Deep learning models predict postoperative complications more accurately than random forests.
problem Predicting postoperative complications to inform patient care decisions.
method Multi-task deep neural networks integrating intraoperative physiological data.
result Deep learning models improved prediction accuracy and provided interpretable risk factors.
The study predicts surgical complications in Crohn's disease patients using machine learning.
problem Predicting surgical complications in Crohn's disease patients.
method Developed a novel algorithm using ensemble machine learning on 29 baseline covariates.
result Proposed pseudo-observation based estimators for evaluating predictive performance.
Study describes severe dengue ICU patients in Brazil, 2012-2024.
problem Characterize severe dengue ICU patients and identify risk factors.
method Prospective study, descriptive statistics, logistic regression, machine learning.
result Advanced age, comorbidities, leukocytes, and platelets are significant risk factors for complications.
Type 2 diabetes mellitus (T2DM) is a chronic disease that often results in multiple complications. Risk prediction and profiling of T2DM complications is critical for healthcare professionals to design personalized treatment plans for patients in diabetes care for improved outcomes. In this paper, we study the risk of …
This study predicts diabetes complications using financial records and neural networks.
problem Managing chronic diseases like diabetes in patients.
method Used financial records from health plans, applied self-attentive recurrent neural networks.
result Successfully predicted diabetes complications with an AUC of 0.81-0.94, 60-240 days ahead.
AI identifies patient clusters for diabetes case management.
problem Diabetes complications and mental health comorbidities drive high healthcare costs.
method Combined AI techniques with diverse data sources for prediction and clustering.
result 83.5% accuracy in predicting diabetes complications and meaningful patient clusters.
Mathematical method based on a direct or indirect analysis of growth rates is described. It is shown how simple assumptions and a relatively easy analysis can be used to describe mathematically complicated trends and to predict growth. Only rudimentary knowledge of calculus is required. Projected trajectories based on …
This paper provides a ML framework for diabetes prediction and care management.
problem Diabetes prediction and care management challenges in real-world healthcare.
method Illustrates a Machine Learning framework for T2DM prediction and risk stratification.
result ML models align with physician's disease management steps.
In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs …
Composite neural network improves PM2.5 prediction.
problem Improving PM2.5 prediction accuracy.
method Composite neural network framework with pre-trained models.
result Composite neural network outperforms individual models and new components added.
We propose that predictability is a prerequisite for profitability on financial markets. We look at ways to measure predictability of price changes using information theoretic approach and employ them on all historical data available for NYSE 100 stocks. This allows us to determine whether frequency of sampling price c…
MuLFA predicts drug interactions more accurately than existing methods.
problem Improving drug safety by predicting drug interactions.
method Proposes MuLFA, a factorization autoencoder that models nonlinear interactions between drug pairs.
result MuLFA outperforms state-of-the-art methods in predicting drug interactions.
PHASE predicts surgical complications from physiological signals.
problem Predicting adverse surgical outcomes from physiological signals.
method Self-supervised transfer learning for physiological signals.
result PHASE outperforms other approaches in predicting five surgical complications.
Researchers have constantly asked whether stock returns can be predicted by some macroeconomic data. However, it is known that macroeconomic data may exhibit nonstationarity and/or heavy tails, which complicates existing testing procedures for predictability. In this paper we propose novel empirical likelihood methods …
ST-SAN predicts flow with spatial-temporal dependencies using self-attention.
problem Challenges in predicting flow due to spatial-temporal dependencies.
method Spatial-Temporal Self-Attention Network (ST-SAN) that addresses temporal and spatial dependencies.
result Significant improvement in flow prediction accuracy (9% in inflow, 4% in outflow) compared to state-of-the-art methods.
GraphAIR improves graph representation learning by capturing non-linear interactions.
problem Challenges in capturing non-linear interactions in graph data.
method Integrates neighborhood aggregation and interaction modeling.
result Demonstrates improved performance on node classification and link prediction tasks.
New model detects postoperative complications early after surgery.
problem Early detection of postoperative complications in patients.
method Hidden Markov Model sequence classifier analyzing postoperative temperature sequences.
result Improved classification performance compared to other machine learning classifiers.
Adaptive model learns from time series data with changing distributions.
problem Predicting time series data under distribution shift.
method Formulates distribution shift as weighted empirical risk minimization. Uses a gradient-based learning method for a forgetting mechanism.
result Proposes an efficient method for adaptive time series prediction.
Network-assisted regression uses conformal prediction for valid inference.
problem Predicting node attributes using network and conventional covariates with valid statistical inference.
method Network analog of conformal prediction under mild joint exchangeability assumption.
result Achieves finite sample validity and asymptotic conditional validity for various network covariates.
This research predicts cryptocurrency price volatility using deep learning models.
problem Predicting the volatility of cryptocurrency prices to mitigate investment risk.
method Used CNN, LSTM, BiLSTM, and GRU models to predict the risk factor of 20 cryptocurrency parameters.
result Developed a new model with RMSE of 0.0089, significantly outperforming existing models.
Important advances have been made using convolutional neural network (CNN) approaches to solve complicated problems in areas that rely on grid structured data such as image processing and object classification. Recently, research on graph convolutional neural networks (GCNN) has increased dramatically as researchers tr…
Model predicts fine dust concentrations in Seoul using LSTM.
problem Predicting concentrations of fine dust in Seoul due to complex interactions.
method Used LSTM based deep learning model to capture non-linear interactions.
result Successfully predicted fine dust concentrations at 25 districts in Seoul.
Proposes SAHP for better Hawkes process modeling.
problem Predicting occurrence patterns of event sequences.
method Leverages self-attention to modify Hawkes process intensity function.
result Demonstrates effectiveness on real-world datasets.
Paper proposes a new method for predicting DER adoption with hierarchical guarantees.
problem Accurately predicting DER adoption in electric grids with uncertainty and spatial disparity.
method Multivariate Hawkes process for modeling DER adoption dynamics and split conformal prediction algorithm for hierarchical validity.
result Empirical evaluation shows superior predictive accuracy and uncertainty calibration compared to existing methods.
FutureQuant Transformer predicts price ranges and volatility for futures trading.
problem Complex futures trading with real-time LOBs and vast data.
method FutureQuant Transformer model using attention mechanisms.
result Significantly improved trading performance with an average gain of 0.1193%.
Adaptive vehicle trajectory prediction for safer autonomous driving.
problem Inability of current methods to guarantee physical feasibility and adapt to human driving policies.
method Bayesian recurrent neural network combining policy and physical models, with gradient-based training and parameter adaptation.
result The proposed method ensures physical feasibility and adaptability to human driving policies.
Tree ensembles, such as random forests and boosted trees, are renowned for their high prediction performance. However, their interpretability is critically limited due to the enormous complexity. In this study, we present a method to make a complex tree ensemble interpretable by simplifying the model. Specifically, we …
Researchers calculate Shannon entropy rates of hidden Markov processes efficiently.
problem No finite expression exists for Shannon entropy rates of hidden Markov processes.
method Developed an efficient method to calculate entropy rates and identify minimal predictive features.
result Entropy rates can be accurately calculated for hidden Markov processes.
CQNPs enhance predictive performance and distribution modeling using quantile regression.
problem Limited predictive likelihood of Gaussian models for complex distributions.
method Introducing Conditional Quantile Neural Processes (CQNPs) that focus on estimating informative quantiles.
result Significant improvements in predictive performance and better modeling of multimodal distributions.
C-index varies among software, complicating model comparison.
problem Variation in C-index calculations across software.
method Comparison of C-index estimators in R and Python.
result Different implementations yield varying results.
This work introduces a noise-adaptive conformal inference method for better prediction sets in noisy data.
problem Real-world complications like random label noise limit the effectiveness of conformal inference.
method An adaptive conformal inference method capable of handling deviations from exchangeability.
result Informative prediction sets with tight marginal coverage guarantees in noisy data.
Simple model predicts trajectory probabilities.
problem Forecasting trajectories from visual data.
method Spatio-temporal convolutional neural network.
result Achieves results on par with or better than existing methods.
THP model captures complex dependencies in event sequences efficiently.
problem Inability of existing models to capture long-term dependencies in event sequences.
method Transformer Hawkes Process (THP) model using self-attention mechanism.
result THP outperforms existing models in likelihood and event prediction accuracy.
We construct families of manifolds that have pairs of genus g Heegaard splittings that must be stabilized roughly g times to become equivalent. We also show that when two unstabilized, boundary-unstabilized Heegaard splittings are amalgamated by a "sufficiently complicated" map, the resulting splitting is unstabili…
Explains equivariant neural networks for machine learning.
problem Understanding equivariance in neural networks.
method Simple mathematical treatment of neural network concepts.
result Clarifies the mathematical basis of equivariant neural networks.
Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited. In this paper, we propose a post processing method that improves the model interpretability of tree ensembles. After learning a complex tree ensembles in a s…
Bayesian neural networks quantify uncertainties in molecular property predictions.
problem Poor predictions in molecular property predictions due to unreliable training data.
method Bayesian neural networks to estimate model-driven and data-driven uncertainties.
result Uncertainty quantification is necessary for reliable molecular applications.
KEMP predicts long-term trajectories for autonomous driving using keyframes.
problem Predicting future trajectories of road agents for autonomous driving.
method Keyframe-based hierarchical end-to-end deep learning framework.
result Ranked 1st on Waymo Open Motion Dataset Leaderboard.
A model learns object representations for physical scene understanding without direct supervision.
problem Learning object-centric representations without direct supervision of object properties.
method Object-Oriented Prediction and Planning (O2P2) model that learns perception, physics interaction, and rendering functions.
result The model can predict physical interactions and build block towers more complex than those seen during training.
Improved statistical inference for expensive data using machine learning predictions.
problem Statistical inference under adaptive two-phase multiwave sampling with expensive measurements.
method Multiwave Predict-Then-Debias estimator combining proxy information and expensive measurements.
result Valid estimators and confidence intervals for M-estimation under adaptive sampling.
Paper proposes a multi-modal probabilistic prediction model for interactive behavior.
problem Predicting future motions of interacting entities in real-world scenarios.
method Generative model for joint prediction of sequential motions of interacting agents.
result Interpretable model capable of handling prediction uncertainties and multi-modal distributions.
Data science predicts user interest for midwifery content.
problem Improving midwives' learning and preventing maternal and newborn deaths.
method Forecasting methods using user-generated logs from online learning apps.
result Determining future user interest in midwifery content types.
Let M be a 3-manifold with torus boundary components T1 and T2. Let φ:T1→T2 be a homeomorphism, Mφ the manifold obtained from M by gluing T1 to T2 via the map φ, and T the image of T1 in Mφ. We show that if φ is "sufficiently complicated" then any incompressible or strongly …
Neural model predicts object states and physical parameters from visual observations.
problem Computational models struggle with physical reasoning and adapting to new environments.
method Visual prior predicts particle-based system from visual observations; inference module refines estimates subject to dynamics constraints.
result Model can infer physical properties within a few observations and adapt to unseen scenarios.
Solar improves variable selection in high-dimensional data with complicated dependence structures.
problem Variable selection in ultrahigh dimensional data with severe multicollinearity and grouping effect issues.
method Subsample-ordered least angle regression (Solar) for ultrahigh dimensional data.
result Solar yields substantial improvements in sparsity, stability, and accuracy of variable selection compared to traditional methods.
For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using…
STOVE predicts convincing physical behavior in videos.
problem Learning models from videos with objects and interactions.
method Compositional state-space model combining image and dynamics models.
result Predicts videos with convincing physical behavior over hundreds of timesteps.
The theoretical existence of non-classical Schottky groups is due to Marden. Explicit examples of such kind of groups are only known in rank two, the first one by by Yamamoto in 1991 and later by Williams in 2009. In 2006, Maskit and the author provided a theoretical method to obtain examples of non-classical Schottky …