Machine learning and C-NLP improve emergency department triage accuracy.
problem Improving accuracy in emergency department triage to manage increased volume.
method Historical EHR data, clinical natural language processing, and ML algorithms (KATE).
result KATE predicts ESI acuity assignments 75.9% of the time, significantly better than nurses.
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.
Deep learning for COVID-19 diagnosis using CXR images with limited data.
problem Difficulty in collecting CXR data for deep learning due to the pandemic.
method Patch-based convolutional neural network with limited trainable parameters.
result Achieves state-of-the-art performance and interpretable saliency maps.
Gradient-based algorithm improves model performance with triage.
problem Improving model accuracy with human expert involvement.
method Formal characterization of triage, optimal triage policy as deterministic threshold rule, gradient-based algorithm.
result Gradient-based algorithm finds triage policies and models with increasing performance.
PCA-Triage optimizes sensor data sampling for IoT networks.
problem Excessive sensor data in IoT networks exceeds available bandwidth.
method PCA-Triage uses streaming incremental PCA loadings to adaptively triage sensor data.
result PCA-Triage achieves high inference performance with minimal bandwidth usage.
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We per…
Framework uses human judgment to distinguish algorithmically indistinguishable cases.
problem Clarifying human-AI collaboration in prediction and decision tasks.
method Integrates human judgment to distinguish algorithmically indistinguishable cases.
result Improves performance of any feasible algorithmic predictor.
SpeakerStew verifies 46 languages with reduced training and inference costs.
problem Speaker verification for 46 languages with smart speaker interactions.
method Pooling multilingual data, triage between text-dependent and text-independent models.
result Training on multiple languages generalizes well and reduces computational requirements.
Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admit ward-bed to receive a patient is delayed until an admission decision is made by a doctor. To reduce the overcrowding and waiting time of E…
Deep learning model predicts severe COVID-19 outcomes.
problem Predicting severe COVID-19 outcomes in ED patients.
method Deep feature fusion model using EHR data and CXR images.
result CO-RISK score achieved AUC of 0.95 and 0.92 for 24 and 72 hours predictions, superior to human performance.
Study assesses CNN model robustness to noise in low-cost CT scans.
problem Evaluate CNN model performance on noisy, artifact-prone low-cost CT images.
method Developed and tested a CNN model for head CT triage, varying tube current and projections.
result Model remains robust to reduced tube current and fewer projections, maintaining AUROC close to original.
Email has remained a principal form of communication among people, both in enterprise and social settings. With a deluge of emails crowding our mailboxes daily, there is a dire need of smart email systems that can recover important emails and make personalized recommendations. In this work, we study the problem of pred…
Bayesian neural networks improve SHD classification and uncertainty quantification.
problem Improving screening for structural heart disease using noninvasive ECG and echocardiography.
method Comparing frequentist and Bayesian neural network classifiers on the EchoNext dataset.
result Bayesian classifiers provide more robust uncertainty quantification.
With an aging and growing population, the number of women requiring either screening or symptomatic mammograms is increasing. To reduce the number of mammograms that need to be read by a radiologist while keeping the diagnostic accuracy the same or better than current clinical practice, we develop Man and Machine Mammo…
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.
The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and sol…
In this work we explore the use of metric index structures, which accelerate nearest neighbor queries, in the scenario where we need to interleave insertions and queries during deployment. This use-case is inspired by a real-life need in malware analysis triage, and is surprisingly understudied. Existing literature ten…
Reconstructs graph structure from noisy data samples.
problem Efficiently discover and model structures in high-dimensional data.
method Combining topological data analysis with numerical modelling.
result Recovery of graph structure from noisy point cloud samples.
We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issue the most appropriate recommendation in terms of best point of care and time to treat. We use an attention-based convolutional neural netwo…
Historically, machine learning in computer security has prioritized defense: think intrusion detection systems, malware classification, and botnet traffic identification. Offense can benefit from data just as well. Social networks, with their access to extensive personal data, bot-friendly APIs, colloquial syntax, and …
We explain increases in clinical risk predictions over time.
problem Tackling the challenge of explaining dynamic risk increases in clinical settings.
method Developed methods to extend static attribution techniques to dynamic settings, addressing challenges specific to time-series data.
result Identified and addressed challenges specific to dynamic risk estimation, improving clinical alert explanations.
Dynamic CBDT improves treatment effect estimation in clinical data.
problem Estimating heterogeneous treatment effects in observational data with high accuracy and interpretability.
method Dynamic Regularized Causal Boosted Decision Trees (CBDT) integrating variance regularization and calibration.
result Significantly improved estimation accuracy and reliable coverage of true treatment effects.
Semi-supervised learning chain boosts histology image accuracy with minimal labeled data.
problem Expensive annotation of large histology image databases.
method Teacher-student knowledge distillation chain approach.
result Accuracy matches 100% labeled data with only 0.5% labeled data.
Proves that emergent algebras right-distributivity implies left-distributivity.
problem Proving the implication between emergent algebra distributivity conditions.
method Analyzing families of quasigroup operations indexed by commutative groups.
result Emergent algebras right-distributive imply left-distributive.
The paper defines strong emergence in field theories and proves it exists between certain theories.
problem Defining and proving the existence of strong emergence phenomena between field theories.
method Formal definition and sufficient conditions for emergence, proving existence in Euclidean background.
result Strong emergence exists between certain parameterized Lagrangian field theories.
Framework detects and ranks suspicious market manipulation using temporal convolutions and expert assessment.
problem Detecting and deterring rogue agents in financial markets.
method Weakly supervised learning, expert assessment, similarity search.
result Promising preliminary results in detecting and ranking suspicious market manipulation.
Adversarial neural network improves cyber attack detection across different networks.
problem Detecting cyber attacks across networks with different traffic distributions.
method Adversarial Siamese neural network that learns invariant attack representations.
result The method retrieves sizable proportions of malicious events, even when trained on one dataset and tested on another.
The paper axiomatizes strong emergence in parameterized field theories and proves existence theorems.
problem Formalizing and proving existence of strong emergence in parameterized field theories.
method Axiomatization and proof of existence theorems for strong emergence between Lagrangian field theories.
result Existence of strong emergence phenomena between parameterized Lagrangian field theories.
Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions to measure evidence conflict and stability.
problem Modern data analysis lacks mechanisms to show the clarity, conflict, or stability of evidence behind predictions.
method Signed Evidence Flow (SEF) combines fitted prediction with signed feature attributions.
result SEF measures conflict and stability, and shows that conflict can improve loss prediction beyond confidence.
Machine learning models detect COVID-19 from routine blood tests.
problem Separating COVID-19 from other viral pneumonias using blood tests.
method Employed random forests and support vector machines on blood data.
result SVM-based classifier achieves 84% accuracy in detecting COVID-19.
An agent-based computational economical toy model for the emergence of money from the initial barter trading, inspired by Menger's postulate that money can spontaneously emerge in a commodity exchange economy, is extensively studied. The model considered, while manageable, is significantly complex, however. It is alrea…
New DL algorithm detects critical chest X-ray findings without manual annotations.
problem Lack of explainability and manual annotation costs for DL models in medical imaging.
method Multi-instance learning approach to jointly classify and localize critical findings in CXR.
result Competitive classification results on three CXR datasets.
We introduce a simplified version of Drinfeld's equations that still solves Kashiwara-Vergne equations.
problem Solving Kashiwara-Vergne equations using Drinfeld's associator equations.
method Introducing a weak version of Drinfeld's associator equations and showing its solutions lead to solutions of the Kashiwara-Vergne equations.
result Solutions to emergent Drinfeld equations still solve the Kashiwara-Vergne equations.
Deep learning models generate languages that lack abstract reasoning.
problem Lack of abstract reasoning in deep learning-generated languages.
method Analyzed emergent language from two multi-agent games with compositional measures.
result Deep learning solutions often fail to generalize to out-of-training examples.
Agents learn to communicate and solve navigation tasks efficiently.
problem Understanding and developing communication in artificial agents.
method Multi-agent navigation tasks in gridworld environments.
result Agents develop an interpretable communication protocol that optimally solves tasks.
Model simulates correlation emergence in two coupled limit order books.
problem Modeling correlation emergence in coupled limit order books.
method Simulated two coupled diffusive limit order books using random walks in the fluid limit, with trader interactions.
result Demonstrated the recovery of an Epps effect from the model.
Paper explores why compositionality is not the right approach for emergent communication.
problem Lack of interaction between biological and computational models of emergent communication.
method Exploring the claim that compositionality is the wrong target for explaining natural language emergence.
result Suggests reflexivity as a better target for explaining natural language emergence.
This thesis explores emergent intelligence in disordered systems like spin glasses and neural networks.
problem Understanding the principles behind emergent intelligent behaviors in disordered systems.
method Statistical physics approach to charting learning mechanisms and dynamics.
result Uncovering relationships between learning mechanisms and physical dynamics.
Research examines how foreign direct investment in Vietnam affects stock returns.
problem Impact of foreign direct investment on stock returns in Vietnam.
method Analyzes data from 1995 to 2015 focusing on M&A deals.
result Foreign direct investment in Vietnam, particularly M&A, influences abnormal stock returns.
Graphs help agents learn emergent communication.
problem Learning robust communication protocols in multi-agent systems.
method Graph convolutional networks to model emergent language and cooperation.
result Agents learn to generalize beyond training samples, revealing true factors of variation.
Emergenet predicts animal influenza strain emergence, outperforming current methods.
problem Limited ability to quantitatively assess animal influenza strain emergence.
method Infer digital twin of sequence evolution using 220,151 HA sequences.
result Emergenet predictions outperform WHO seasonal vaccine recommendations and CDC IRAT scores.
This paper presents a method called One-class Classification using Length statistics of Emerging Patterns Plus (OCLEP+).
Study finds long memory in some emerging Asian stocks but not in developed markets.
problem Evaluating stock market efficiency in emerging vs developed markets.
method Improved wavelet estimator of long range dependence.
result Emerging Asian markets show more long memory in stock returns than developed markets.
AI detects oral pre-cancerous lesions with high accuracy.
problem Manual screening of oral cavity cancer is expensive and lacks specialists.
method Deep convolutional neural networks (DCNNs) using transfer learning.
result DCNN models achieve high accuracy in distinguishing between benign and pre-cancerous tongue lesions.
Automated trading systems on developed and emerging capital markets are studied in this paper. The standard for developed market is automated trading system with 40-days simple moving average. We tested it for the index SIX Industrial for 1000 and 730 trading days of the slovak emerging capital market. The Buy and Hold…
Extracts intrinsic spatial coordinates for complex agent systems to learn PDEs.
problem Modeling collective dynamics of heterogeneous agents.
method Data-driven extraction of intrinsic spatial coordinates, learning PDEs in emergent space.
result Collective dynamics can be approximated through learned PDEs in emergent coordinates.
Framework preserves emergent physics in non-equilibrium systems from particle trajectories.
problem Linking short spatiotemporal scales to emergent bulk physics in multiscale systems.
method Metriplectic bracket formalism for structure-preserving coarse-graining.
result Preservation of thermodynamic laws and conservation in machine-learned dynamics.
Improved reinforcement learning with emergency stops.
problem Reducing exploration in reinforcement learning.
method Emergency stop mechanisms to reduce sample complexity.
result Significant improvement in sample complexity and speed.