Machine learning and C-NLP improve emergency department triage accuracy.
problem Improving accuracy in emergency department triage to manage increased volume.
method Historical EHR data, clinical natural language processing, and ML algorithms (KATE).
result KATE predicts ESI acuity assignments 75.9% of the time, significantly better than nurses.
UMAP visualizes patient phenotypes from EHR data for emergency triage.
problem Interpreting high-dimensional EHR data for rapid patient triage.
method UMAP for non-linear dimensionality reduction, Gaussian mixture models for clustering.
result UMAP reveals clinically relevant patient phenotypes from EHR data.
Deep learning for COVID-19 diagnosis using CXR images with limited data.
problem Difficulty in collecting CXR data for deep learning due to the pandemic.
method Patch-based convolutional neural network with limited trainable parameters.
result Achieves state-of-the-art performance and interpretable saliency maps.
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We per…
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.
MAMMO reduces radiologist workload by triaging mammograms, improving accuracy.
problem Reducing radiologist workload while maintaining diagnostic accuracy.
method Developed a clinical decision support system with a multi-task learning CNN and triage network.
result Reduced radiologist workload by 42.8% with improved overall diagnostic accuracy.
Deep learning model predicts severe COVID-19 outcomes.
problem Predicting severe COVID-19 outcomes in ED patients.
method Deep feature fusion model using EHR data and CXR images.
result CO-RISK score achieved AUC of 0.95 and 0.92 for 24 and 72 hours predictions, superior to human performance.
Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admit ward-bed to receive a patient is delayed until an admission decision is made by a doctor. To reduce the overcrowding and waiting time of E…
Gradient-based algorithm improves model performance with triage.
problem Improving model accuracy with human expert involvement.
method Formal characterization of triage, optimal triage policy as deterministic threshold rule, gradient-based algorithm.
result Gradient-based algorithm finds triage policies and models with increasing performance.
PCA-Triage optimizes sensor data sampling for IoT networks.
problem Excessive sensor data in IoT networks exceeds available bandwidth.
method PCA-Triage uses streaming incremental PCA loadings to adaptively triage sensor data.
result PCA-Triage achieves high inference performance with minimal bandwidth usage.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.
Study predicts email triage actions using content-based approach.
problem Predicting user triage actions on incoming emails.
method Formulated as a recommendation problem, uses content-based approach with additional similarity features.
result Proposed framework achieves better performance than state-of-the-art deep recommendation methods.
We explain increases in clinical risk predictions over time.
problem Tackling the challenge of explaining dynamic risk increases in clinical settings.
method Developed methods to extend static attribution techniques to dynamic settings, addressing challenges specific to time-series data.
result Identified and addressed challenges specific to dynamic risk estimation, improving clinical alert explanations.
SpeakerStew verifies 46 languages with reduced training and inference costs.
problem Speaker verification for 46 languages with smart speaker interactions.
method Pooling multilingual data, triage between text-dependent and text-independent models.
result Training on multiple languages generalizes well and reduces computational requirements.
Study assesses CNN model robustness to noise in low-cost CT scans.
problem Evaluate CNN model performance on noisy, artifact-prone low-cost CT images.
method Developed and tested a CNN model for head CT triage, varying tube current and projections.
result Model remains robust to reduced tube current and fewer projections, maintaining AUROC close to original.
AI detects oral pre-cancerous lesions with high accuracy.
problem Manual screening of oral cavity cancer is expensive and lacks specialists.
method Deep convolutional neural networks (DCNNs) using transfer learning.
result DCNN models achieve high accuracy in distinguishing between benign and pre-cancerous tongue lesions.
Bayesian neural networks improve SHD classification and uncertainty quantification.
problem Improving screening for structural heart disease using noninvasive ECG and echocardiography.
method Comparing frequentist and Bayesian neural network classifiers on the EchoNext dataset.
result Bayesian classifiers provide more robust uncertainty quantification.
Dynamic CBDT improves treatment effect estimation in clinical data.
problem Estimating heterogeneous treatment effects in observational data with high accuracy and interpretability.
method Dynamic Regularized Causal Boosted Decision Trees (CBDT) integrating variance regularization and calibration.
result Significantly improved estimation accuracy and reliable coverage of true treatment effects.
Framework uses human judgment to distinguish algorithmically indistinguishable cases.
problem Clarifying human-AI collaboration in prediction and decision tasks.
method Integrates human judgment to distinguish algorithmically indistinguishable cases.
result Improves performance of any feasible algorithmic predictor.
The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and sol…
Improved forecasting of suicide attempts using LSGPs for patients with little data.
problem Challenges in predicting suicide attempts due to their rarity and patient heterogeneity.
method Introduced Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity.
result LSGPs outperform baseline models, even without kernel-design, and offer new insights into patient similarity.
Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.
problem Improving seizure detection accuracy for wearable devices.
method Transfer learning with tensor kernel machine using canonical polyadic decomposition.
result Patient fine-tuned model achieves high performance with smaller model size.
In this work we explore the use of metric index structures, which accelerate nearest neighbor queries, in the scenario where we need to interleave insertions and queries during deployment. This use-case is inspired by a real-life need in malware analysis triage, and is surprisingly understudied. Existing literature ten…
Personalized models explain TB treatment outcomes considering patient context.
problem Heterogeneity in TB treatment outcomes due to co-morbidities.
method Multi-task learning approach encoding patient context into personalized models.
result Identifies anemia, age of onset, and HIV as influential for treatment efficacy.
Simulates patient pathways to detect delayed rare disease diagnoses.
problem Delayed rare disease diagnoses in France, causing health system and patient harm.
method Probabilistic modelling of patient pathways to create an alert system.
result Alert system detects and refers wandering patients to CRMRs.
Clusters of ACS patients identified for better therapeutic stratification.
problem Data-driven classification and subtyping of ACS patients for improved treatment.
method Outcome-driven clustering using a multi-task neural network with attention.
result Seven patient clusters with distinct characteristics and risk profiles identified.
The paper presents a method to score patient engagement in care programs and predicts their response.
problem Improving health outcomes of high-need patients through better patient engagement.
method Data-driven behavioral engagement scoring pipeline for two aspects of patient engagement.
result The scoring method successfully predicts patient engagement and provides interpretable insights.
Model predicts wound and episode-level readmission risk and time to re-admit.
problem Identify patients at high risk of re-admission to prevent wound recurrences and reduce healthcare costs.
method Data-driven analysis of wound care and episode-level patient data.
result Model achieves high recall and precision for predicting re-admission risk and time.
Currently, approximately 30% of epileptic patients treated with antiepileptic drugs (AEDs) remain resistant to treatment (known as refractory patients). This project seeks to understand the underlying similarities in refractory patients vs. other epileptic patients, identify features contributing to drug resistance acr…
System detects multiple patients' behaviors in real-time using mmWave radar and CNN.
problem Real-time patient behavior monitoring in hospitals.
method Used mmWave radar for tracking and collecting Doppler patterns. Created a three-layer CNN model for behavior classification.
result System achieved very good inference accuracy in predicting patient behaviors in real-time.
Deep learning model creates patient representations for scalable EHR-based stratification.
problem Challenges in summarizing and representing patient data from EHRs prevent scalable stratification analysis.
method Unsupervised framework based on deep learning (ConvAE) using word embeddings, CNNs, and autoencoders.
result ConvAE significantly outperformed baselines in clustering diverse patient cohorts, identifying clinically relevant subtypes.
Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis diagnostic criteria fail to account for significant underlying heterogeneity, both between patients as well as over time in a single patient. W…
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.
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…
Longitudinal patient data has the potential to improve clinical risk stratification models for disease. However, chronic diseases that progress slowly over time are often heterogeneous in their clinical presentation. Patients may progress through disease stages at varying rates. This leads to pathophysiological misalig…
Study automates detection of visitation disruptions in ICU patients.
problem Difficulty in detecting frequent visitation disruptions in ICU patients.
method Used DensePose R-CNN model to count people in video frames, analyzed disruptions and patient outcomes.
result Automated method detects visitation disruptions, impacts on pain and length of stay examined.
Model predicts treatment initiation from clinical data using patient-clinician relations.
problem Predicting treatment initiation from clinical time series data considering patient-clinician relations.
method Graph-Augmented Time-Sensitive Model using top eigenvectors of graph Laplacian.
result Relational similarity improves prediction over baselines, e.g., 5% improvement in AUPRC.
A new model uses GPs and latent force models to predict patient responses to drugs.
problem Challenges in modeling short-term effects of drugs on patient physiology.
method Hybrid Gaussian process with latent force model for joint modeling of patient physiology and drug effects.
result The model accurately predicts patient responses to three common drugs, showing competitive performance.
Bayesian methods improve group testing for identifying infected patients.
problem Identifying infected patients from group testing results with false positives.
method Bayesian inference and belief propagation algorithm, combined with expectation-maximization method.
result True-positive rate improved by considering credible intervals.
Paper predicts IVF pregnancy rates from basic patient info.
problem Predicting IVF pregnancy rates from patient characteristics.
method Clustering patients into groups, then SVM models for each group.
result Support vector machine models achieve best overall performance.
Model identifies key problems in HIV patients' records.
problem Complex and time-consuming task of identifying patient problems from electronic health records.
method Unsupervised phenotyping approach that jointly learns phenotypes from structured and unstructured data.
result Learned phenotypes and their relatedness are clinically valid and surpass existing methods.
Study develops electronic phenotypes of ICU patient acuity.
problem Limited time for patient acuity assessments and imprecise clinical trajectory prediction.
method Developed electronic phenotypes using automated variable retrieval in electronic health records.
result Identified three phenotypes: persistently stable, persistently unstable, and transitioning from unstable to stable.
Deep learning clusters patient time-series data for better prognosis.
problem Clustering time-series data for patient phenotyping and prognosis.
method Deep predictive clustering with novel loss functions for future outcome distribution.
result Model achieves superior clustering performance and identifies meaningful patient subgroups.
Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that both the number of cancer patients admitted to the ICU and overall survival have increased since the millennium change. MIMIC-III, a freely accessible critical care data…
Machine learning detects NASH patients from medical claims data.
problem Detecting undiagnosed NASH patients for screening and management.
method Gradient-boosted decision trees trained on administrative medical claims data.
result Model precision for NASH detection is significantly higher than NASH incidence.
The paper tracks patient recovery using graphs of joint movement data.
problem Tracking individual patient recovery trajectories in physical therapy.
method Bayesian learning of Random Geometric Graphs from joint movement data.
result Optimal exercise routines can be recommended based on patient recovery data.
Reconstructs graph structure from noisy data samples.
problem Efficiently discover and model structures in high-dimensional data.
method Combining topological data analysis with numerical modelling.
result Recovery of graph structure from noisy point cloud samples.
Model predicts HU response for sickle cell patients.
problem Predicting which sickle cell patients will respond to Hydroxyurea.
method Developed Deep Artificial Neural Network models.
result 92.6% accuracy in predicting HbF response.