Study predicts onset of type II diabetes using survey data and machine learning.
problem Early diagnosis of type II diabetes from patient data.
method Developed an ensemble classifier using five classification algorithms.
result Ensemble model had an AUC of 0.834, indicating high performance.
Improved piano transcription by predicting onsets and frames together.
problem Polyphonic piano music transcription accuracy.
method Deep convolutional and recurrent neural network trained to predict pitch onsets and frames.
result Over 100% relative improvement in note F1 score on MAPS dataset.
Dr.VOT measures both positive and negative VOTs accurately in natural speech.
problem Accurate measurement of VOT in natural speech.
method Deep-learning model based on RNNs for structured prediction.
result Dr.VOT improves over state-of-the-art performance on VOT estimation.
LSTMs predict CHF onset 15 months in advance with 91.47% AUC.
problem Predicting congestive heart failure onset 15 months in advance.
method LSTM-based RNN architecture using 216,394 patient data.
result LSTMs achieve AUCROC of 0.9147 for CHF onset prediction.
Natural language processing predicts AKI onset in ICU patients.
problem Early detection of AKI in ICU patients to improve outcomes.
method Clinical notes were processed to generate word and concept embeddings. Five classifiers and a deep learning model were used to predict AKI.
result The best model achieved an AUC of 0.779 for predicting AKI onset.
AI predicts dementia onset from emotional face evaluations.
problem Early detection of dementia in aging societies.
method Behavioral responses analysis and AI regression.
result Encouraging AI-based prediction results for MoCA scores.
Convolutional networks predict seizure onset in the preictal phase.
problem Predicting the onset of focal seizures using EEG data.
method Wavelet transformation and convolutional filters to learn features; optimization for prediction horizon.
result A ten-minute prediction horizon for seizure onset.
Adversarial learning improves music transcription accuracy.
problem Conditional independence of labels in deep learning models limits transcription performance.
method Adversarial training scheme operating on time-frequency representations to reduce inter-label dependencies.
result Adversarial learning reduces error rate and increases model confidence.
Method detects lithium-ion battery knee onset for early warning.
problem Early warning of lithium-ion battery performance changes.
method Temporal dynamics extraction and Matrix Profile anomaly detection.
result Knee onset detected for categorizing battery health.
DeepAISE predicts sepsis onset with high accuracy and low false alarms.
problem Early prediction of sepsis in ICU patients to improve clinical situational awareness.
method Recurrent neural survival model that combines clinical criteria and treatment policies.
result DeepAISE produces the most accurate predictions (AUC=0.90 and 0.87) and lowest false alarm rates (FAR=0.20 and 0.26) compared to baseline models.
Method predicts PAF episodes up to 15 minutes in advance from ECGs.
problem Predicting Paroxysmal Atrial Fibrillation (PAF) from ECGs.
method Feature extraction of ECGs as multi-variate time series, kernel similarity-based classification.
result Comparable classification accuracy with state-of-the-art methods, predictive up to 15 minutes.
Study uses high-frequency data to predict ruble depreciation during crisis.
problem Predicting ruble depreciation during the Russian invasion of Ukraine.
method Uses intraday high-frequency data (google searches and implied volatility) to model exchange rate fluctuations.
result Implied volatility is more effective than attention in predicting ruble depreciation.
We present a sparse and invariant representation with low asymptotic complexity for robust unsupervised transient and onset zone detection in noisy environments. This unsupervised approach is based on wavelet transforms and leverages the scattering network from Mallat et al. by deriving frequency invariance. This frequ…
A system is presented that segments, clusters and predicts musical audio in an unsupervised manner, adjusting the number of (timbre) clusters instantaneously to the audio input. A sequence learning algorithm adapts its structure to a dynamically changing clustering tree. The flow of the system is as follows: 1) segment…
Method detects critical events in complex systems by learning latent causal structure.
problem Detecting onset of epileptic seizures, customer churn, or pandemics from hidden causal interactions.
method A machine learning method that learns an optimal feature representation from powers of the empirical covariance or precision matrix.
result Proves structural consistency and demonstrates competitive results in seizure and churn prediction.
New approach predicts event probabilities for better event detection.
problem Class imbalance and inaccurate event detection in time series analysis.
method Regression-based approach to predict probability densities at event locations.
result Regression-based approaches outperform segmentation-based methods.
Machine learning predicts synchronization transitions in unknown systems.
problem Predicting synchronization transitions in systems with unknown equations.
method Developed a 'parameter-aware' machine learning scheme using reservoir computing or echo state networks.
result Machine learning accurately predicts synchronization transitions, including hysteresis loops.
New method synthesizes piano training data, improving transcription performance.
problem Lack of large piano datasets limits note onset transcription models.
method Synthesizes arbitrary training data, models piano dynamics, avoids disentanglement problem.
result Achieves good transcription performance on MAPS dataset and excellent generalization.
The paper studies phase transitions in Information Bottleneck for representation learning.
problem Understanding the behavior of compression and prediction terms in IB objective.
method Studied phase transitions in IB objective using second-order calculus of variations and Fisher information matrix.
result IB phase transitions correspond to learning new classes and are related to maximum correlation between input and target orthogonal to the learned representation.
Study uses ML to predict cancer patient mortality from FN onset.
problem Predicting mortality in cancer patients with FN to improve survival.
method Multi-domain machine learning models using HCUP data.
result Clinical diagnoses have highest predictive power for FN mortality.
BoXHED boosts hazard estimation for dynamic health risk scores.
problem Analyzing time-varying health vitals for disease onset prediction.
method Gradient boosting for nonparametric hazard function estimation with time-dependent covariates.
result Novel interaction effects among risk factors identified in cardiovascular disease onset data.
Automatic Music Transcription (AMT) is one of the oldest and most well-studied problems in the field of music information retrieval. Within this challenging research field, onset detection and instrument recognition take important places in transcription systems, as they respectively help to determine exact onset times…
Study predicts when ALS patients will lose speech, swallowing, etc. based on covariates.
problem Predicting when ALS patients will experience significant functional decline.
method Multi-event survival analysis, covariate-based models.
result Covariate-based models outperform Kaplan-Meier estimator in predicting time-to-event outcomes.
Investor attention predicts global equity market volatility during Ukraine invasion.
problem Predicting global equity market volatility during geopolitical events.
method Event-specific attention indices based on Google Trends, analyzed across 51 global equity markets.
result Investor attention significantly predicts volatility in countries with higher economic openness to Russia and closer to it.
Method identifies financial rogue waves close to their onset.
problem Identifying extreme financial events close to their onset.
method Analogy between rogue waves in optics and financial volatility, using Schrödinger equation with potential shaped by Kerr nonlinearity.
result Numerical gradient spikes at the onset of extreme financial events.
GMMSEQ clusters AE data streams, identifying cluster onsets and growth.
problem Clustering unlabeled AE data with onsets and growth information.
method Modified Gaussian Mixture Models (GMM) with an objective function for AE timestamps.
result GMMSEQ provides better cluster characterization than standard methods.
Liver transplant patients have a 25% chance of developing diabetes within 5 years.
problem Predicting new onset diabetes after liver transplant to improve patient care.
method Comparison of time-to-event prediction models and classifiers, including regularized Cox proportional-hazards model.
result Regularized Cox proportional-hazards model achieved high Concordance Index of 0.863.
Deep learning detects sepsis early, improving patient outcomes.
problem Improving timeliness of sepsis detection in various healthcare settings.
method Deep learning model on electronic health records, combining CNN and LSTM networks.
result Model performance ranges from 0.856 to 0.756 AUROC, detecting sepsis up to 24 hours in advance.
Deep neural network improves EEG-based epilepsy diagnosis.
problem Automated identification of seizure onset zones in epilepsy patients.
method Residual deep convolutional neural network trained on raw EEG data.
result State-of-the-art performance on epilepsy classification benchmarks.
Paper compares neural networks and classical statistics for dementia prediction, highlighting interpretability of classical methods.
problem Tackles the challenge of interpreting risk factors for dementia prediction.
method Compares neural networks and classical statistics for dementia prediction.
result Classical statistics provide clearer interpretation of risk factors compared to neural networks.
A theory for the evolution of a metric g driven by the equations of three-dimensional continuum mechanics is developed. This metric in turn allows for the local existence of an evolving three-dimensional Riemannian manifold immersed in the six-dimensional Euclidean space. The Nash-Kuiper theorem is then applied to th…
New method detects careless responding in long surveys.
problem Careless responding in long surveys threatens internal validity.
method Detects a changepoint in combined measurements of carelessness.
result Highly accurate in identifying carelessness onset.
New method detects heuristics in complex game strategies.
problem Understanding decision-making in games with infinite strategy spaces.
method Introducing decoupled strategies to detect convergence towards Nash equilibria.
result Predictive measure ΔD reveals participants' actions with high success rate.
Deep learning predicts vascular disease from medical history.
problem Predicting high-risk vascular diseases from medical records.
method Medical History-based Prediction using Attention Network (MeHPAN) models.
result MeHPAN models outperform standard classification models.
Neural network predicts cardiovascular events from EHRs with high accuracy.
problem Predicting onset of cardiovascular diseases from electronic health records.
method Multi-task gated recurrent units with attention mechanism.
result Model outperforms clinical risk scores in predicting stroke and myocardial infarction.
Study investigates how machine learning models degrade over time, leading to patient safety issues.
problem Overtime degradation of machine learning models in clinical settings.
method Used MIMIC-IV dataset to train models replicating commercial approaches, observing and analyzing degradation over a decade.
result An RNN model built on Epic features degrades from 0.729 AUC to 0.525 AUC over a decade, highlighting technical and clinical drift as root causes.
Detecting depression early from social media texts.
problem Early diagnosis and prevention of depression.
method Topic analysis and learned confidence scores.
result Achieved good results compared to state of the art.
Paper proposes a graph neural network for accurate long-term ILI prediction.
problem Limited long-term prediction performance and spatio-temporal dependency in existing models.
method Cross-location attention based graph neural network (Cola-GNN) for time series embeddings and location aware attentions.
result Proposed method shows strong predictive performance and interpretable results for long-term epidemic predictions.
More than two thirds of mental health problems have their onset during childhood or adolescence. Identifying children at risk for mental illness later in life and predicting the type of illness is not easy. We set out to develop a platform to define subtypes of childhood social-emotional development using longitudinal,…
Method screens weakly associated predictors in high-dimensional data.
problem Identifying weakly associated predictors in ultrahigh-dimensional data.
method Covariance-insured screening methodology.
result Validates the method through simulations and real data studies.
Model predicts epileptic seizures by detecting preictal state using wavelet transform and PCA.
problem Predicting epileptic seizures before onset.
method Common spatial pattern filtering, wavelet transform for preprocessing, PCA for feature extraction, SVM for classification.
result Average sensitivity of 93.1% for 84 seizures in 23 subjects.
Generative adversarial networks enhance risk prediction in EHRs.
problem Limited labeled data in EHRs for deep learning risk prediction.
method ehrGAN, a modified GAN, generates realistic EHR data for semi-supervised learning.
result Significant improvements in classification tasks with generated data over baselines.
PliableBVS extends Bayesian lasso for modeling interactions with modifying variables.
problem Modeling interactions between large and small sets of variables, especially in omics studies.
method Bayesian variable selection with spike-and-slab priors and hierarchical structure.
result PliableBVS outperforms pliable lasso in identifying active main and interaction effects.
Bayesian nonparametric model predicts user activity and intervention success.
problem Predicting user activity and intervention success in online experiments.
method Bayesian nonparametric approach to model user heterogeneity and derive user activity predictions.
result The proposed method outperforms existing approaches in predicting user activity and intervention success.
A deep learning algorithm for ECG segmentation.
problem ECG signal segmentation for various sampling rates and monitors.
method UNet-like neural network for adaptive and generalized ECG segmentation.
result F1-measures for ECG segmentation are at least 97.8%, 99.5%, and 99.9%.
Deep neural networks predict CVCM track circuit failures early.
problem Subtle anomalies in CVCM track circuits lead to failures, causing disruptions.
method Deep neural networks classify anomalies before they escalate.
result Deep neural networks achieve 99.31% overall accuracy in detecting CVCM failures.
New law predicts first extinction in resampling processes.
problem Intractable extinction times in resampling processes.
method Modeling multinomial updates as independent square-root diffusions.
result Closed-form law for first-extinction time with linear cost.
Sparse point observations can provide useful local constraints, but their benefit for radar-like fields depends on the training loss, uncertainty representation, and how observation support is encoded in the model.
problem Improving dense radar-field forecasts with sparse point observations
method A multimodal graph neural network nowcasting system over the Nordic radar domain
result Each source improves a different part of the forecast problem