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.
Detects anomalies in product health metrics at eBay for better alerts.
problem Detecting anomalies in unsupervised product health metrics at eBay.
method Developed a Moving Metric Detector (MMD) for anomaly detection and a point-wise ranking model for alert retrieval.
result Improves alert precision and avoids alert spamming in eBay production.
GANs generate realistic cyber-attack alerts with feature dependencies.
problem Challenges in creating realistic cyber-attack alert data.
method Used Generative Adversarial Networks (GANs) to learn complex data distributions.
result GANs successfully generate realistic alerts with feature dependencies.
System predicts respiratory failure up to 8 hours early.
problem Early detection of respiratory failure in ICU patients.
method Machine learning on ICU patient monitoring data.
result System outperforms traditional clinical decision-making.
Intrusion detection systems (IDSs) generate valuable knowledge about network security, but an abundance of false alarms and a lack of methods to capture the interdependence among alerts hampers their utility for network defense. Here, we explore a graph-based approach for fusing alerts generated by multiple IDSs (e.g.,…
Deep learning, an area of machine learning, is set to revolutionize patient care. But it is not yet part of standard of care, especially when it comes to individual patient care. In fact, it is unclear to what extent data-driven techniques are being used to support clinical decision making (CDS). Heretofore, there has …
DeepClean detects and removes artefacts from ICU waveform data.
problem Accurate removal of artefacts from ICU waveform data reduces bias and uncertainty in clinical assessment.
method Self-supervised deep generative learning using a convolutional variational autoencoder.
result DeepClean detects artefacts with high sensitivity and specificity, significantly outperforming baseline methods.
System prioritizes rough sleeper alerts to improve outreach success.
problem Connecting rough sleepers with essential services efficiently.
method Data-driven approach prioritizing alerts based on likelihood of success.
result Increased outreach success rate by at least 15%.
Study shows SHAP explanations impact alert processing decisions but not performance.
problem Utility of SHAP explanations in alert processing by human experts.
method Human-grounded evaluation with three groups of participants, qualitative analysis of reflections.
result SHAP explanations impact decision-making but not alert processing performance.
To assure cyber security of an enterprise, typically SIEM (Security Information and Event Management) system is in place to normalize security event from different preventive technologies and flag alerts. Analysts in the security operation center (SOC) investigate the alerts to decide if it is truly malicious or not. H…
Integrates CNN and GRU for precise stock market risk alerts.
problem Predicting future stock market risks and providing early warnings.
method Uses CNN for feature extraction and GRU for time series analysis.
result Effective early warnings of future stock market risks.
A CBR approach helps fraud analysts trust machine learning predictions.
problem Understanding the trustworthiness of machine learning predictions for fraud analysts.
method Case-based reasoning (CBR) approach to visualize similar previous instances and their local post-hoc explanations.
result Empirically, the visualization of similar previous instances is useful and easy to use for fraud analysts.
Robust fuzzy clustering for EEG driver alertness with outlier detection.
problem Ambiguous state boundaries in multivariate time series data.
method RFCPCA, a robust fuzzy subspace-clustering method for MTS.
result RFCPCA improves clustering accuracy and characterizes uncertainty and outliers in MTS.
Paper detects social media influencers affecting financial markets.
problem Impact of social media influencers on financial markets.
method Developed an early warning system for detecting suspicious social network activity.
result Discrepancy in meme and non-meme stocks' reactions to social networks.
Improved aggregation methods learn from all ICU events without preprocessing for better patient risk analysis.
problem Lack of efficient methods to dynamically assess patient status in ICU.
method Improved aggregation methods for a deep learning architecture that learns from all events without preprocessing.
result Models achieve strong performance (AUROC 0.87) in patient mortality classification.
A system predicts future malicious behavior of network entities.
problem Prioritizing alert data and understanding attack recurrence.
method Machine learning-based network entity reputation database system.
result It is possible to precisely estimate future attack probabilities.
Eigenrank selects images for deep learning training and predicts segmentation failures.
problem Challenges in automating deep learning for medical image segmentation.
method Eigenrank selects images for training and predicts failures using Von Neumann information.
result U-Net trained on Eigenrank-selected images outperforms one trained on random images.
Detects potential depegs in Curve's StableSwap pools to protect LPs.
problem Detecting and alerting LPs to potential depegs in Curve's StableSwap pools.
method Constructed metrics based on price and trading data, fine-tuned BOCD algorithm.
result Model detects USDC depeg 5 hours before price dip, with few false alarms.
We present RAPID (Real-time Automated Photometric IDentification), a novel time-series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with Gated Recurrent Units (GRUs), we present th…
Simulates patient pathways to detect delayed rare disease diagnoses.
problem Delayed rare disease diagnoses in France, causing health system and patient harm.
method Probabilistic modelling of patient pathways to create an alert system.
result Alert system detects and refers wandering patients to CRMRs.
Bitcoin reacts positively to USDT minting but not burning, showing state-dependence.
problem Understanding Bitcoin's response to Tether's supply changes.
method Analyzing Bitcoin's intraday price movements in response to USDT minting and burning events.
result Bitcoin's response to USDT minting events declines after 60 minutes and is influenced by investor sentiment and public announcements.
Social media services such as Twitter are a valuable source of information for decision support systems. Many studies have shown that this also holds for the medical domain, where Twitter is considered a viable tool for public health officials to sift through relevant information for the early detection, management, an…
Silence on suspicious stock market patterns persists despite lack of plausible explanations.
problem Suspicious patterns in stock market returns not explained or warned about.
method Analysis of correspondence and market data over five years.
result People aware of suspicious patterns chose not to alert the public.
Graph-based methods for anomaly detection and semi-supervised learning.
problem Detecting unusual clinical actions and anomalies in hospital data.
method Label propagation, harmonic solution, regularization, graph connectivity analysis.
result Effective anomaly detection and semi-supervised learning methods for healthcare data.
Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction quality of a model in the absence of labels. However, such tracking is imperative to prevent catastrophic business outcomes resulting from inco…
Paper forecasts extreme Bitcoin volatility spikes using whale transactions and CryptoQuant data.
problem Forecasting extreme volatility spikes in Bitcoin market.
method Proposes Synthesizer Transformer model for forecasting.
result Model outperforms state-of-the-art models in forecasting extreme volatility spikes.
CovidSens uses social media to track COVID-19 spread.
problem Accurate and timely dissemination of COVID-19 information.
method Social sensing to analyze online user data.
result Real-time COVID-19 spread tracking system.
The paper proposes a method to detect relevant model degradations without over-alerting.
problem Detecting meaningful changes in machine learning model performance over time.
method Sequential monitoring scheme accounting for temporal dependence and multiple testing issues.
result The proposed method outperforms benchmark methods in detecting relevant changes in model quality.
The paper predicts diseases using both clinical and genomics data.
problem Clinical predictions using genomics data are not common.
method Integrated clinical and genomics datasets, machine learning, Principal Component Analysis for feature selection.
result 73% accuracy in predicting 75 disease classes.
Improved model predicts ICU readmission and mortality with interpretable results.
problem Lack of clinically interpretable predictions from deep learning models on clinical notes.
method Augmented a convolutional model with an attention mechanism for clinical note prediction.
result Attention mechanism improves prediction performance while providing interpretable results.
A new algorithm uses bandits to diversify database activity monitoring.
problem Limitation of current DAM systems in collecting diverse data.
method Redefined DAM sampling as a bandit problem and developed a novel algorithm combining expert knowledge and random exploration.
result Adding diversity to sampling using the bandit-based approach improves coverage without decreasing alert quality.
Clinical AI models fail to transfer between sites due to site-specific practices.
problem Clinical AI models perform poorly at new sites.
method Identify and isolate site-specific clinical practices affecting data distribution.
result A potential solution to improve model transferability.
System suggests clinical concepts in real-time for faster note creation.
problem Efficiently creating structured clinical notes with minimal keystrokes.
method Contextual autocompletion using shallow neural networks.
result Reduces keystrokes by 67% in real hospital environments.
We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. However, most registration algorithms are designed for high-resolut…
Machine learning aids in clinical prediction tasks.
problem Improving accuracy in clinical predictions.
method Introduction to machine learning concepts and algorithms, followed by practical application to clinical datasets.
result Demonstrated the application of machine learning models to clinical prediction problems.
Novel metrics improve machine learning models for ICU patient care.
problem Predicting vital sign trajectories for early detection of adverse events.
method Developed novel performance metrics aligned with clinical contexts, validated on simulated and real datasets, and optimized neural networks using these metrics.
result Neural networks trained with these metrics excel in predicting clinically significant events.
AI predicts medical specialty diagnostic choices from EHR records.
problem Predicting timely medical specialty diagnostic workups for patients.
method Ensemble of feed-forward neural networks trained on EHR data.
result Significantly higher accuracy compared to traditional checklists.
We propose a general model explanation system (MES) for "explaining" the output of black box classifiers. This paper describes extensions to Turner (2015), which is referred to frequently in the text. We use the motivating example of a classifier trained to detect fraud in a credit card transaction history. The key asp…
Benchmarking recursive collapse claims with a new framework under false-positive control.
problem Evaluating recursive systems for failure patterns and warning claims.
method Developed Loopzero framework for testing recursive failures, specified claim boundaries in Lean, evaluated under FP constraint, and compared with standard detectors.
result No standard detectors or Loopzero's pre-registered quantile detector achieved the required operating point under the false-positive contract.
Machine learning predicts patient recruitment for clinical trials.
problem Improving patient recruitment prediction for clinical trials.
method Machine learning methods applied to historical clinical trial data.
result Reduced prediction error compared to current industry standards.
Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the longand irregular clinical event sequences. We make the observation that clinical events at a…
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic pr…
New dataset and approach improve skin cancer detection accuracy.
problem Lack of patient clinical information in automated skin cancer detection.
method Introduced a new dataset with clinical images and patient information. Combined clinical data with dermoscopy images using deep learning models.
result Combining clinical data improves skin cancer detection accuracy by around 7%.
Survey on DRL for clinical decision support.
problem Improving clinical decision-making through AI.
method Deep reinforcement learning with DNN.
result Survey and comparison of DRL algorithms in clinical applications.
Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagn…
A clinical Meta-Dataset from TCGA for multi-task learning.
problem Clinical decision making requires considering multiple factors; current benchmarks lack consistency and variety.
method Developed a Meta-Dataset with 174 tasks from TCGA, using regression and neural networks.
result Demonstrated the feasibility of predicting multiple clinical variables from gene expression data.
Study develops a new algorithm for assessing clinical trial abstracts.
problem Limited tools for assessing qualitative statements in clinical research.
method Developed a three-class sentiment classification algorithm using BERT model.
result Algorithm achieved 91.3% classification accuracy and 0.92 macro F1-Score.
Improved negation detection in Dutch clinical texts using machine learning.
problem Extracting negation from clinical text for better model development.
method Comparison of rule-based and machine learning methods (biLSTM, RoBERTa).
result BiLSTM and RoBERTa models outperform rule-based method in F1 score, precision, and recall.