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.
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.
Study investigates deep learning model's reliability in clinical MRI data.
problem Tackles reliability of DL models in clinical out-of-distribution MRI data.
method Investigated performance of DL model trained on diverse datasets compared to clinical data.
result Model performs better in similar protocols but worse in clinical data with different tissue contrasts.
Late fusion of clinical notes and physiological data improves ICU mortality prediction.
problem Improving ICU mortality prediction using multimodal data.
method Late fusion of clinical notes and physiological time series data with a deep learning architecture.
result Late fusion approach provides statistically significant improvement in mortality prediction performance.
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.
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.
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%.
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.
Bayesian approach for handling incomplete clinical data.
problem Challenges in machine learning with multimodal, incomplete clinical data.
method Generative and discriminative learning, semi-supervised strategy, imputation of missing views.
result Automatic imputation of missing views and robust inference across different data sources.
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.
Wide and deep neural network predicts Alzheimer's progression from shape and clinical data.
problem Predicting Alzheimer's disease progression from shape and clinical data.
method Fused anatomical shape and tabular clinical data in a neural network, employing survival analysis loss.
result The model outperforms shape and clinical models individually.
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.
A new imputation model for clinical data captures both cross-sectional and temporal correlations.
problem Missing values in multivariable time series clinical data.
method Integrates Gaussian processes with mixture models and individualized mixing weights.
result The proposed model provides more accurate imputation than benchmarks on real-world and synthetic datasets.
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.
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.
Predict sepsis early from EHR data with aggregated clinical events.
problem Predict sepsis from clinical data in EHR with temporal interactions.
method Aggregates heterogeneous clinical events, captures temporal interactions with LSTM.
result Achieved high utility score (0.321) in PhysioNet/Computing in Cardiology Challenge 2019.
Causal ML predicts treatment outcomes, aiding personalized medicine.
problem Predicting individualized treatment effects for personalized medicine.
method Flexible, data-driven methods using causal inference with clinical trial and real-world data.
result Causal ML allows for estimating individualized treatment effects.
Unified model combines neural networks and dictionary learning for clinical predictions from brain data.
problem Predicting clinical severity from brain imaging data.
method Combines neural networks with dictionary learning to model patient-specific and shared features.
result Unified model outperforms state-of-the-art methods in predicting clinical severity.
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…
Proposes clustering as a new evaluation method for clinical knowledge embedding.
problem Traditional Link Prediction evaluation protocol loses information and harms model accuracy.
method Proposes Clustering Evaluation Protocol as an alternative.
result Experimental results show the proposed protocol can potentially replace Link Prediction.
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.
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.
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.
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.
The medical research facilitates to acquire a diverse type of data from the same individual for particular cancer. Recent studies show that utilizing such diverse data results in more accurate predictions. The major challenge faced is how to utilize such diverse data sets in an effective way. In this paper, we introduc…
TrialGraph uses graph machine learning to improve clinical trial design and predict side effects.
problem Complexity and cost in clinical trials hinder drug development.
method Curated clinical trial data set converted to graph-structured formats, applied graph machine learning algorithms.
result MetaPath2Vec algorithm performed exceptionally well, improving prediction accuracy.
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absen…
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, …
Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and gen…
Clinical notes are a rich source of information about patient state. However, using them to predict clinical events with machine learning models is challenging. They are very high dimensional, sparse and have complex structure. Furthermore, training data is often scarce because it is expensive to obtain reliable labels…
Disease phenotyping algorithms process observational clinical data to identify patients with specific diseases. Supervised phenotyping methods require significant quantities of expert-labeled data, while unsupervised methods may learn non-disease phenotypes. To address these limitations, we propose the Semi-Supervised …
A new hybrid federated learning algorithm for combining clinical and omics data.
problem Combining clinical and omics data in federated learning settings.
method Reformulated Kernel Regularized Least Squares algorithm for hybrid federated learning.
result Validation of two variants of the hybrid algorithm on well-established datasets.
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…
MimickNet approximates clinical ultrasound post-processing without proprietary data.
problem Matching proprietary clinical-grade ultrasound post-processing techniques.
method Deep learning framework MimickNet that transforms raw DAS beams into post-processed images.
result MimickNet achieves high SSIM scores (0.930-0.967) on test sets.
Compressed sensing in MRI enables high subsampling factors while maintaining diagnostic image quality. This technique enables shortened scan durations and/or improved image resolution. Further, compressed sensing can increase the diagnostic information and value from each scan performed. Overall, compressed sensing has…
FRESH combines patient-level and aggregate-level data for better clinical decision making.
problem Combining patient-level and aggregate-level data for clinical decision making.
method FRESH method that re-calibrates a patient-level model to match specified aggregate statistics.
result Unified data-efficient model for clinical decision making.
Unified Bayesian framework for missing data imputation and prediction in clinical time series.
problem High prevalence of missing values in clinical time series data.
method Unified Bayesian recurrent framework for imputation and prediction.
result Strong performance gains over state-of-the-art methods on mortality prediction tasks.
Predictive models identify patients at risk of severe COVID-19.
problem Identifying patients at risk of severe COVID-19 to ease healthcare strain.
method Machine learning on routinely collected clinical data.
result Models predict positive SARS-CoV-2 tests, hospitalizations, and critical care with high accuracy.
Method augments CTNs for ICD coding with neural network imputation.
problem Time-consuming manual annotation of CTNs for ICD coding.
method Semi-self-supervised neural network imputation of clinical features.
result Data augmentation improves ICD coding performance significantly.
Language models improve clinical prediction models using EHR data.
problem Limited patient data for training clinical prediction models.
method Using patient representation schemes from natural language processing.
result 3.5% mean improvement in AUROC on five prediction tasks.
Method predicts biomarker trajectories with uncertainty bands for Alzheimer's disease.
problem Uncertainty in biomarker predictions poses risks in clinical deployment.
method Conformal prediction for randomly-timed biomarker trajectories.
result Conformal bands achieve desired coverage and are tighter than baseline.
In healthcare, patient risk stratification models are often learned using time-series data extracted from electronic health records. When extracting data for a clinical prediction task, several formulations exist, depending on how one chooses the time of prediction and the prediction horizon. In this paper, we show how…
AdaptiveNet tackles disease progression prediction in rheumatoid arthritis using deep neural networks.
problem Predicting disease progression in rheumatoid arthritis using clinical data.
method AdaptiveNet, a novel recurrent neural network architecture, that handles multiple lists of different events and missing data.
result AdaptiveNet outperforms classical baselines in disease progression prediction.
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.
New model selects more promising patients for knee osteoarthritis trials.
problem Selecting patients likely to benefit from osteoarthritis treatments.
method Multi-classifier prediction from longitudinal data, cost-sensitive learning, feature selection.
result Model reduces by 20-25% the number of patients showing no progression.
Challenge hides and seeks privacy in clinical time-series data.
problem De-identifying clinical time-series data while preserving utility and privacy.
method Synthetic data generation to preserve temporal dynamics and limit re-identification risk.
result A novel competition tracks synthetic data generation and patient re-identification.
FedRD improves risk difference estimation in federated learning for clinical outcomes.
problem Privacy-preserving model co-training in medical research is hindered by server-dependent architectures and focus on relative effect measures.
method FedRD is a server-independent, communication-efficient framework for federated risk difference estimation in distributed survival data.
result FedRD provides valid confidence intervals and hypothesis testing, and is asymptotically equivalent to pooled individual-level analysis.