Deep RL optimizes lab test scheduling for better patient outcomes and cost savings.
problem Redundant lab tests lead to cost and patient discomfort.
method Deep reinforcement learning for optimal scheduling.
result Deep RL policy outperforms heuristic scheduling in both accuracy and cost.
Tests for Esophageal cancer can be expensive, uncomfortable and can have side effects. For many patients, we can predict non-existence of disease with 100% certainty, just using demographics, lifestyle, and medical history information. Our objective is to devise a general methodology for customizing tests using user pr…
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.
Detection of interactions between treatment effects and patient descriptors in clinical trials is critical for optimizing the drug development process. The increasing volume of data accumulated in clinical trials provides a unique opportunity to discover new biomarkers and further the goal of personalized medicine, but…
Unified Bayesian framework improves clinical trial hypothesis testing.
problem Lack of transparency and inability to quantify evidence in traditional P-values.
method Interval null hypothesis framework combined with Bayes factor-based tests.
result Bayesian interval hypothesis testing ensures frequentist error control and interpretability.
Test for quasi-independence in ordered time data.
problem Determining dependence beyond temporal ordering.
method Nonparametric statistical test considering infinite alternatives.
result Better power and computational efficiency compared to existing methods.
Paper proposes machine learning model for early Alzheimer's diagnosis.
problem Early and accurate diagnosis of Alzheimer's Disease.
method Machine learning models, demographic, biomarker, and cognitive test data.
result 90% accuracy and 87% accuracy in predicting Alzheimer's development.
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.
Clarifies the confidence interval approach for bioequivalence testing.
problem Ensuring the reliability of bioequivalence testing methods.
method Clarifies the conditions under which a 100(1-2α)% confidence interval yields a size-α test.
result A 100(1-2α)% confidence interval approach for bioequivalence testing yields a size-α test only when the two one-sided tests are 'equal-tailed'.
Proposes dynamic borrowing method for historical data in clinical trials.
problem Insufficient statistical power in rare and pediatric disease clinical trials.
method Dynamic borrowing method based on frequentist approach using similarity measures.
result Demonstrates usefulness of dynamic borrowing in reanalyzing clinical trial data.
The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according…
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.
Transforms any test into anytime-valid with sample savings.
problem Sequential data invalidates classical test guarantees.
method Predicts test outcomes to create anytime-valid stopping rules.
result Ensures Type-I error control and near-optimal power.
The paper introduces sanity tests to detect spurious correlations in AI-guided radiology systems.
problem Detecting when AI systems perform well on development data for the wrong reasons.
method Design and implementation of sanity tests to identify spurious correlations.
result Sanity tests can identify spurious correlations in AI-guided radiology systems.
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.
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.
Clinical models trained on EHRs degrade in performance over time due to data drift.
problem Model performance degradation over time in clinical settings.
method Accessed year of care for each record in MIMIC, aggregated features into clinical concepts, and tested mitigation strategies.
result State-of-the-art models show significant performance drops when tested on future data compared to historical data.
Study evaluates deep learning methods for dermatology, finding they perform poorly under non-ideal conditions.
problem Lack of robustness of deep learning methods in dermatology under real-world conditions.
method Simulated non-ideal conditions on user-submitted dermatology images.
result Deep learning methods show significant drop in accuracy and prediction changes under non-ideal conditions.
Machine learning models detect COVID-19 from routine blood tests.
problem Separating COVID-19 from other viral pneumonias using blood tests.
method Employed random forests and support vector machines on blood data.
result SVM-based classifier achieves 84% accuracy in detecting COVID-19.
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%.
Machine learning improves diagnostic test accuracy for bovine tuberculosis.
problem Improving diagnostic test sensitivity for bovine tuberculosis.
method Machine learning to assess risk landscapes and predict infection.
result Test sensitivity improved, detecting 240 more infected herds per year.
Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.…
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.
Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI. We developed data-driven prediction models to estimate…
We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task. Our system was trained on a decade of clinical x-rays (~53,000 studies) and can be applied to clinical data, automatically excluding inappropriate and technically unsatisfactory …
C3T-Budget optimizes drug efficacy in dose-finding trials with budget and safety constraints.
problem Heterogeneous patient populations and budget constraints make dose-finding clinical trials challenging.
method Contextual constrained clinical trial algorithm that maximizes drug efficacy while learning subgroup responses.
result Demonstrates efficient budget usage and balanced learning-treatment trade-off in simulated trials.
Predicting blood lactate levels helps manage ICU patients without invasive tests.
problem Predict blood lactate levels accurately in ICU patients without invasive tests.
method Defined a benchmark problem, evaluated different prediction algorithms, and investigated missing value imputation methods.
result Promising prediction results show the potential of machine learning in ICU care.
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.
Deep learning predicts heart failure readmission from clinical notes.
problem Predicting and preventing heart failure readmission.
method Convolutional Neural Networks (CNN) trained on clinical notes.
result Deep learning models outperform traditional machine learning methods in readmission prediction.
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to f…
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.
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.
Automated brain CT image retrieval from traumatic brain injury cohorts using deep neural networks.
problem Manual image retrieval of whole brain CT scans from large clinical cohorts is time-consuming and resource-intensive.
method Proposes a deep convolutional neural network (dMIR) for automated classification of 2D montage images.
result Achieved high accuracy (f1=1.0) for validation and testing data sets.
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.
Method predicts ODX scores for breast cancer patients based on clinical data.
problem Predicting ODX scores for breast cancer patients to aid decision-making.
method Distributional random forest approach using 9 clinico-pathological characteristics.
result Correctly predicted 92% of low risk and 40.2% of high risk patients.
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…
Privacy-preserving inference for clinical trials using differential privacy.
problem Balancing knowledge sharing and privacy in healthcare data.
method Differential privacy (DP) applied to log-linear belief updates in distributed settings.
result Differentially private, distributed inference methods outperform existing techniques.
Paper develops a model to identify LVO in stroke patients.
problem Early identification of LVO in stroke patients to prevent severe outcomes.
method Used demographic, clinical, and CT scan data to build three hierarchical models.
result Level-3 model with clinical and imaging features achieved best performance.
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…
Study validates machine learning models for patient outcomes using various methods.
problem Validating machine learning models for patient outcomes in electronic health records.
method Used three state-of-the-art machine learning methods (random forest, gradient boosting, logistic regression) to predict patient outcomes and assess feature importance.
result Permutation tests applied to random forest and gradient boosting models showed the most agreement with clinical interpretation of feature importance.
Hidden stratification causes machine learning models to fail on rare but important patient subgroups.
problem Machine learning models fail on rare patient subgroups not identified during training or testing.
method Assessed techniques for measuring and describing hidden stratification effects on multiple medical imaging datasets.
result Evidence of hidden stratification leading to over 20% performance differences on clinically important subsets.
A new method boosts survival analysis by stratifying patients and removing noise covariates.
problem Weak detection of treatment differences in randomized clinical trials due to patient heterogeneity.
method 5-Step Stratified Testing and Amalgamation Routine (5-STAR) using elastic net Cox regression and conditional inference trees.
result The 5-STAR routine significantly improves power in detecting treatment effects compared to traditional methods.
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.
Paper introduces tractographic feature to predict stroke outcomes.
problem Predicting stroke outcomes using lesion volume alone is limited.
method Tractographic feature combining lesion and connectome data.
result Tractographic feature outperforms stroke volume in predicting mRS grades.
New method infers viral load from pooled tests.
problem Inefficient viral load inference in pooled testing.
method Message passing algorithm with PCR noise function.
result Accurate viral load inference possible.
We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims t…
AI4COVID-19 app diagnoses COVID-19 from cough samples.
problem Scalable screening tool for COVID-19 testing.
method Transfer learning and multi-pronged AI architecture.
result AI4COVID-19 can distinguish COVID-19 coughs from others.
Method detects batch heterogeneity in genomic data.
problem Batch effects confound genomic diagnostics.
method Bayesian model evidence clustering.
result Detects batch effects without known labels.