Novel metrics improve machine learning models for ICU patient care.
problem Predicting vital sign trajectories for early detection of adverse events.
method Developed novel performance metrics aligned with clinical contexts, validated on simulated and real datasets, and optimized neural networks using these metrics.
result Neural networks trained with these metrics excel in predicting clinically significant events.
Novel framework for uncertainty quantification in metric spaces.
problem Uncertainty quantification in regression models with metric responses.
method Developed algorithms for large datasets, agnostic to predictive models, with asymptotic and non-asymptotic guarantees.
result Asymptotic and non-asymptotic guarantees for special cases, demonstrated in clinical applications.
A new method uses asymmetric Shapley values to assess gene importance in clinical prediction models.
problem Clinical prediction models struggle with assessing the importance of high-dimensional features like genomics.
method Derive efficient algorithms to compute local and global asymmetric Shapley values for a mixed-dimensional prediction model.
result Asymmetric Shapley values provide a more suitable alternative to quantify feature importance in clinical prediction models.
This study shows unstructured clinical notes can improve mortality prediction.
problem Lack of effective use of unstructured clinical notes in mortality prediction.
method Used a hierarchical architecture with convolutional and recurrent layers to predict in-hospital mortality from unprocessed clinical notes.
result Achieved higher metrics in mortality prediction compared to structured data approaches.
Study identifies key aspects of explainable ML for clinical trust.
problem Lack of concrete definitions for usable explanations in clinical settings.
method Surveyed clinicians from two specialties to understand their needs for explainability.
result Characterized specific aspects of explainability that improve trust in ML models.
The paper proposes a method to predict the performance of data-driven algorithms using surrogate models.
problem Improving the performance prediction of data-driven knowledge discovery algorithms.
method Surrogate-assisted performance prediction using evolutionary modeling of clinical pathways.
result The proposed approach provides interpretable prediction of algorithm performance and quality.
Enhances generative model for clinical data privacy and accuracy.
problem Data privacy in electronic patient records.
method Improves a time-series generative model with privacy safeguards.
result DP-TimeGAN achieves a mean authenticity of 0.778 on the CKD dataset.
Develops CSI for predicting disease progression from sparse data.
problem Lack of frequent clinical metrics for disease progression.
method Machine learning framework (CSI) for analyzing disease progression from sparse observations.
result CSI effectively predicts disease progression from sparse data.
MRI identifies chronic symptoms in mTBI patients.
problem Chronic symptoms in mTBI patients are hard to characterize.
method Multi-parametric MRI and low-dimensional projection.
result MRI metrics correlate with patient symptoms.
AI system predicts acute critical illness from EHRs with explainability.
problem Lack of clinical interpretability in AI predictions for acute critical illness.
method Developed an explainable AI early warning score (xAI-EWS) system.
result System provides clinicians with insights into EHR data explaining predictions.
SAFER improves personalized treatment recommendations for dynamic clinical contexts.
problem Personalized treatment optimization in evolving clinical contexts with safety concerns.
method Integrates structured EHR and clinical notes, uses conformal prediction for safe recommendations.
result SAFER outperforms state-of-the-art baselines in recommendation metrics and mortality rates.
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing int…
Framework detects brain tumors robustly from MRI images.
problem Low clinical incidence of brain tumor cases makes diagnosis challenging.
method YOLOv8n for detection, DeiT for classification, PTP metric for evaluation.
result F1-score of 0.92 achieved with reduced computational resources.
Enhances clinical trial predictions by quantifying uncertainty.
problem Uncertainty in medical diagnosis and drug discovery predictions.
method Selective classification integrated with Hierarchical Interaction Network (HINT).
result Significant improvement in PR-AUC, F1, ROC-AUC, and overall accuracy.
Study examines impact of fairness penalties on clinical risk prediction models.
problem Widespread health disparities in machine learning-guided clinical decision-making.
method Empirical study across multiple databases, outcomes, and sensitive attributes.
result Penalizing fairness violations nearly universally degrades model performance and fairness metrics.
New accuracy measure Ha improves AI system assessment in clinical practice.
problem Inadequate metrics for assessing AI system performance in clinical settings.
method Introducing H-accuracy (Ha) as a more informative measure.
result H-accuracy is a generalization of balanced accuracy and related to Net Benefit.
Evaluation metrics for prediction models don't fully reflect intervention impact.
problem Standard metrics don't accurately reflect reduction in patient outcomes from model use.
method Synthesized and discussed various evaluation methods, analyzed with simulated and real data.
result Evaluations without interventional data are limited or require strong assumptions.
New diagnostics detect variability in individual risk estimates from machine learning models in healthcare.
problem Variability in individual risk estimates from machine learning models in healthcare, leading to unreliable treatment decisions.
method Proposed evaluation framework using empirical prediction interval width and empirical decision flip rate diagnostics.
result Randomness in optimization and initialization can lead to substantial individual-level variability in risk estimates, affecting clinical decisions.
The paper predicts diseases using both clinical and genomics data.
problem Clinical predictions using genomics data are not common.
method Integrated clinical and genomics datasets, machine learning, Principal Component Analysis for feature selection.
result 73% accuracy in predicting 75 disease classes.
Improved model predicts ICU readmission and mortality with interpretable results.
problem Lack of clinically interpretable predictions from deep learning models on clinical notes.
method Augmented a convolutional model with an attention mechanism for clinical note prediction.
result Attention mechanism improves prediction performance while providing interpretable results.
Generative AI models improve clinical trial data by generating survival outcomes.
problem Generating valid survival outcomes for clinical trials with synthetic data.
method A variational autoencoder (VAE) that jointly generates mixed-type covariates and survival outcomes.
result The method outperforms GAN baselines on fidelity, utility, and privacy metrics.
Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.
problem Difficult prediction of medium-horizon Alzheimer's disease progression due to tied clinical scores and irregular biomarker observations.
method Developed a residual gap-aware transformer that combines statistical reference with transformer-based residual learning.
result The proposed model reduces mean error and improves prediction-observation correlation compared to baseline models.
Clinical AI models fail to transfer between sites due to site-specific practices.
problem Clinical AI models perform poorly at new sites.
method Identify and isolate site-specific clinical practices affecting data distribution.
result A potential solution to improve model transferability.
Improved AI lung ultrasound segmentation using expert confidence values.
problem Label uncertainty in lung ultrasound due to subjective interpretation by radiologists.
method Designing a data annotation protocol capturing expert confidence, training AI on binarized labels with confidence thresholds.
result Improved AI segmentation and better clinical outcomes (e.g., S/F oxygenation ratio estimation, patient readmission prediction).
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.
We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. However, most registration algorithms are designed for high-resolut…
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.
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 deep learning method improves 4D Flow MRI super-resolution under domain shift.
problem Domain shift in low-resolution 4D Flow MRI data.
method Distributional deep learning framework for domain generalization.
result Framework significantly outperforms traditional methods in real data applications.
Machine learning predicts patient recruitment for clinical trials.
problem Improving patient recruitment prediction for clinical trials.
method Machine learning methods applied to historical clinical trial data.
result Reduced prediction error compared to current industry standards.
Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the longand irregular clinical event sequences. We make the observation that clinical events at a…
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic pr…
New dataset and approach improve skin cancer detection accuracy.
problem Lack of patient clinical information in automated skin cancer detection.
method Introduced a new dataset with clinical images and patient information. Combined clinical data with dermoscopy images using deep learning models.
result Combining clinical data improves skin cancer detection accuracy by around 7%.
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.
Spatially-aware metrics improve uncertainty evaluation in segmentation.
problem Uncertainty evaluation metrics treat voxels independently, ignoring spatial context.
method Proposed three spatially aware metrics incorporating structural and boundary information.
result Improved alignment with clinically important factors and better discrimination between uncertainty patterns.
Survey on DRL for clinical decision support.
problem Improving clinical decision-making through AI.
method Deep reinforcement learning with DNN.
result Survey and comparison of DRL algorithms in clinical applications.
A clinical Meta-Dataset from TCGA for multi-task learning.
problem Clinical decision making requires considering multiple factors; current benchmarks lack consistency and variety.
method Developed a Meta-Dataset with 174 tasks from TCGA, using regression and neural networks.
result Demonstrated the feasibility of predicting multiple clinical variables from gene expression data.
Numeracy is the ability to understand and work with numbers. It is a necessary skill for composing and understanding documents in clinical, scientific, and other technical domains. In this paper, we explore different strategies for modelling numerals with language models, such as memorisation and digit-by-digit composi…
Study develops a new algorithm for assessing clinical trial abstracts.
problem Limited tools for assessing qualitative statements in clinical research.
method Developed a three-class sentiment classification algorithm using BERT model.
result Algorithm achieved 91.3% classification accuracy and 0.92 macro F1-Score.
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolut…
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 predicts patient trajectories and interventions from EMR data.
problem Forecasting patient outcomes from EMR data.
method Deep state space generative model capturing latent state dynamics.
result Model outperforms state-of-the-art methods on real EMR data.
The paper explores using historical data to improve clinical trial analysis by optimizing covariate weights.
problem Limited covariates in small clinical trials reduce the effectiveness of analysis.
method Leverage historical data to pre-specify covariate weights as a composite covariate.
result A composite covariate improves the cost/benefit ratio and reduces overfitting in small clinical trials.
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, …
Study examines biases in clinical word embeddings, revealing performance gaps across groups.
problem Biases in clinical word embeddings leading to performance differences across groups.
method Pretrained BERT models on MIMIC-III, fill-in-the-blank method, fairness evaluation on clinical tasks.
result Classifiers trained from BERT representations exhibit statistically significant differences in performance across groups.
Study improves healthcare time series imputation by considering structured missingness.
problem Structured missingness in clinical data impacts time series imputation models.
method Analysis of different masking strategies on imputation methods using PhysioNet Challenge 2012 dataset.
result Masking choices significantly affect imputation accuracy and clinical prediction.
New method detects biomarker-treatment interactions in clinical trials.
problem Detecting interactions between high-dimensional biomarkers and treatments in randomized trials.
method Two-stage penalized regression screening using ridge regression for multivariate screening.
result Ridge regression screening provides greater power than traditional methods in correlated data.
Improved language model for French clinical reports achieves state-of-the-art performance in medical NLP tasks.
problem Lack of specialized language models for French clinical reports.
method Adapted a general pre-trained language model (CamemBERT) to French clinical reports using a corpus of 21M reports.
result Pretrained and fine-tuned models improved F1-score by 3 percentage points on APMed task.