The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed wit…
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168,982 papers · 148 categories
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48 results for “prescriptive analysis”
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspectivecs.LG
A model learns symptom-drug relations for PD patients.
problem Automatic prescription recommendation for Parkinson's Disease patients.
method Builds a dataset of PD symptoms and prescriptions, learns latent symptom space, uses alternating optimization.
result Effective in recommending suitable prescription drugs for new PD patients.
The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction Systemcs.LG
The study compares Euclidean and cosine distances in medical drug prescription prediction.
problem Comparing Euclidean and cosine distances in medical drug prescription prediction.
method Established geometric properties and compared distances in real-world medical data.
result Different distances lead to different optimizing nonlinear kernel embedding frameworks.
PNNs improve treatment outcomes in TAVR and liver trauma.
problem Improving treatment outcomes in medical procedures.
method Multimodal Prescriptive Neural Networks (PNNs) combining optimization and machine learning.
result PNNs significantly improve estimated outcomes in medical procedures.
The Prescriptive Canvas improves business outcomes by directly prescribing actions based on predictions.
problem Sub-optimal performance in business projects due to a two-step approach of prediction and decision-making.
method The Prescriptive Canvas methodology for framing and communicating actions directly based on predictions.
result Improves framing and communication across stakeholders for successful business impact.
A new neural network model for optimal treatment assignment.
problem Learning optimal treatment assignment from observational data.
method Prescriptive ReLU network (P-ReLU) model that balances performance and interpretability.
result P-ReLU can be converted into an equivalent prescriptive tree with hyperplane splits for interpretability.
Paper integrates predictive and prescriptive tasks using bilevel optimization.
problem Combining prediction and decision-making in machine learning.
method Bilevel optimization for integrating predictive and prescriptive tasks.
result Improved decision-making through direct integration of predictions.
We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier work proposed prescriptive formulations based on supervised machine learning methods. These prescriptive methods can factor in contextual in…
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.
Random forest identifies key features for diagnosing machine faults.
problem Identifying specific factors causing machine faults.
method Gaussian mixture model clustering, spectrum analysis, random forest classification.
result Identified significant features for different machine states.
Deep learning predicts drug prescriptions across global health records.
problem Predicting drug prescriptions in chronic disease patients.
method Adaptive cross-global attention graph kernel network with support vector machine.
result Model outperforms current methods in accuracy and interpretability.
ODTLearn learns optimal decision trees for predictive and prescriptive tasks.
problem Learning optimal decision trees for high-stakes predictive and prescriptive tasks.
method Mixed-integer optimization framework and object-oriented design.
result Implementation of optimal decision trees for various tasks.
Local classification of surfaces and hypersurfaces with radial mean curvature.
problem Classifying surfaces and hypersurfaces with specific curvature properties.
method Local classification and recursive construction method.
result Local classification of hypersurfaces with vanishing second mean curvature.
The paper solves curvature prescription on a disk with negative Gaussian curvature.
problem Prescribing Gaussian curvature and geodesic curvature on a disk with negative Gaussian curvature.
method Variational approach, critical points of a functional, perturbation argument, monotonicity trick, blow-up analysis, Morse index estimates.
result General existence results for the curvature prescription problem.
Constructs surfaces with conical singularities using variational methods.
problem Creating Hamiltonian Stationary Surfaces with specific singularities.
method Variational methods and convergence process similar to Ginzburg-Landau analysis.
result Obtained surfaces with prescribed conical singularities related to optimal Wente constants.
Paper solves curvature prescription problem on surfaces with boundary.
problem Prescribing Gaussian and geodesic curvatures on compact surfaces with boundary.
method Mean field-type formulation and variational techniques.
result Existence results for positive, zero, and negative Euler characteristics.
This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple pr…
P.Lecomte has proposed to take into account the covariant derivatives used to build ordering prescriptions for the naturality of transformation properties and has conjectured that there exists an natural ordering prescription for differential operators of any orders between density bundles which in addition is invarian…
New approach for estimating individual treatment effects in low compliance settings.
problem Estimating individual treatment effects in scenarios with low compliance.
method Proposes a new approach using Structural Causal Model and do-calculus to estimate Individual Prescription Effect (IPE) with asymptotic variance guarantees.
result Consistently improves state-of-the-art in low compliance settings.
Prescribing, by conformal transformation, the kth-elementary symmetric polynomial of the Schouten tensor to be constant is a generalisation of the Yamabe problem. On compact Riemannian n-manifolds we show that, for k between and including 3 and n, this prescription equation is an Euler-Lagrange equation of some act…
On the -Nirenberg problemmath.AP
Study proves curvature prescription on spheres for k ≥ n/2.
problem Prescribing -curvature on standard spheres.
method Existence and compactness theorems proved for k ≥ n/2.
result Extends Chang, Han, Yang's result for n = 4 and k = 2.
In this paper we perform a fine blow-up analysis for a fourth order elliptic equation involving critical Sobolev exponent, related to the prescription of some conformal invariant on the standard sphere. We derive from this analysis some a priori estimates in dimension 5 and 6. On the five dimensionl sphere these a prio…
Method learns optimal treatment policies from observational data.
problem Learning interpretable treatment assignment policies from observational data.
method Mixed-integer optimization (MIO) technology.
result Asymptotically exact in converging to optimal treatment policies.
In this paper we give a new proof of a theorem by Alexandrov on the Gauss curvature prescription of Euclidean convex sets. This proof is based on the duality theory of convex sets and on optimal mass transport. A noteworthy property of this proof is that it does not rely neither on the theory of convex polyhedra nor on…
New approach makes deep reinforcement learning robust without assuming adversary knowledge.
problem Deep reinforcement learning policies are vulnerable to state observation perturbations.
method Proposes an adversary agnostic robust DRL paradigm using policy distillation with two terms: prescription gap maximization and Jacobian regularization.
result Boosts adversarial robustness on five Atari games compared to state-of-the-art methods.
PNNs improve personalized healthcare policies using mixed integer programming.
problem Learning treatment policies for patients with limited data.
method Prescriptive networks (PNNs) trained with mixed integer programming.
result PNNs outperform existing methods in reducing peak blood pressure.
Two problems concerning asymptotically hyperbolic manifolds with an inner boundary are studied. First, we study scalar curvature presciption with either Dirichlet or mean curvature prescription interior boundary condition. Then we apply those results to the Lichnerowicz equation with (future or past) apparent horizon i…
In this paper, we study conformal invariants that arise from nodal sets and negative eigenvalues of conformally covariant operators; more specifically, the GJMS operators, which include the Yamabe and Paneitz operators. We give several applications to curvature prescription problems. We establish a version in conformal…
Study conformal invariants from nodal sets on manifolds with boundary.
problem Understanding conformal invariants from nodal sets and eigenvalues on manifolds with boundary.
method Analysis of conformal covariant operators and eigenvalues on manifolds with boundary.
result Relate Dirichlet and Neumann eigenvalues and apply results to curvature prescription problems.
For any compact manifold of dimension n>=5, we prescribe the volume and any finite part of the spectrum of the Hodge Laplacian acting on diffential forms of degree 1<p<n-1 (exept for p=n/2 if n is even), within a given conformal class. When n<5 and when p=0,1,n-1,n, and p=n/2 if n is even, this simultaneous prescriptio…
Study compares two methods to extend invariants, finding incompatibility for Brieskorn spheres.
problem Comparing two methods to extend invariants for 3-manifolds.
method Two prescriptions: regularized -surgery combined with false-mock modular conjecture, and resurgence-based construction.
result Incompatibility found between the two prescriptions for some Brieskorn spheres.
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. …
The paper introduces a framework for prescriptive process monitoring that generates alarms to prevent or mitigate undesired outcomes.
problem Existing predictive process monitoring techniques do not prescribe when and how to intervene to decrease undesired outcomes.
method The paper proposes a framework that extends predictive monitoring with the ability to generate alarms, incorporating a cost model to assess the trade-off between generating alarms and the cost of undesired outcomes.
result The net cost of undesired outcomes can be minimized by optimizing the generation of alarms based on the progress of the process instance and introducing delays for triggering alarms.
Proves a higher-order positive energy theorem for stationary solutions in fourth-order gravity.
problem Proving a positive energy theorem for fourth-order gravitational theories.
method Analyzes geometric analysis intersections and links to -curvature.
result Establishes a positive energy theorem for stationary solutions in fourth-order gravity, similar to the classical ADM theorem.
We prove that, for a generic set of smooth prescription functions on a closed ambient manifold, there always exists a nontrivial, smooth, closed hypersurface of prescribed mean curvature . The solution is either an embedded minimal hypersurface with integer multiplicity, or a non-minimal almost embedded hypersur…
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendationcs.LG
Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforc…
Solves curvature prescription on rotational surfaces.
problem Prescribing different types of curvatures on rotational surfaces.
method Using arbitrary continuous functions of distance from axis of revolution.
result Complete classification of surfaces with specific curvature relationships.
We extend our studies of a quantum field model defined on a lattice having the dilation group as a local gauge symmetry. The model is relevant in the cross-disciplinary area of econophysics. A corresponding proposal by Ilinski aimed at gauge modeling in non-equilibrium pricing is realized as a numerical simulation of t…
Let be an open Riemann surface and be an integer. We prove that on any closed discrete subset of one can prescribe the values of a conformal minimal immersion . Our result also ensures jet-interpolation of given finite order, and hence, in particular, one may in addition prescribe the…
In this paper it is hown that given any smooth, positive function f on a closed, smooth manifold of dimension greater than four and with positive Paneitz invariant, there exists a metric on M such that = f.
The paper solves CR curvature prescription on pseudo-Einstein 3-manifolds.
problem Prescribing the -curvature on pseudo-Einstein CR 3-manifolds.
method The approach involves studying the problem in both compact and non-compact settings, proving existence of solutions under mild assumptions.
result Existence of one-parameter families of solutions, including normal and non-normal solutions.
The paper tackles robust statistical methods using Wasserstein DRO formulations.
problem Distributional uncertainty in learning from limited samples.
method Min-max distributionally robust optimization with Wasserstein DRO formulations.
result Error bounds free from the curse of dimensionality.
New scaling framework for MoE architectures ensures stability and optimal performance at scale.
problem Lack of principled understanding of how hyperparameters should scale in MoE architectures.
method Developed a novel Dynamical Mean Field Theory (DMFT) for three scaling regimes of MoE architectures.
result Derived Maximally Scale-Stable Parameterization (MSSP) for SGD and Adam, providing robust learning rate transfer and monotonic improvement with scale.
CPR models complex decision processes by breaking them into context-specific policies, improving interpretability and accuracy.
problem Interpreting dynamic human decision-making processes in medical contexts.
method Develops Contextualized Policy Recovery (CPR) framework for multi-task learning, modeling each context-specific policy as a linear map.
result Achieves state-of-the-art performance in predicting medical decisions, closing the gap between interpretable and black-box methods.
Study uses holography to analyze entanglement entropy in deformed CFTs.
problem Analyzing entanglement entropy in $Tar{T}$-deformed CFTs.
method Holographic methods and direct bulk gravitational action evaluation.
result Agreement with known results for entanglement entropy.
Kelly betting is a prescription for optimal resource allocation among a set of gambles which are typically repeated in an independent and identically distributed manner. In this setting, there is a large body of literature which includes arguments that the theory often leads to bets which are "too aggressive" with resp…
Study proves existence of special hypersurfaces in de Sitter space.
problem Existence of compact spacelike hypersurfaces with prescribed curvature.
method Proves existence using prescribed k-curvature in de Sitter space.
result Compact spacelike hypersurfaces with prescribed k-curvature exist in de Sitter space.
We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs). Our approach consists of: (i) predicting future outcomes under each possible therapy using a robustified nonlinear model, and (ii) adopting a randomized prescriptiv…