Paper uses GAN to predict opioid relapse from social media data.
problem Accurate relapse prediction for opioid addiction.
method Generative Adversarial Networks (GAN) model trained on sentiment images and social influences.
result GAN model predicts relapse better than alternatives, showing relapse is linked to joy and negative emotions.
Study identifies diverse health states of opioid users to improve policy.
problem Diverse health states of opioid users lead to ineffective policy interventions.
method Probabilistic topic modeling of medical histories.
result Learned phenotypes predict future opioid use and prescription variability.
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.
CASTNet forecasts opioid overdoses using crime patterns.
problem Forecasting opioid overdose occurrences.
method Community-attentive spatio-temporal networks incorporating multi-head attention.
result Superior forecasting performance and interpretable community contributions.
Computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets, like the μ Opioid Receptor and other members of the GPCR family, traverse many non-crystallographic states. We discover new conformational states of μOR…
The paper examines addictive behaviors in RL agents using a modified Snake game.
problem The emergence of addictive behaviors in reinforcement learning agents.
method A modified Snake game was used to model addictive policies in Q-learning agents, and sufficient parametric conditions were derived for the emergence of addictive behaviors.
result The feasibility of addictive wireheading in RL agents was demonstrated, providing venues for further research.
Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.
problem Relapse prevention for alcohol and tobacco addiction users.
method Records user profiles, tracks relapse history, uses machine learning for prediction, and recommends activities.
result Predictive machine learning algorithms help in preventing relapse.
The study identifies patient subgroups with enhanced or diminished opioid treatment effects.
problem Lack of prescribing guidelines for opioids leading to adverse outcomes.
method Generative model using mixture distribution and sparsity to discover subgroups with treatment effects.
result Human-interpretable insights on subgroups with enhanced or diminished treatment effects.
Optimal investment and consumption model with habit formation constraint.
problem Formulating an optimal investment and consumption model with habit formation constraint.
method Formulated an infinite-horizon optimal investment and consumption problem with habit formation model, derived explicit policies, and analyzed the system of differential equations.
result Optimal investment and consumption policies derived explicitly, showing different consumption and investment strategies based on habit formation level.
We solve an optimal consumption problem with habit formation constraints.
problem Maximizing utility with habit formation constraints.
method Formulated and solved a deterministic optimal consumption problem.
result Optimal consumption policies derived explicitly.
This paper studies the continuous time utility maximization problem on consumption with addictive habit formation in incomplete semimartingale markets. Introducing the set of auxiliary state processes and the modified dual space, we embed our original problem into a time-separable utility maximization problem with a sh…
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.
Model predicts drug overdose hotspots using EMS and toxicology data.
problem Predicting drug overdose hotspots to focus limited services.
method Spatial-temporal point process model integrating EMS and toxicology data.
result Model improves prediction accuracy by integrating heterogeneous data.
Study uses machine learning to analyze state drug policies and reduce overdose deaths.
problem Epidemic opioid overdose rates and ineffective state-level policies.
method Hierarchical clustering of 138 binomial variables to generate policy bundles, then regression analysis.
result Balancing certain policies leads to reduced overdose deaths, but only after second year.
Enhanced framework selects features for unbiased causal inference.
problem Unbiased estimation of causal quantities in causal inference.
method Three-stage computational framework balancing treatment and non-treatment variables.
result Significantly reduces bias and variance in estimating causal quantities.
We consider a model of optimal investment and consumption with both habit formation and partial observations in incomplete Itô processes market. The investor chooses his consumption under the addictive habits constraint while only observing the market stock prices but not the instantaneous rate of return. Applying the …
Let f be an ordinary polynomial in C[z1,...,zn] with no negative exponents and with no factor of the form z1α1...znαn where αi are non zero natural integer. If we assume in addicting that f is maximally sparse polynomial (that its support is equal to the set of vertices of its Newton p…
Predicts academic risk in college students using interpretable machine learning.
problem Predicting academic risk from high-dimensional, unbalanced student data.
method Binary classification task using LightGBM model and Shapley value.
result 8 predictors for academic risk identified, including quality of academic partners and dormitory study atmosphere.
Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle, emerging irregularities. Additionally, recent dete…
A new reinforcement learning framework for multi-reward processing.
problem Understanding and modeling multi-reward interactions in complex systems.
method Two-stream reward processing with biological associations.
result Agents can react differently to different types of rewards.
We introduce a machine learning approach for extracting fine-grained representations of protein evolution from molecular dynamics datasets. Metastable switching linear dynamical systems extend standard switching models with a physically-inspired stability constraint. This constraint enables the learning of nuanced repr…
This paper studies the optimal consumption under the addictive habit formation preference in markets with transaction costs and unbounded random endowments. To model the proportional transaction costs, we adopt the Kabanov's multi-asset framework with a cash account. At the terminal time T, the investor can receive unb…
The "standard" Merton formulation of optimal investment and consumption involves optimizing the integrated lifetime utility of consumption, suitably discounted, together with the discounted future bequest. In this formulation the utility of consumption at any given time depends only on the amount consumed at that time.…
We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan enables early detection of emerging patterns in spatio-temporal data, accounting for both the non-iid nature of the data and the fact that detecting subtle p…
The paper proposes a fair reinforcement learning framework to prevent healthcare disparities.
problem Unfair reinforcement learning policies in healthcare can lead to socioeconomically-disadvantaged subgroups being underprivileged.
method The paper introduces a counterfactual fairness framework and a sequential data preprocessing algorithm to achieve fair sequential decision making.
result The proposed approach greatly enhances fair access to counseling in a digital health dataset designed to reduce opioid misuse.
The paper analyzes optimal retirement strategies in a market with habit persistence and jump diffusion, finding discontinuous investment strategies.
problem Optimal retirement decision in a market with habit persistence and jump diffusion.
method Habit reduction method and duality approach to solve the dual problem using a C1 version of Itô's formula. result Discontinuous investment strategies are possible when the so-called ``de facto wealth'' exceeds a critical proportion of wage.
Automates kernel discovery for longitudinal data analysis.
problem Handling irregularly sampled, sparse longitudinal data with multilevel correlation.
method Combines deep neural networks and non-parametric kernel methods to discover complex multilevel correlation structure.
result Significantly outperforms state-of-the-art methods on benchmark data sets.
Bayesian method corrects timing misalignment in recurrent event studies.
problem Estimating differences in event rates under two treatments with timing misalignment.
method g-computation procedure with joint semiparametric Bayesian model.
result Correctly estimates average causal effects under right-censoring.
A novel generative encoder model for imaging and image processing.
problem Efficiently processing and recovering images with noise.
method A pre-training phase with a GAN and an AE, followed by an optimization phase.
result The GE model outperforms state-of-the-art algorithms in image recovery.
ENN method uses expectile regression for genetic data analysis of complex diseases.
problem Discover additional genetic variants contributing to complex diseases.
method Developed an expectile neural network (ENN) method integrating expectile regression and neural networks.
result ENN method outperforms existing expectile regression in discovering genetic variants predisposing to sub-populations.
Paper proposes a machine learning-based method for estimating mediation effects.
problem Challenges in estimating mediation effects with multiple, continuous mediators.
method Developed a one-step estimation algorithm using machine learning and Riesz learning.
result Proposed method can estimate mediation effects from just two statistical estimands.