The study uses demographical data to predict health conditions.
problem Predicting health conditions based on patient demographics and symptoms.
method Analyzing Electronic Health Records (EHR) from Brazil to identify age-related clusters of health conditions.
result Age of patients significantly influences the likelihood of certain health conditions.
The study of record statistics of correlated series is gaining momentum. In this work, we study the records statistics of the time series of select stock market data and the geometric random walk, primarily through simulations. We show that the distribution of the age of records is a power law with the exponent α lyi…
Researchers analyze record statistics in correlated random walks and Lévy flights.
problem Understanding record statistics in correlated time series.
method Review of random walk models and Lévy flights, focusing on number of records and record ages.
result Effects of correlations on record statistics were observed and analyzed.
In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related can…
Study reveals gender and age biases in drug interactions, impacting public health costs.
problem Gender and age biases in drug interactions leading to adverse reactions and increased costs.
method City-wide longitudinal analysis of electronic health records (EHR) from Blumenau, Brazil.
result Women have a 60% increased risk of drug interactions compared to men, and this increases to 90% for major adverse drug reactions.
We investigate the statistics of records in a random sequence {xB(0)=0,xB(1),⋯,xB(n)=xB(0)=0} of n time steps. The sequence xB(k)'s represents the position at step k of a random walk `bridge' of n steps that starts and ends at the origin. At each step, the increment of the position is a random ju…
Improved sex and age-specific model for Parkinson's diagnosis.
problem Accurate diagnosis of Parkinson's disease for better patient care.
method Sex-specific and age-dependent handwriting classification model.
result Significantly improved accuracy (83.75% for females, 79.55% for older age group) compared to generalized model.
BIGMACS aligns multiple ocean sediment cores using Bayesian inference and Gaussian process regression.
problem Aligning and synchronizing ages from different ocean sediment cores using multiple proxies.
method BIGMACS uses Bayesian inference and Gaussian process regression to align and integrate age proxies.
result Constructs a new Deep Northeastern Atlantic stack and age models for additional cores.
Predicts the age of astronomical transients from real-time data.
problem Improving understanding of transients and their progenitor systems.
method Bayesian probabilistic recurrent neural network.
result Accurately predicts the age of transients with robust uncertainties.
Predicts gender and age from mobile phone data for marketing.
problem Enhance marketing offers by predicting customer demographics.
method Machine learning algorithms applied to CDRs, CRM, and billing info.
result 85.6% accuracy in gender prediction, 65.5% in age prediction.
Study identifies three sub-phenotypes of AKI with different severity.
problem Tackles the heterogeneity of AKI to improve targeted interventions.
method Used a memory network-based deep learning approach on EHR data.
result Identified three distinct sub-phenotypes of AKI with varying severity.
Town hall discusses AI's impact on statistics, culture, and training.
problem Adapting statistics to AI advancements and infrastructure.
method Open panel discussion and audience Q&A.
result Candid perspectives on evolving statistical practices.
New method extracts brain age from MRI sequences over time.
problem Lack of ground-truth labels in longitudinal neuroimaging data.
method Combines factor disentanglement with self-supervised learning.
result Extracts brain age information from MRI sequences.
We study the statistics of records of a one-dimensional random walk of n steps, starting from the origin, and in presence of a constant bias c. At each time-step the walker makes a random jump of length ηdrawn from a continuous distribution f(η) which is symmetric around a constant drift c. We focus in particular on th…
Web-based framework detects Parkinson's disease from speech recordings.
problem Early detection of Parkinson's disease from speech recordings.
method Collected data from 726 participants, extracted acoustic and deep learning features, trained machine learning models.
result Achieved 0.75 AUC performance in detecting Parkinson's disease.
New dataset for evaluating speech recognition fairness across demographics.
problem Lack of fairness metrics in speech recognition datasets.
method Developed Fair-Speech dataset with diverse demographic information.
result Helps evaluate ASR models for fairness across demographics.
Wearable smart suit tracks infant movements with high accuracy.
problem Early detection of atypical motor development in infants.
method Developed a multi-sensor smart suit for data collection, trained a deep CNN algorithm for automatic posture and movement classification.
result Setup achieves human equivalent accuracy in infant posture and movement classification.
Generates synthetic EHR chief complaints from structured data.
problem Generating unstructured text in EHRs.
method Encoder-decoder deep learning model trained on EHR data.
result Synthetic chief complaints preserve epidemiological information.
Paper proposes RL for efficient MCI diagnosis from dialogue data.
problem Efficiently diagnose MCI from conversational data with minimal interaction.
method Reinforcement learning framework trained on clinical trial transcripts.
result Significantly outperforms supervised learning approaches with minimal conversation turns.
Proposes a new Sliced-Wasserstein distance for covariance matrices in M/EEG signals.
problem Efficiently dealing with distributions of covariance matrices in M/EEG multivariate time series.
method Defines a Sliced-Wasserstein distance for symmetric positive definite matrices and applies it to brain-age prediction and Brain Computer Interface applications.
result Demonstrates computational efficiency and strong theoretical guarantees for the proposed distance.
Deep learning classifies OCT images of normal vs AMD with high accuracy.
problem Automated classification of OCT images for diagnosing Age-related Macular Degeneration.
method Automated extraction of OCT images linked to clinical data, training a deep neural network.
result Deep learning achieved high accuracy (92.64% sensitivity, 93.69% specificity) in classifying OCT images of normal vs AMD.
Study uses 1D-CNNs to forecast mortality in ELSA survey.
problem Forecasting mortality in the middle-aged and older population.
method 1D-CNNs applied to longitudinal data with various over/undersampling and activation functions.
result Swish nonlinearity outperforms other functions in forecasting mortality.
MLHO predicts COVID-19 adverse outcomes using past medical records.
problem Predicting adverse outcomes after COVID-19 infection.
method Iterative feature and algorithm selection, sequential representation mining.
result Mean AUC ROC of 0.91 for mortality prediction.
Machine learning detects epilepsy development from EEG before seizures.
problem Early detection of epilepsy development (epileptogenesis) before seizures.
method Deep CNN combined with prediction aggregation for EEG data analysis.
result Deep learning achieves 99% AUC for EPG detection from EEG recordings.
Study uses NMF to analyze multimorbidity patterns in large EHR dataset.
problem Understanding and quantifying multimorbidity patterns over time.
method Non-negative Matrix Factorisation (NMF) for temporal phenotyping.
result Temporal characteristics of disease clusters reveal new multimorbidity patterns.
Method converts age labels into distributions to improve speaker age estimation.
problem Label ambiguity in age labels makes precise speaker age estimation challenging.
method Converts age labels into label distributions and uses label distribution learning.
result Our method outperforms baseline methods by reducing MAE by 10% on a real-world dataset.
Bayesian approach predicts brain-age from EEG sleep states across age.
problem Inconsistent brain-age predictions due to subjective sleep state classification.
method Unified Bayesian Network with Gaussian Mixture Models for EEG sleep states.
result Improved accuracy in brain-age prediction over a wider age range.
Deep learning predicts opioid use disorder risk in patients.
problem Identifying patients at high risk of opioid use disorder.
method Applied LSTM models to analyze electronic health records of opioid users.
result LSTM model outperformed other methods with F1 score of 0.8023 and AUCROC of 0.9369.
Unified model of urban consumer behavior and mobility patterns.
problem Understanding the lifestyles and infrastructure of urban regions.
method Collective matrix factorization for dual view modeling of consumer behavior and mobility.
result Unified model reveals deeper insights into consumer behavior and mobility patterns.
The paper models retirement spending using biological age instead of chronological age.
problem Retirement spending varies at the same chronological age.
method Developed a stochastic mortality model to adjust for biological age.
result Optimal consumption rates derived using biological age.
Unified model for age-invariant face recognition with photorealistic face synthesis.
problem Reliable face recognition across ages remains challenging due to significant intra-class variations.
method Unified deep architecture for cross-age face synthesis and recognition, continuous face rejuvenation/aging, disentangled age-invariant face representations.
result Superior performance on CAFR and other cross-age datasets, promising generalizability to unconstrained face recognition.
SFCNeXt estimates brain age from small MRI datasets.
problem Efficiently estimating brain age from limited MRI data.
method Simple fully convolutional network (SFCNeXt) with SPEC and HRL.
result SFCNeXt outperforms complex models in small sample size scenarios.
Deep learning predicts brain age from raw MRI data with high accuracy and reliability.
problem Predicting brain age from neuroimaging data to assess individual differences in brain aging.
method Convolutional Neural Networks (CNN) applied to raw T1-weighted MRI data.
result Brain-predicted age is a reliable and heritable biomarker of brain aging.
ConvNets improve EEG pathology detection accuracy.
problem Improving automated EEG diagnosis accuracy.
method Two ConvNet architectures, shallow and deep, trained on EEG data.
result ConvNets achieved ~6% better accuracy than previous methods.
The paper uses AI to analyze ECG data, revealing age-related changes and identifying key features.
problem Investigating age-related changes in ECG data to distinguish healthy from disease-related changes.
method Employed deep-learning and tree-based models on raw ECG signals and features from a diverse age group.
result Identified age-related declines in breathing rates and high SDANN values in elderly individuals.
Bayesian CNN estimates uncertainty in bone age prediction.
problem Uncertainty quantification in age estimation models.
method Variational Inference for Bayesian CNNs.
result Model uncertainty distinguished from data uncertainty.
Study uses deep learning with attention to predict fetal brain gestational age.
problem Accurately predicting fetal brain gestational age for early diagnosis.
method Attention-based deep learning model combining multi-view MRI data.
result Age prediction performance with R2 = 0.94 using multi-view MRI and attention.
GOPSA optimizes EEG data for cross-site age prediction, improving performance on multiple metrics.
problem Predictive shifts in EEG data from different sites and participants.
method Geodesic Optimization for Predictive Shift Adaptation (GOPSA) on the SPD manifold.
result Significantly higher performance on age prediction metrics compared to state-of-the-art methods.
Paper tackles deconfounding age effects in dementia detection models.
problem Dementia detection models are affected by age, leading to potential non-generalizable accuracies.
method Proposes fair representation learning to learn age-invariant representations.
result Best models compromise accuracy by only 2.56% and 1.54% on clinical datasets.
This work generates synthetic EHRs with privacy guarantees for machine learning tasks.
problem Privacy concerns and heterogeneity in EHR data limit their use in machine learning.
method Generative Adversarial Networks (GANs) with differential privacy (DP) for synthetic data generation.
result Synthetic EHRs maintain performance close to real data, even with DP applied.
3D CNN accurately classifies infant neurodevelopmental age from MRI scans.
problem Estimating neurodevelopmental age in infants from MRI data.
method 3D Convolutional Neural Network (3D CNN) trained on MRI images of 112 infants.
result 3D CNN achieves 99% sensitivity and 98.3% specificity in age classification.
Develops a method to analyze SARS-CoV-2 viral load vs. age, finding a significant increase.
problem Determining if SARS-CoV-2 viral load depends on patient age.
method Flexible, non-parametric, hierarchical, Bayesian, causal model.
result Statistically significant increase in viral load with age, but only for one dataset.
Optimizes consumption, investment, and healthcare spending for aging populations.
problem Maximizing utility in an aging population with changing mortality rates.
method Solves an optimal stochastic control problem using a novel version of Perron's method.
result Endogenous mortality law that is asymptotically exponential in the old-age limit.
Probabilistic programming aids in automatically dating ice cores, reducing manual error and uncertainty.
problem Automatically dating ice cores with high accuracy and capturing uncertainty.
method Probabilistic models and probabilistic programming for automatic inference.
result Demonstrated the use of probabilistic programming for ice core dating, showcasing its benefits and limitations.
Twitter provides an open and rich source of data for studying human behaviour at scale and is widely used in social and network sciences. However, a major criticism of Twitter data is that demographic information is largely absent. Enhancing Twitter data with user ages would advance our ability to study social network …
Model learns aging rates from single cross-sectional data.
problem Cross-sectional data limits traditional time-series methods.
method Latent-variable model with order-isomorphic nonlinear function.
result Reconstructs aging rates from single cross-sectional data.
Novel U-learning method for predicting continuous outcomes from high-dimensional data.
problem Challenges in making valid inferences on predictions from high-dimensional inputs.
method U-learning via combinatory multi-subsampling for ensemble predictions and confidence intervals.
result Valid inferences on predictions from Lasso and neural networks.
Estimates future ICU demand using age-structured data and logistic modeling.
problem Projecting future ICU demand from low-quality data.
method Age-structured sampling, logistic fit, subnotification factor correction.
result Corrected logistic curve projects future ICU demand.