without predefined sets, improving interpretability and performance.
problem Finding a balance between interpretability and accuracy in concept extraction models.
method Bayesian approach with LLMs as both concept extractor and prior.
result BC-LLM outperforms interpretable and black-box models across various datasets.
EDUCE model explains text predictions by identifying key concepts.
problem Providing clear explanations for text predictions.
method Uses unsupervised binary concept representations to explain model decisions.
result Model automatically defines relevant concepts for prediction tasks.
Model predicts eligibility of cancer patients for clinical trials.
problem Clinical trials exclude many patients based on comorbidities and age.
method Deep neural networks trained on clinical trial protocols and free-texts.
result Model accurately predicts eligibility of clinical trial statements.
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.
The classification procedure of streaming data usually requires various ad hoc methods or particular heuristic models. We explore a novel non-parametric and systematic approach to analysis of heterogeneous sequential data. We demonstrate an application of this method to classification of the delays in responding to the…
CLIP dataset helps extract action items from hospital discharge notes.
problem Lost action items in long discharge notes hinder information sharing.
method Created CLIP dataset of annotated discharge notes, used multi-aspect extractive summarization, trained models on pre-trained language models and context.
result Best models improved by incorporating context and pre-trained language models.
Model creates summaries of patient notes to save time and reduce errors.
problem Improper summarization of patient notes leads to inefficiencies and errors.
method Developed an LSTM model to sequentially label topics in history of present illness notes.
result Achieved an F1 score of 0.876, indicating the model's effectiveness.
Method learns graph from data clusters using FCA.
problem Learning graph representation from multivariate data.
method Uses formal concept analysis (FCA) to extract hierarchical relationships between clusters.
result Empirically shows superior hierarchical structure extraction compared to baseline.
Framework for AI healthcare products from concept to market.
problem Failure of AI products to reach clinics despite promising potential.
method Decision-making framework for AI healthcare product development.
result Guides through a three-phase process to market launch of validated AI products.
Method extracts time-localized clusters to explain deep learning models in ECG analysis.
problem Limited understanding of deep learning models in ECG analysis.
method Extracts time-localized clusters from model's internal representations.
result Enhances trust in AI-driven diagnostics and reveals clinically relevant patterns.