Self-supervised learning improves ECG classification performance.
problem Label scarcity in clinical 12-lead ECG data.
method Adapted self-supervised methods to ECG domain, focusing on contrastive representations and latent forecasting.
result Contrastive predictive coding adaptation yields linear evaluation performance only 0.5% below supervised performance.
Self-supervised method improves biosignal models with limited labeled data and subjects.
problem Limited labeled data and subjects in biosignals datasets.
method Contrastive learning with subject-aware loss and data augmentation.
result Self-supervised embeddings yield competitive results compared to supervised methods.
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the non-sparseness of biosignals presents a major challenge to compressed sensing. This study…
Adversarial transfer learning improves stress assessment across users.
problem Transfer learning challenges in physiological biosignals.
method Disentangled nuisance-robust representations using adversarial networks.
result Adversarial framework enhances cross-subjects stress assessment.
The effectiveness of biosignal generation and data augmentation with biosignal generative models based on generative adversarial networks (GANs), which are a type of deep learning technique, was demonstrated in our previous paper. GAN-based generative models only learn the projection between a random distribution as in…
AutoTransfer improves subject transfer learning for biosignal datasets.
problem Subject transfer learning for challenging biosignal datasets.
method Regularization framework with mutual information or divergence penalties.
result Improves subject transfer learning performance on EEG, EMG, and ECoG datasets.
Proposes a framework for extracting consistent physiological features across users.
problem Variability of biosignals across different users and tasks.
method Adversarial feature extractor for disentangled universal representations.
result Up to 8.8% improvement in average accuracy of classification.
This paper investigates the use of distributed processing on the problem of emotion recognition from physiological sensors using a popular machine learning library on distributed mode. Specifically, we run a random forests classifier on the biosignal-data, which have been pre-processed to form exclusive groups in an un…
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.
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.
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…
Perceptrons are neuronal devices capable of fully discriminating linearly separable classes. Although straightforward to implement and train, their applicability is usually hindered by non-trivial requirements imposed by real-world classification problems. Therefore, several approaches, such as kernel perceptrons, have…
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.
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.
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…
Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we …
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.
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.
In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate to…
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.
The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opport…
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.
Deep learning analyzes healthcare provider actions and patient outcomes.
problem Understanding provider behavior in non-randomized healthcare settings.
method Deep causal behavioral policy learning (DC-BPL) using transformer architecture.
result Optimal provider policies identified for specific patient types.
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.
Discusses handling intercurrent events in clinical trials with time-to-event outcomes.
problem Handling intercurrent events in clinical trials with time-to-event outcomes.
method Defines estimands and six ICE handling strategies, including new competing-risk strategy.
result Novel methods for handling intercurrent events in clinical trials with time-to-event outcomes.
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.
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.
Framework improves clinical timeline reconstruction from text and tables.
problem Temporal precision and event timing in clinical narratives and EHRs.
method Retrieval-augmented multimodal alignment framework.
result Consistently improves absolute timestamp accuracy and temporal concordance.
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals. This development equips doctors and medical staff with tools to evaluate their hypotheses and hence make more precise decisions. Although most current research in the literatu…
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.
Current clinical practice to monitor patients' health follows either regular or heuristic-based lab test (e.g. blood test) scheduling. Such practice not only gives rise to redundant measurements accruing cost, but may even lead to unnecessary patient discomfort. From the computational perspective, heuristic-based test …
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…
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
problem Capturing latent, time-varying dependencies among clinical features in time-series data.
method Chain-of-Influence (CoI) framework constructs an explicit, time-unfolded graph of feature interactions.
result Achieves state-of-the-art predictive performance (AUROC of 0.960 on CKD progression and 0.950 on ICU mortality).
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.
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.
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.
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventiv…
Convolutional neural network improves assertion detection in multi-label clinical text.
problem Detecting assertions in multi-label clinical text with rich descriptions.
method Developed a CNN architecture for multi-label scope detection.
result At least 12% improvement over state-of-the-art on multi-label clinical text.