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.
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.
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.
This new research explores the effects of various training methods on a Polish to English Statistical Machine Translation system for medical texts. Various elements of the EMEA parallel text corpora from the OPUS project were used as the basis for training of phrase tables and language models and for development, tunin…
Deep EHR predicts chronic diseases using medical notes and structured data.
problem Early detection of chronic diseases for better management and resource allocation.
method Proposes a multi-task framework combining free-text medical notes and structured EHR data using deep learning.
result Deep learning models using text outperform models using only structured data, and models with numerical values and negations in text perform best.
The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translat…
Tsetlin Machine improves text categorization accuracy and interpretability.
problem High accuracy and interpretability in medical text categorization.
method Representing text as propositional variables, capturing categories with simple formulae, and using the Tsetlin Machine to learn these formulae.
result Tsetlin Machine outperforms other methods on various datasets, delivering best recall and precision.
A neural network predicts medical codes from clinical notes with high accuracy and interpretability.
problem Lack of automated and accurate annotation of medical codes from clinical notes.
method Attentional Convolutional Network that aggregates and selects relevant segments of clinical notes.
result Achieved precision@8 of 0.71 and Micro-F1 of 0.54, better than prior methods.
Deep learning predicts readmissions from less structured data.
problem Predicting readmissions from non-standard, unstructured medical records.
method Proposes a deep learning architecture that handles less structured data, including Spanish text.
result Achieves AUROC of 0.76 on a Chilean medical dataset, comparable to US results.
Improved NER in medical text with few examples.
problem Limited annotated examples for NER in medical texts.
method Layer-wise initialization, hyperparameter tuning, pre-training data, custom word embeddings, optimizing OOV words.
result F1 score improved from 69.3% to 78.87%.
New method reduces costs in text classification with partial labels.
problem Text classification with limited labeled data.
method Proposes a new maximum likelihood estimator with self-correction for ambiguous training examples.
result Our estimators converge faster under certain conditions.
Large dataset for medical abstracts classified by sentence role.
problem Efficiently classify long medical abstracts for researchers.
method Labeled dataset of 200k abstracts, each with 2.3M sentences.
result Improved sentence classification for medical literature.
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.
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.
System assesses patient urgency and recommends care based on medical notes.
problem Assessing patient urgency and recommending appropriate care.
method Attention-based convolutional neural network trained on medical notes.
result Precision increases to 85% when using attention scores for warning symptoms.
Develops a deep multimodal classifier for medical images and reports.
problem Challenges of small datasets in medical deep learning.
method Transfer learning, integrated gradients, unsupervised clustering, tuning parameter.
result Improves classification accuracy by 4% and 7% on average.
Convolutional residual model predicts diagnoses from EHR notes.
problem Predicting multiple diagnoses from medical text data.
method Convolutional Neural Network (CNN) + Deep Residual Network.
result Superior performance compared to baseline models.
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.
Systematic review of electronic health record phenotyping approaches.
problem Detecting patient cohorts using electronic health records.
method Comprehensive literature review of preprocessing and modeling approaches.
result Natural language processing shows promise for electronic phenotyping.
LLmFPCA-detect detects anomalies in sparse longitudinal text data using LLMs and mFPCA.
problem Challenges in detecting patterns and anomalies in sparse longitudinal textual data.
method Pairs LLM-based text embeddings with mFPCA to detect clusters and anomalies.
result LLmFPCA-detect outperforms state-of-the-art baselines on Amazon and Wikipedia datasets.
Deep learning predicts hospital readmission risk from notes.
problem Predicting hospital-wide readmission risk from doctors' notes.
method Deep learning and natural language processing on unstructured text.
result Model predicts readmission with c-statistic 0.70.
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.
Framework extracts symptoms from EHRs for rapid disease outbreak detection.
problem Extracting relevant data from unstructured medical texts.
method Conformal active learning for efficient data mining.
result Framework achieves strong performance with minimal manual labeling.
Cross-modal data programming speeds medical machine learning.
problem Labeling medical datasets is time-consuming and requires expert knowledge.
method Generates training labels by writing rules over auxiliary modalities, estimating accuracies and correlations.
result Matches or exceeds hand-labeling with statistical significance, faster and more flexible.
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.
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.
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.
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.
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.
Proposes BONMI for integrating noisy matrices from multi-source data.
problem Integrating noisy matrices from multi-source data with block-wise missingness.
method Exploits orthogonal Procrustes problem to align eigenspaces and completes missing blocks.
result Statistical rate for eigenspace of underlying matrix comparable to independently missing assumption.
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.
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.
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.
Survey on understanding neural networks for medical applications.
problem Black-box nature of deep neural networks hinders their use in critical applications.
method Comprehensive review of interpretability studies in neural networks.
result Interpretability research is crucial for the acceptance of neural networks in medical diagnosis.
Automated generation of medical reports from chest x-rays using expert annotations.
problem Generating long, unstructured text from medical images with context and consistency.
method First learn visually-informative medical concepts from raw reports, then use these concepts to auto-generate structured reports from images.
result Validation on OpenI dataset shows auto-generated reports are consistent with manual annotations.
Hybrid AI and rule-based framework de-identifies medical imaging data.
problem De-identifying medical imaging data to protect PHI and PII.
method Combines rule-based and AI techniques with uncertainty quantification.
result Robust performance across benchmark datasets and regulatory standards.
Deep learning predicts ICU mortality with enhanced interpretability.
problem Improving mortality prediction accuracy and clinician trust in AI.
method Trained a deep learning model on MIMIC-III to interpret nursing notes.
result Model reaches ROC of 0.8629, outperforming SAPS-II.
A tool simplifies neural network training for medical image analysis.
problem Difficulties in training neural networks for medical image analysis.
method Intuitive interface for WSI annotation and display, human-in-the-loop strategy.
result Improved network performance through iterative annotation.
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.
O-MedAL optimizes medical image analysis with online active deep learning.
problem Improving accuracy in medical image analysis with limited labeled data.
method Online Active Deep Learning method that queries examples maximizing average distance to training set.
result Significant performance improvements, including 6.30% accuracy boost with 25% labeled data.
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.
ChatGPT's medical response accuracy is 56%, but studies vary widely.
problem Lack of standard guidelines for evaluating ChatGPT's performance in medicine.
method Systematic review and meta-analysis of 17 studies.
result ChatGPT's overall integrated accuracy in medical queries is 56%.
Study uses multimodal machine learning to predict ICD-10 codes.
problem Improving accuracy and interpretability of ICD-10 code predictions.
method Developed separate models for text and tabular data, integrated using an ensemble method.
result Best-performing model achieved micro-F1 of 0.7633 and micro-AUC of 0.9541.
Study uses social media analytics to identify exercise-related topics.
problem Understanding exercise-related discussions on social media.
method Data collection, topic modeling, and data annotation.
result 86% of detected topics were meaningful after annotation.
Enhances machine learning with background knowledge through feature generation.
problem Machine learning struggles with small datasets.
method Generates features from auxiliary datasets not similar to the original task.
result Significant improvement in various learning tasks.
Optimal Survival Trees improve accuracy in medical data analysis.
problem Analyzing censored outcomes in medical data.
method Mixed-integer optimization and local search techniques.
result Improves accuracy in large datasets compared to existing methods.
Combines LSTM with HMM to improve interpretability of RNNs.
problem Making deep neural networks more understandable and interpretable.
method Integrates LSTM and HMM, training them sequentially or jointly.
result A hybrid model outperforms standalone LSTM, especially on smaller datasets.
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
problem Impact of medication non-adherence on adverse outcomes in schizophrenia patients.
method Survival analysis, causal inference methods (T-learner, S-learner, nearest neighbor matching), different amounts of longitudinal information.
result Non-adherence to schizophrenia meds advances adverse events by 1 to 4 months.