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.
Develops a RNN model to predict obesity status improvement using irregular activity data.
problem Predicting obesity status improvement using irregular activity data.
method Develops a RNN-based time-aware architecture to handle irregular observation times and extract relevant features from longitudinal patient records.
result Achieves an accuracy of 77-86% in predicting obesity status improvement.
Review of childhood data for predicting overweight and obesity in later life.
problem Predicting overweight and obesity in later life using childhood data.
method Bibliographic searches and iterative searching of references.
result High performance prediction models often use short time periods or late childhood data.
Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO), however, collecting and analyzing a larg…
New method improves human mesh recovery for obese people.
problem Improving mesh recovery for obese people.
method Generative optimization of mesh parameters from 2D keypoints.
result Significant improvement in mesh recovery performance on obese person images.
Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-…
Expands statistical background for knee osteoarthritis treatment models.
problem Developing optimal exercise and weight loss treatments for knee osteoarthritis.
method Precision medicine models and jackknife cross-validation method.
result Jackknife estimator provides consistent value function estimation.
Framework uses human annotations to make models robust to spurious correlations.
problem Machine learning models fail when unmeasured variables change test distributions.
method Human annotations to augment training examples, UV-DRO objective for robustness.
result Improvements of 5-10% on digit recognition task and 1.5-5% on NYPD Police Stops analysis.
Paper addresses measurement error in observational data, estimating effects of maternal smoking and opioid use on childhood obesity.
problem Systematic bias in inferences from observational datasets due to measurement error.
method Missing data view of measurement error problem; marginalizes latent true exposure; identifies outcome distribution under specific conditions.
result Method estimates effects of maternal smoking and opioid use on childhood obesity using only subject-reported data, refining estimates and consistency with existing literature.
Smart phone apps that enable users to easily track their diets have become widespread in the last decade. This has created an opportunity to discover new insights into obesity and weight loss by analyzing the eating habits of the users of such apps. In this paper, we present diet2vec: an approach to modeling latent str…
Proposes MCLLO for assessing and recalibrating multiclass probability predictions.
problem Limited multicategory recalibration methods for assessing and comparing model calibration.
method MCLLO recalibration method that assesses calibration without model access and is easy to interpret.
result MCLLO outperforms other methods in simulations and real-world case studies.
Study uses CNNs to estimate BMI from photos.
problem Estimating BMI from photos with limited data.
method Used deep learning (CNNs) to process silhouette images.
result High correlation between estimated and actual BMI.
Machine learning predicts biologic therapy outcomes in psoriasis patients.
problem Predicting long-term biologic therapy outcomes in psoriasis patients.
method Machine learning algorithms were used to predict drug discontinuation and treatment duration.
result Machine learning models accurately predict outcomes with high diagnostic accuracy and low MAE.
Satellite images predict U.S. county mortality rates.
problem Predicting mortality rates in U.S. counties using satellite imagery.
method Convolutional neural network trained on crude mortality rates, learned features interpreted using Shapley Additive Feature Explanations.
result Predicted mortality from satellite images correlated strongly with true mortality rates (Pearson r=0.72).
Improved logistic regression for multi-omics data improves prediction and variable selection.
problem Predicting binary class labels from multi-omics datasets with varying characteristics.
method Two-step penalized logistic regression with separate variable selection for each data layer.
result Our approach selects more relevant predictors and achieves comparable prediction performance.
Paper analyzes factors affecting COVID-19 risk in US counties.
problem Identifying factors influencing COVID-19 risk in US counties.
method Combines unsupervised (K-means clustering) and supervised learning models.
result Mean temperature, poverty, obesity, and other factors are most significant.
Efficiently solves Elastic Net in high dimensions with Newton method.
problem Feature selection in high-dimensional data with non-negligible collinearity.
method Semi-smooth Newton Augmented Lagrangian Method.
result Significantly reduces computational cost compared to competitors.
New method stabilizes IF-based estimators for causal mediation analysis with continuous mediators.
problem Stability issues in IF-based estimators for continuous mediators.
method Nonparametric weighted balancing method to estimate nuisance functions.
result Significant reductions in bias and variance compared to existing methods.
SAERMA combines deep learning and rule mining to identify SNP interactions.
problem Identifying significant SNPs and their interactions in complex diseases.
method Stacked Autoencoder Rule Mining Algorithm (SAERMA) integrating deep learning and association rule mining.
result Achieved high classification accuracy (77% AUC, 77% SE, 68% SP) with 100 units.
GraphKKE learns fixed-length feature vectors from time-evolving graphs of human microbiome data.
problem Understanding dynamic changes in human microbiome graphs over time.
method Spectral analysis of transfer operators and graph kernels.
result GraphKKE captures temporal changes in human microbiome graphs.
Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to moni…
Study compares estimators for causal mediation analysis with multiple mediators.
problem Estimating causal effects through multiple mediators in observational studies.
method Parametric and non-parametric estimators, including multiply robust and double machine learning approaches.
result Advanced estimators perform well across various settings and real 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.
Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.
problem Binary prediction sets are limited; fuzzy prediction sets offer richer guarantees.
method Generalize prediction sets to fuzzy sets, showing they are e-values with merging properties.
result Optimal e-values lead to optimal fuzzy prediction sets, including optimal conformal prediction.
Paper defines predictive multiplicity and measures its severity in classification problems.
problem Challenges in machine learning due to competing models with conflicting predictions.
method Formal measures and integer programming tools for linear classification problems.
result Real-world datasets may admit competing models with wildly conflicting predictions.
A novel continual prediction model outperforms traditional one-time models in predicting AKI.
problem Optimally predicting AKI before it develops during a hospital stay.
method A novel continual prediction model that predicts AKI every time a patient's AKI-relevant variable changes in the EHR.
result The continual prediction model outperformed traditional one-time models, achieving a higher AUC of 0.724 compared to 0.653.
This paper re-examines conformal e-prediction and its advantages over conformal prediction.
problem The relationship between conformal prediction and conformal e-prediction.
method Systematic re-examination of conformal prediction and conformal e-prediction from a modern perspective.
result Conformal e-prediction has advantages such as ease of designing conditional predictors and guaranteed validity of cross-predictors.
Two new methods improve efficiency of conformal predictive systems.
problem Efficiency of conformal predictive systems in regression problems.
method Split conformal predictive systems and cross-conformal predictive systems.
result Cross-conformal predictive systems are more efficient but not guaranteed valid.
Self-calibrating conformal prediction improves interval efficiency and offers a practical alternative.
problem Improving the reliability and uncertainty quantification of machine learning predictions.
method Combines Venn-Abers calibration and conformal prediction for binary and regression problems.
result Improves interval efficiency through model calibration and offers practical alternatives.
Study uses deep learning to predict asset prices, finds complex target processes lead to meaningless predictions.
problem Complexity of successful price prediction models hinders understanding.
method Deep learning models for high-frequency price prediction, focusing on volatility and directional prediction.
result Inadequately defined target price process renders predictions meaningless.
The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.
problem The need for accurate joint predictions in decision-making problems.
method The paper analyzes combinatorial decision problems, sequential predictions, and multi-armed bandits, introducing an approximate Thompson sampling algorithm and new regret bounds.
result Accurate joint predictions are essential for good performance in decision-making problems.
Behavior modification improves prediction accuracy by nudging user behavior.
problem Improving prediction accuracy using behavior modification techniques.
method Combining prediction and behavior modification with reinforcement learning algorithms.
result Behavior modification can make predictions more certain but may not generalize.
New adaptive conformal predictive systems developed.
problem Severe restrictions on adapting predictive distributions to test objects.
method Calibrating existing predictive systems to ensure full adaptability and validity.
result Developed fully adaptive split-conformal and cross-conformal predictive systems.
Predictions can shape outcomes, study helps predict these effects.
problem Understanding how predictions influence real-world outcomes.
method Causal identifiability analysis of prediction-covariate-outcome relationships.
result Standard supervised learning can identify transferable relationships from predictions.
Proposes feature conformal prediction for broader application in semantic feature spaces.
problem Establishing valid prediction intervals in semantic feature spaces.
method Extends conformal prediction to semantic feature spaces using deep representation learning.
result Feature conformal prediction outperforms regular conformal prediction under mild assumptions.
AutoCP automates the construction of accurate prediction intervals.
problem Creating valid and accurate prediction intervals for machine learning models.
method AutoML framework that optimizes prediction interval length for better accuracy and less conservatism.
result AutoCP significantly outperforms benchmark algorithms in constructing accurate prediction intervals.
Proposes a method to apply conformal prediction to probabilistic time series forecasting models.
problem Obtaining accurate prediction regions for multi-step time series forecasting with probabilistic models.
method Conformalises conditional normalising flows to generate potentially disjoint prediction regions.
result Improves predictive efficiency in time series forecasting with multimodal distributions.
ICP improves prediction intervals for continuous outcomes at lower computational cost.
problem Systematic bias in point predictions that undermines their use in decision-making.
method Develops Isotonic Conformal Prediction (ICP) framework to decouple calibration from prediction-set construction.
result SICP and TICP procedures match SC-CP coverage at lower computational cost.
Optimizes predictions for specific tasks using parametrized decision analysis.
problem Optimizing predictions for specific decision tasks of interest.
method Designs a class of parametrized actions for Bayesian decision analysis.
result Derives efficient and interpretable solutions for various action parametrizations and loss functions.
New measures for prediction validity and consonant plausibility introduced.
problem Challenges in predicting future observations and quantifying prediction uncertainty.
method Introducing Type-2 validity and using consonant plausibility measures and conformal prediction.
result Achieving both Type-1 and Type-2 validity through consonant plausibility measures and conformal prediction.
RFpredInterval package builds prediction intervals for random forests and boosted forests.
problem Quantifying uncertainty in random forest and boosted forest point predictions.
method 16 methods to build prediction intervals with random forests and boosted forests.
result The proposed method outperforms existing methods in building prediction intervals.
FPPI selectively uses predictions to improve inference efficiency.
problem Improving statistical inference with limited labeled data and heterogeneous prediction quality.
method Filtered Prediction-Powered Inference (FPPI) framework.
result FPPI achieves strictly improved asymptotic efficiency compared to existing methods.
COP improves online conformal prediction by incorporating data patterns, leading to tighter prediction sets.
problem Overly conservative prediction sets in online conformal prediction methods when data distribution shifts.
method Conformal Optimistic Prediction (COP) incorporating estimated cumulative distribution function of non-conformity scores.
result COP produces tighter prediction sets with valid coverage guarantees, outperforming other methods.
Proposes a model to update industrial data predictions based on temporal changes.
problem Improving prediction accuracy in industrial data analytics by addressing changing conditions over time.
method Integrates similarity and loss functions to estimate and update prediction models adaptively.
result The data renewal model enhances prediction accuracy by identifying and updating model changes.
Unified framework for estimating random forest prediction errors.
problem Estimating prediction errors for random forests.
method Novel estimator of conditional prediction error distribution function.
result Proposed estimators enable competitive prediction intervals.
Predicts food ingredient amounts from images.
problem Predicting relative amounts of ingredients from food images.
method Proposes two deep learning models for sparse and dense predictions, with semi-automatic data pre-processing.
result Encouraging experimental results on a recipe dataset.
This paper studies trade-offs in private prediction methods.
problem Leakage of training data information in machine learning predictions.
method Private training and private prediction methods with trade-offs.
result Private training methods outperform private prediction methods in various settings.
Conformal prediction helps quantify uncertainty but its use by humans is unclear.
problem Uncertainty quantification in predictions for human decision making.
method Decision theoretic framework for evaluating predictive uncertainty.
result Conformal prediction sets and human decision making goals are in tension.