Generates synthetic EHR chief complaints from structured data.
problem Generating unstructured text in EHRs.
method Encoder-decoder deep learning model trained on EHR data.
result Synthetic chief complaints preserve epidemiological information.
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45 results for “Chief Complaints”
Deep learning models outperform traditional methods in automated chief complaint classification for syndromic surveillance.
problem Improving accuracy and speed of automated classification of emergency department records for outbreak detection.
method Implemented two LSTM and GRU models compared to MNB and SVM classifiers trained on 3.6 million de-identified records.
result RNN models outperform bag-of-words classifiers, especially for chief complaints.
Many methods have been proposed for detecting emerging events in text streams using topic modeling. However, these methods have shortcomings that make them unsuitable for rapid detection of locally emerging events on massive text streams. We describe Spatially Compact Semantic Scan (SCSS) that has been developed specif…
UMAP visualizes patient phenotypes from EHR data for emergency triage.
problem Interpreting high-dimensional EHR data for rapid patient triage.
method UMAP for non-linear dimensionality reduction, Gaussian mixture models for clustering.
result UMAP reveals clinically relevant patient phenotypes from EHR data.
Detects systematic anomalies in consumer complaints using NLP.
problem Detecting small, frequent anomalies in consumer complaints.
method NLP conversion of narratives, followed by anomaly detection algorithm.
result Demonstrates effectiveness of NLP for detecting systematic anomalies.
Computational method identifies 30 major complaints in GEICO reviews.
problem Analyzing large numbers of online complaints is challenging.
method Topic modeling approach to reveal latent semantic of complaints.
result 30 major complaints in four categories identified from GEICO reviews.
LDA identifies latent topics in CFPB consumer complaints over time.
problem Identify latent topics in CFPB consumer complaints for better regulation effectiveness.
method Latent Dirichlet Allocation (LDA) for topic modeling of consumer complaints.
result Time trends of latent topics reveal regulatory effectiveness and consumer protection issues.
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, wh…
New model considers unfairness complaints to ensure multiple fairness criteria.
problem Ensuring fairness in systems that may conflict with each other.
method Data-driven model guided by unfairness complaints, supports multiple fairness criteria, and considers their incompatibilities. Stochastic and adversarial settings analyzed with efficient algorithms.
result Efficient algorithms for both stochastic and adversarial settings with competitive guarantees.
TS-CHIEF improves TSC accuracy while drastically reducing runtime.
problem Infeasibility of HIVE-COTE for large datasets.
method Combines time series embeddings into a tree-structured classifier.
result Achieves state-of-the-art accuracy with 130k time series in 2 days.
Language models learn automotive complaints, improving defect detection.
problem Detecting technical quality issues in customer feedback.
method Continual pre-training on ODI Complaints data set.
result Models achieve high precision in identifying technical terms.
Early detection and precise characterization of emerging topics in text streams can be highly useful in applications such as timely and targeted public health interventions and discovering evolving regional business trends. Many methods have been proposed for detecting emerging events in text streams using topic modeli…
We give a concise proof of a classification of lens spaces up to orientation-preserving homeomorphisms. The chief ingredient in our proof is a study of the Alexander polynomial of ` symmetric' links in .
An Economic analogy to Electrodynamicsphysics.gen-ph
In this note, we would like to find the laws of electrodynamics in simple economic systems. In this direction, we identify the chief economic variables and parameters, scalar and vector, which are amenable to be put directly into the crouch of the laws of electrodynamics, namely Maxwell's equations. Moreover, we obtain…
MLJ offers a Julia package for composing machine learning models.
problem Complex model composition in machine learning.
method Flexible model composition tools and meta-algorithms.
result Clear benefits of Julia over multi-language alternatives.
We show that the topological modular functor from Witten-Chern-Simons theory is universal for quantum computation in the sense a quantum circuit computation can be efficiently approximated by an intertwining action of a braid on the functor's state space. A computational model based on Chern-Simons theory at a fifth ro…
The preceding paper constructed tangle machines as diagrammatic models, and illustrated their utility with a number of examples. The information content of a tangle machine is contained in characteristic quantities associated to equivalence classes of tangle machines, which are called invariants. This paper constructs …
Essential surfaces in link diagrams on surfaces are crucial for understanding link properties.
problem Understanding the essentiality of surfaces in link diagrams on surfaces.
method Proving checkerboard surfaces are π1-essential and contain no essential closed curves that are ∂-parallel.
result Checkerboard surfaces of alternating link diagrams are π1-essential and contain no essential closed curves that are ∂-parallel.
Proportional centroid clustering aims to fairly group points without prior protected subsets.
problem Fairly group points without prior protected subsets.
method Define fairness as proportionality, present algorithms for efficient computation and optimization.
result Proportional solutions trade off with the k-means objective.
This article presents a proof of Pogorelov's result that there exists a metric with no local realization in . It also construct in a very elementary way a realization of this metric. Pogorelov's result is somewhat controversial among the community of researchers that study isomet…
Study evaluates deep learning methods for dermatology, finding they perform poorly under non-ideal conditions.
problem Lack of robustness of deep learning methods in dermatology under real-world conditions.
method Simulated non-ideal conditions on user-submitted dermatology images.
result Deep learning methods show significant drop in accuracy and prediction changes under non-ideal conditions.
Topological Quantum Computationquant-ph
The theory of quantum computation can be constructed from the abstract study of anyonic systems. In mathematical terms, these are unitary topological modular functors. They underlie the Jones polynomial and arise in Witten-Chern-Simons theory. The braiding and fusion of anyonic excitations in quantum Hall electron liqu…
Prototype learns automotive industry ontology from unstructured data.
problem Automatic learning of domain-specific ontologies from unstructured text data.
method Two-stage classification system: first classifier for concepts and irrelevant collocates, second classifier for concept types.
result Prototype validated with automotive industry complaint and repair data.
Every year, thousands of people receive consumer product related injuries. Research indicates that online customer reviews can be processed to autonomously identify product safety issues. Early identification of safety issues can lead to earlier recalls, and thus fewer injuries and deaths. A dataset of product reviews …
We argue for the principle of unchanged optimality in RL benchmarks and discuss its implications.
problem Generalization in reinforcement learning benchmarks.
method Discussion of conceptual properties and subtle choices in state representation and model architecture.
result The principle of unchanged optimality is important for RL benchmarks and can be broken or satisfied by model architecture choices.
Study identifies key MRI features for predicting cognitive performance after mTBI.
problem Identify relevant diffusion MRI metrics for cognitive functions in mTBI patients.
method Proposes a novel feature selection method combining best-first search with genetic algorithm crossover.
result Achieves significantly more accurate predictions than other feature selection algorithms.
A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving. While systems that could support these tasks are emerging, the need to scale to distributed, streaming data requires new models and algorith…
The paper proposes a survival model to optimize mobile notification delivery times.
problem Inappropriate notification timing and interruptions lead to user complaints.
method Developed a state transition framework and a survival model with log-linear and Weibull structures.
result The survival model outperforms logistic regression in prediction accuracy.
RIDM combines imitation and RL with a single demo, no action info needed.
problem Learning from a single observed demonstration without action information.
method Reinforced Inverse Dynamics Modeling (RIDM) that operates on raw state features.
result RIDM performs favorably compared to baseline on simulated and real tasks.
The paper tackles confidence calibration for exploratory machine learning problems.
problem Difficulty in curating datasets and confusion about category validity.
method Introduces four new algorithms for category-specific confidence estimation, including kernel density ratios.
result Kernel density ratios provide a novel approach to confidence calibration, especially for exploratory problems.
Meta-Gradient RL adapts return nature online for improved performance.
problem Estimating and optimizing value functions without a true value function oracle.
method Gradient-based meta-learning algorithm that adapts return nature online.
result Achieved new state-of-the-art performance on 57 Atari 2600 games.
Novel algorithm improves poultry welfare by classifying chicken behaviors.
problem Classifying chicken behaviors from sparse and noisy data.
method Time series dictionary learning algorithm.
result Robustly learns from weakly labeled data sources.
We argue that the AdS/CFT calculational prescription for double-trace deformations leads to a holographic derivation of the conformal anomaly, and its conformal primitive, associated to the whole family of conformally covariant powers of the Laplacian (GJMS operators) at the conformal boundary. The bulk side involves a…
The Gaussian kernel is never positive-definite on Riemannian symmetric spaces.
problem Proving the non-positive-definiteness of the Gaussian kernel on non-Euclidean symmetric spaces.
method Developed new geometric and analytical arguments to rigorously characterize the positive-definiteness of the Gaussian kernel.
result L-Godement theorems provide necessary and sufficient conditions for positive-definiteness.
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In our method, we construct on each iteration a Krylov subspace formed by the gradient and an approximation to the Hessian matrix, and then use a …
MethaneMapper detects methane emissions with high accuracy and reduced model size.
problem Challenges in detecting and quantifying methane emissions from AVIRIS-NG data.
method Spectral absorption wavelength aware transformer network, introducing two novel modules.
result Achieves 0.63 mAP in detection and reduces model size by 5x.
Italy's daily COVID-19 cases stratified by age groups.
problem Analyzing the spread and impact of COVID-19 across different age groups.
method Stratified daily data of COVID-19 cases by age classes.
result Detailed insights into how age affects COVID-19 outcomes.
A method to combine saliency metrics for better CNN pruning decisions.
problem Improving CNN pruning decisions by combining multiple saliency metrics.
method Proposes a method to compose different saliency metrics for better CNN pruning decisions.
result The composition of saliencies avoids many poor pruning choices identified by individual saliencies.
Algorithm reduces audit costs by identifying best service configurations from biased textual evidence.
problem Designing service systems from textual evidence requires accurate selection despite biased automated scoring.
method Developed PP-LUCB algorithm combining LLM scores and selective audits to minimize costs.
result Correctly identified the best model in 40/40 trials with 90% cost reduction.
For an -component link , the Milnor's isotopy invariant is defined for each multi-index $I=i_1i_2...i_m (i_j\in\n)$. Here is called the length. Let denote the maximam number of times that any index appears. It is known that Milnor invariants with are link-homotopy invariant. N. Habegger and X. S.…
SketchOGD improves memory efficiency for continual learning.
problem Catastrophic forgetting in continual learning.
method Matrix sketching to compress model gradients.
result SketchOGD outperforms existing memory-efficient OGD variants.
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
problem Coarse SEC item codes mislabel routine and significant events.
method Two-stage system tagging 8-K disclosures against a 119-event taxonomy.
result LLM judge finds precision rises to 96% with quality scores.
Modern applications in sensitive domains such as biometrics and medicine frequently require the use of non-decomposable loss functions such as precision@k, F-measure etc. Compared to point loss functions such as hinge-loss, these offer much more fine grained control over prediction, but at the same time present novel c…
This paper shows how to perform likelihood inference for complex graphical models efficiently.
problem Intractable normalizing constants in fully and partially observed exponential family graphical models.
method Using a technique from Geyer (1991), the paper estimates the normalizing constant and its gradient.
result Full likelihood-based analysis is feasible and computationally efficient for these models.
This work studies fluctuation in multilayer neural networks using mean field theory.
problem Understanding fluctuation in multilayer neural networks with mean field training.
method Developed a second-order mean field limit to capture fluctuation, demonstrating stability of gradient descent training.
result Gradient descent training in multilayer networks biases towards minimal fluctuation, even after convergence.