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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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7 results for “hypotensive”

Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learningstat.ML

SODA-RL learns diverse treatment options for hypotension from data.

problem Identifying the best treatment for acute hypotension from observational data.

method SODA-RL: Safely Optimized, Diverse, and Accurate Reinforcement Learning.

result SODA-RL identifies distinct, plausible treatment options from observational data.

Reinforcement Learning Hypotension Critical Care Electronic Health Records Treatment Options

2020-01-09abs ↗pdf ↗

Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detectioncs.CV

The study predicts acute hypotensive episodes using unsupervised learning and hashing.

problem Early detection of acute hypotensive episodes in ICU.

method Unsupervised representation learning and stratified locality sensitive hashing applied to multivariate time-series data.

result The method accurately predicts upcoming acute hypotensive episodes.

ICU Acute Hypotensive Episodes Representation Learning Locality Sensitive Hashing Time-series

2018-11-14abs ↗pdf ↗

Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretabilitycs.LG

Study predicts blood pressure response to fluid bolus therapy with high accuracy.

problem Predicting successful response to fluid bolus therapy in hypotensive ICU patients.

method Used attention-based LSTM and GRU neural networks on a large ICU database.

result Stacked LSTM with attention mechanism achieved highest accuracy of 0.852.

blood pressure neural networks hypotensive ICU fluid bolus therapy

2018-12-03abs ↗pdf ↗

Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Datacs.LG

A new CA-GAN architecture improves minority class data generation in health datasets.

problem Algorithmic bias due to health data poverty and underrepresentation of minority groups.

method Proposes CA-GAN architecture to address shortcomings of resampling and GAN-based approaches.

result CA-GAN outperforms SMOTE and WGAN-GP* in generating authentic minority class data and maintaining original distribution.

health data poverty algorithmic bias resampling GAN time-series data

2022-10-25abs ↗pdf ↗

Adaptive Semi-Supervised Inference for Optimal Treatment Decisions with Electronic Medical Record Datastat.ME

Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.

problem Estimating optimal treatment regimes from electronic medical records.

method Imputation-based semi-supervised method using unlabeled data.

result Proposed method yields more efficient estimators of optimal treatment regimes.

treatment regime optimal treatment semi-supervised learning imputation electronic medical records

2022-03-04abs ↗pdf ↗

Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectoriescs.LG

Bayesian method estimates dynamics from near-optimal trajectories.

problem Estimating dynamics from near-optimal expert trajectories in reinforcement learning.

method Constraint-based Bayesian approach integrating expert near-optimality.

result Significant improvements in decision-making and transfer success.

Bayesian Inverse Transition Learning Reinforcement Learning Near-Optimal Trajectories Dynamics Estimation Constraint-Based Methods

2024-11-07abs ↗pdf ↗

Forecasting adverse surgical events using self-supervised transfer learning for physiological signalscs.LG

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.

surgical complications physiological signals transfer learning machine learning adverse events

2020-02-12abs ↗pdf ↗