A simple formula captures the essence of optimal lifestyling.
problem Optimal investment in life-cycle economics with credit constraints.
method Provides a simple explicit formula for optimal lifestyling.
result Simple formula accurately captures the main essence of lifestyling effect.
Study of urban lifestyles from mobility data of 1.2M people in 11 U.S. cities.
problem Lack of interpretability in digital mobility data for understanding urban lifestyles.
method Privacy-enhanced dataset of mobility visitation patterns, latent activity behavior decomposition.
result Detected 12 latent activity behaviors that describe urban lifestyles, not single lifestyles.
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.
Paper uses Monte Carlo simulations to predict retirement portfolios.
problem Retirement financial planning uncertainty.
method Monte Carlo simulations incorporating inflation, interest rates, etc.
result Probabilistic prediction of IRA and 401(k) values.
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.
System identifies health risks using semantic and machine learning.
problem Identifying risk factors associated with health conditions in subpopulations.
method Developed a combined semantic and machine learning system using a health risk ontology and knowledge graph.
result Dynamic discovery of risk factors and their subpopulations.
Study uses social media analytics to identify exercise-related topics.
problem Understanding exercise-related discussions on social media.
method Data collection, topic modeling, and data annotation.
result 86% of detected topics were meaningful after annotation.
A recipe recommendation system suggests missing ingredients using collaborative filtering.
problem Encouraging healthy diets through personalized ingredient suggestions.
method Item-based collaborative filtering applied to a sparse dataset of recipes.
result Best method achieves a recall@10 of circa 40%.
System recommends workouts and predicts success rates using RNNs.
problem Promoting healthy lifestyles through personalized exercise recommendations.
method Two interconnected recurrent neural networks (RNNs) using historical workout data.
result Interconnected-RNN model predicts exercise success rates with improved accuracy.
Proposes a multi-stream RNN model for predicting merchant transactions.
problem Predicting future transaction statistics of merchants.
method Multi-stream RNN model tailored for multivariate time series and multi-step predictions.
result Outperforms existing state-of-the-art methods in merchant transaction predictions.
Deep learning model detects and classifies marine microfossils.
problem Manual identification of microfossils is time-consuming and error-prone.
method Transfer learning from ImageNet dataset to classify foraminifera.
result Proposed model achieves high accuracy on foraminifera classification.
We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real wor…
New algorithm improves causal discovery in biomedical data.
problem Stability and accuracy issues in causal discovery algorithms.
method Exploits temporal structure and tiered background knowledge.
result Increases accuracy in finite samples for causal structure estimation.
Machine learning predicts obesity causes using genetic and imaging data.
problem Predicting causes of obesity in children and adults.
method Use ML techniques like decision trees, SVM, RF, GBM, LASSO, BN, and ANN on genetic and imaging data.
result ML models accurately predict obesity causes and chronic diseases.
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.
Deep learning models accurately recognize and estimate physical activity types and energy expenditure from wrist accelerometer data.
problem Rigorous evaluation of wrist-worn accelerometers for assessing physical activity across the lifespan.
method Built deep learning networks to extract spatial and temporal representations from time-series data, recognizing physical activity types and estimating energy expenditure.
result Deep learning models achieved high performance: F1 scores of 0.82, 0.81, and 95 for sedentary, locomotor, and lifestyle activities, respectively; root mean square error of 1.1 for EE estimation.
Simple models are preferred over complex models, but over-simplistic models could lead to erroneous interpretations. The classical approach is to start with a simple model, whose shortcomings are assessed in residual-based model diagnostics. Eventually, one increases the complexity of this initial overly simple model a…
Method tackles missing covariates in large-scale datasets.
problem Cross-population missing data problem in large-scale datasets.
method Augmented transfer regression learning method combining importance-weighted estimating equations and imputation terms.
result Estimator is n1/2-consistent and asymptotically normal, attaining semiparametric efficiency bound under correct specification. Machine learning integrates diverse biological data to understand complex phenomena.
problem Combining multiple data types to understand biological and medical phenomena.
method Developing effective models to integrate heterogeneous biological data.
result Machine learning can identify important features and predict outcomes from diverse biological data.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
problem Understanding how weather affects bike sharing usage patterns.
method Gathered bike usage and weather data, used k-means clustering algorithm to identify clusters.
result Weather significantly impacts bike usage, with temperature and precipitation being the most influential factors.
Over the last years, huge resources of biological and medical data have become available for research. This data offers great chances for machine learning applications in health care, e.g. for precision medicine, but is also challenging to analyze. Typical challenges include a large number of possibly correlated featur…
Activity2vec learns representations from wearable activity data.
problem Proactive screening and monitoring of chronic conditions.
method Adversarial unsupervised representation learning with three components.
result Activity2vec outperforms many baselines in disorder prediction tasks.
The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits, and clinical history. This data is then used by statistical models to predict personalized treatments. However, due to pri…
New benchmark predicts cardiometabolic risk from accelerometer data, with varying accuracy.
problem Lack of accurate tabular benchmarks for cardiometabolic risk from accelerometer data.
method Tabular learning methods (ridge regression, XGBoost, TabPFN v2) applied to NHANES data.
result TabPFN v2 achieves best performance, but triglycerides remain largely unpredictable.
This study maps cycling risks and discomfort in Zurich, offering personalized route recommendations.
problem High cycling accidents and discomfort in Smart Cities.
method Geolocated bike accidents data, kernel density contours, weather, time, accident type and severity analysis.
result Empirical continuous spatial risk estimations and personalized route recommendations.
Develops a real-time exercise recommendation system using deep learning.
problem Improving accuracy in exercise recommendation systems without user feedback.
method Deep recurrent neural network with attention mechanisms, real-time expert feedback.
result Improved accuracy in exercise recommendation system after real-time active learning.
LSTM models predict low likelihood of another COVID-19 wave in India.
problem Inaccurate and unreliable COVID-19 infection forecasting models due to data limitations and model complexity.
method Application of LSTM, bidirectional LSTM, and encoder-decoder LSTM models for multi-step infection forecasting.
result Predictions indicate low likelihood of another wave in October and November 2021.
The paper compares two methods for handling missing data in causal discovery.
problem Handling missing data in causal discovery algorithms.
method Test-wise deletion and multiple imputation.
result Multiple imputation is more challenging for causal discovery than for estimation.
Method predicts NAFLD risk with high accuracy and distribution-free coverage guarantees.
problem Insufficient population-level screening tools for NAFLD.
method Gradient-boosted decision trees with conformal prediction.
result Method achieves AUROC of 0.912 internally and 0.891 externally, superior to other models.
Bayesian method decomposes ITR value into direct and indirect effects.
problem Assessing how clinical benefit of an ITR is generated.
method Causal mediation framework using nested potential outcomes and Bayesian causal mediation forests.
result Identification and estimation of natural direct and indirect effects.
AI enhances personalized drug development and decision-making in pharma.
problem Traditional drug development lacks personalized treatment plans.
method Application of AI in drug discovery, clinical trials, and post-marketing assessment.
result AI improves personalized medicine, optimizing health outcomes.
Research quantifies financial exclusion risks in UK, focusing on cash infrastructure and socio-economic factors.
problem Localised financial exclusion in the UK as cash infrastructure declines.
method Developed a composite indicator using various input variables.
result Financial exclusion is more prevalent in deprived communities and affluent areas.
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.
Digital risk scores predict depression and anxiety over 10 years.
problem Identifying individuals at risk of depression and anxiety.
method Developed a 10-year predictive algorithm using UKB cohort, selecting predictors via Cox proportional hazards model and DeepSurv.
result Highly discriminating models for depression and anxiety were developed.
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…